AWS unveils frontier AI agents and custom model tools to unlock enterprise AI value at re:Invent

AWS doubles down on custom LLMs with features meant to simplify model creation | TechCrunch

Right on the heels of announcing Nova Forge, a service to train custom Nova AI models, Amazon Web Services (AWS) announced more tools for enterprise customers to create their own frontier models. AWS announced new capabilities in Amazon Bedrock and Amazon SageMaker AI at its AWS re:Invent conference on Wednesday. These new capabilities are designed to make building and fine-tuning custom large language models (LLMs) easier for developers. The cloud provider is introducing serverless model customization in SageMaker, which allows developers to start building a model without needing to think about compute resources or infrastructure, according to Ankur Mehrotra, general manager of AI platforms at AWS, in an interview with TechCrunch. To access these serverless model-building capabilities, developers can either follow a self-guided point-and-click path or an agent-led experience where they can prompt SageMaker using natural language. The agent-led feature is launching in preview. "If you're a healthcare customer and you wanted a model to be able to understand certain medical terminology better, you can simply point SageMaker AI, if you have labeled data, then select the technique and then off SageMaker goes, and [it] fine tunes the model," Mehrotra said. This capability is available for customizing Amazon's own Nova models and certain open source models (those with publicly available model weights), including DeepSeek and Meta's Llama. AWS is also launching Reinforcement Fine-Tuning in Bedrock that allows developers to choose either a reward function or a pre-set workflow, and Bedrock will run a model customization process automatically from start to finish. Frontier LLMs -- meaning the most advanced AI models -- and model customization appear to be an area of focus for AWS at this year's conference. AWS announced Nova Forge, a service where AWS will build custom Nova models for its enterprise customers for $100,000 a year, during AWS CEO Matt Garman's keynote on Tuesday. "A lot of our customers are asking, 'If my competitor has access to the same model, how do I differentiate myself?'" Mehrotra said. "'How do I build unique solutions that are optimized, that optimize my brand, for my data, for my use case, and how do I differentiate myself?' What we've found is that, the key to solving that problem is being able to create customized models." AWS has yet to gain a substantial user base for its AI models. A July survey from Menlo Ventures found that enterprises greatly prefer Anthropic, OpenAI, and Gemini to other models. However, the ability to customize and fine-tune these LLMs could start to give AWS a competitive advantage. Follow along with all of TechCrunch's coverage of the annual enterprise tech event here, and see all the announcements you may have missed thus far here.

AWS announces new capabilities for its AI agent builder | TechCrunch

Amazon Web Services (AWS) is bulking up its AI agent platform, Amazon Bedrock AgentCore, to make building and monitoring AI agents easier for enterprises. AWS announced multiple new AgentCore features on Tuesday during the company's annual AWS re:Invent conference. The company announced new tools for managing AI agent boundaries, agent memory capabilities, and agent evaluation features. One upgrade is the introduction of Policy in AgentCore. This feature allows users to set boundaries for agent interactions using natural language. These boundaries integrate with AgentCore Gateway, which connects AI agents with outside tools, to automatically check each agent's action and stop those that violate written controls. Policy allows developers to set access controls to certain internal data or third-party applications like Salesforce or Slack. These boundaries can also include telling an AI agent they can automatically issue refunds up to $100 but must bring a human in the loop for anything larger, David Richardson, vice president of AgentCore, told TechCrunch. The company also announced AgentCore Evaluations, which is a suite of 13 pre-built evaluation systems for AI agents that monitor factors including correctness, safety, and tool selection accuracy, among others. This also allows developers to have a head start in building their own evaluation features as well. "That one is really going to help address the biggest fears that people have [with] deploying agents," Richardson said about the new evaluation capabilities. "[It's] a thing that a lot of people want to have but is tedious to build." AWS also announced that it's building a memory capability into the agent platform, AgentCore Memory. This feature allows agents to develop a log of information on users over time, like their flight time or hotel preferences, and use that information to inform future decisions. "Across these three things, we are continuing to iterate at the different layers at AgentCore," Richardson said. "Talking to existing systems with Policy, [making agents] more powerful with [AgentCore Memory], helping the development team iterate with an agent." While agents are the soup du jour of the AI industry right now, some people believe the technology won't last. But Richardson thinks that the tools AgentCore is developing can withstand the fast-moving market even as trends change -- which he expects they will. "Being able to take advantage of the reasoning capabilities of these models, which is coupled with being able to do real world things through tools, feels like a sustainable pattern," Richardson said. "The way that pattern works will definitely change. I think we feel ready for that." Follow along with all of TechCrunch's coverage of the annual enterprise tech event here.

Amazon says new DevOps agents need no babysitting - you can try them here

Amazon is entering a crowded market of vendors promising DevOps agents. During its re:Invent conference in Las Vegas on Tuesday, Amazon's AWS introduced three agentic AI technologies that the company says will tie together the tasks surrounding mere code writing, including checking code in and out of code libraries and watching for security breaches. The three agent offerings, dubbed frontier agents, are "a new class of AI agents that are autonomous, scalable, and work for hours or days without intervention," stated AWS in a press release. Also: AI agents see explosive growth on AWS Marketplace - over 40x the team's initial targets The offerings automate tasks in the code repository updates, tasks in DevOps, such as monitoring for application failure, and tasks in cybersecurity, such as analyzing code vulnerabilities. By performing ancillary tasks in and around code generation, the frontier agents are meant to relieve the burden placed on programmers when they have to manually monitor the input and output of AI coding tools. With coding tools, "you can find yourself acting as the human 'thread' that holds work together -- rebuilding context when switching tasks, manually coordinating cross-repository changes, and restitching information scattered across tickets, pull requests, and chat threads," stated AWS. In its announcement, Amazon emphasized that the frontier agents will work behind the scenes while a programmer is busy with other things. Learning from its own internal development teams, AWS said, the company realized it was important that "the team could switch from babysitting every small task to directing agents toward broad, goal-driven outcomes." Also: Enterprises are not prepared for a world of malicious AI agents AWS also emphasized the importance of "how many agentic tasks they could run at the same time" and that "the longer the agents could operate on their own, the better." The frontier agents "figure out" how to achieve a goal, "can perform multiple tasks at the same time," and "can operate for hours or days without requiring intervention," stated AWS. By taking on more tasks without direct oversight, AWS stated, the frontier agents will move "from assisting with individual tasks to completing complex projects autonomously like a member of your team." Amazon did not provide technical details on how the agents operate without oversight or explicit goals. The three frontier agents are: Also: AI aims to predict and fix developer coding errors before disaster strikes Developers can access the KIRO agent right now at its dedicated developer site. The Security Agent and DevOps Agent are both accessed through the AWS management console. With a raft of automations across the development life cycle, AWS is wading into a market crowded with companies that are extending agentic capabilities. Some of these companies have been prominent partners to AWS. The DevOps vendors such as Cisco's Splunk, Datadog, and Dynatrace have for years been making the case that their AI-driven automations will speed not only code writing but also testing, debugging, deployment, and monitoring, in an effort to find vulnerabilities before applications go into production. And code management pure-plays such as GitLab, which competes with Microsoft's GitHub, are rolling out agentic technologies to automatically reconcile code changes. AWS, in fact, has a partnership with GitLab, announced last quarter, to integrate GitLab's agent tools, called Duo Agent, into AWS's generative AI code-writing assistant, Q Developer. Also: Lost in translation? Amazon Q Developer now speaks more languages And cybersecurity firms such as Palo Alto Networks have staked out authentication of access to code as one of the responsibilities of a broad enterprise identity and authentication offering, along with automating security alerts for compromised code.

Amazon is forging a walled garden for enterprise AI

AWS Chief Matt Garman lays out his vision bringing artificial intelligence to the enterprise Re:Invent Amazon wants to make AI meaningful to enterprises, and it's building yet another walled garden disguised as an easy button to do it. During his keynote at Amazon Web Services' annual re:Invent conference, CEO Matt Garman laid out the cloud titan's vision for lowering the barriers to enterprise AI adoption, spanning infrastructure to custom models and pre-baked agents. "When I speak to customers and many of you out there, you haven't yet seen the returns that match up to the promise of AI. The true value of AI has not yet been unlocked," Garman said. Garman's comments broadly align with the results of an MIT study from August which found enterprises had invested between $35 and $40 billion in generative AI initiatives and, so far, have almost nothing to show for it. As damning as that appears, it suggests there's still plenty of hot air left to pump into the AI bubble if Amazon and others can demonstrate the technology's value to enterprises. To do that, AWS has reprised the same strategy it used to popularize cloud computing more than two decades ago: Start with the hardware and build layer upon layer of abstraction that lowers barriers to entry. The further the hardware and the more specialized the service becomes, the tighter AWS's grip becomes. The price of that easy button is lack of portability. The latest example of this is a new AWS platform called Nova Forge, which the cloud giant hopes will make it easier for users to create custom generative AI models. "Today, you just don't have a great way to get a frontier model that deeply understands your data and your domain," Garman said. "What if you could integrate your data at the right time ... during the training of a frontier model, and then create a proprietary model that was just for you? I think this is actually what customers really want." Amazon's approach to that task falls somewhere between training a model from scratch, a job that needs more data and compute power than most enterprises possess, and post-training fine-tuning of open-weights models. "It's really hard to teach a model a completely new domain that it wasn't already pre-trained on," Garman said. "It's a little bit like humans trying to learn a new language. When you start, when you're really young, it's relatively easy to pick up, but when you try to learn a new language later in life, it's actually much, much harder," he said. Rather than fine-tuning a finished model, Forge provides access to a partially trained checkpoint for its Nova models, which customers can then train to completion using a combination of their own proprietary data and AWS-curated datasets. According to Garman: "This introduces your domain-specific knowledge, all without losing the important foundational capabilities of the model, like reasoning." The result is a proprietary model, which Amazon calls "Novellas", deployed in the AWS Bedrock AI-as-a-service platform. Bedrock runs atop a range of hardware including both Nvidia GPUs and AWS's own homegrown accelerators, eliminating the need to manage hardware or the low-level software stacks necessary to get the most out of them. But while these custom models may be exclusive to you, you can't take them with you beyond the bounds of AWS. The same is true of Amazon's new Nova LLMs. On stage, Garman revealed Nova 2, a family of proprietary LLMs and conversational AI models available in four distinct flavors: Nova 2 Lite, Pro, Sonic, and Omni. Lite and Pro are reasoning models which Garman boasted are competitive with closed-weight models from OpenAI and Anthropic. Sonic is a speech-to-speech model designed for conversational AI, while Omni supports multi-modal inputs, allowing it to both ingest and output images and text. Again, these models are only available on Bedrock. Of course, Amazon will tell you Bedrock also supports a wide variety of open-weights models, including Mistral AI's newly announced Mistral Large and Mistral 3 family of LLMs. However, these can't be used with Forge. In this respect, AWS's Forge and Nova models help Amazon to address the stickiness problem associated with API services, which can be easily swapped out for another cheaper or more performant one any time the customer pleases. While helpful for Amazon, they make it harder for enterprises to walk away from their investments. Amazon doesn't just want to sell you custom models. It's also developing tools to simplify the development of AI agents that can perform complex multi-step tasks, often without supervision. During the keynote Garman unveiled two new additions to AWS's Bedrock Agent Core platform in the hopes of convincing its customers these AI agents can actually be trusted. The first is a new policy extension which allows customers not only to dictate what tools and data the agent is allowed to use, but also how it uses them. For example, a customer service agent may have a policy preventing it from authorizing returns on items valued at more than $1,000, forcing a manual review by a human operator. "Now that you have these clear policies in place, organizations can much more deeply trust the agents that they're building and deploying, knowing that they'll stay within the boundaries that you've defined," Garman said. The second is a new evaluation suite aimed at ensuring agents behave as expected in the real world. "You only know how your agents are going to react and respond when you have them out there in the real world. That means you have to continuously monitor and evaluate your agent behavior in real time and then quickly react if you see them doing something that you don't like." This, he explains, can help to avoid situations upgrading the base model inadvertently degrades the application performance. In addition to building custom agents, Garman also touted a growing number of pre-baked agents available in the company's cloud marketplace, including several of its own aimed at automating development and cybersecurity. At least when it comes to agents, Amazon isn't trying to be everything to everyone. Agents need to connect to a variety of tools, services, and models - only some of which Amazon offers. "You only have to use the building blocks that you need. We don't force you as builders to go down a single, fixed path. We allow you to pick and choose which services you want to make for your own situation," Garman said. But while Amazon may not force you to use all of its services, it does offer a way to build shake-n-bake AI agents or assistants, which aren't nearly so easily migrated from one cloud to another. ®

AWS unveils Frontier AI agents for software development

AWS Frontier agents work independently on specialized tasks, with the first three agents focused on autonomous coding, application security, and devops. Amazon Web Services has unveiled a new class of AI agents, called frontier agents, which the company said can work for hours or days without intervention. The first three agents are focused on software development tasks. The three agents announced December 2 include the Kiro autonomous agent, AWS Security Agent, and AWS Devops Agent, each focused on a different aspect of the software development life cycle. AWS said these agents represent a step-function change in what can be done with agents, moving from assisting with individual tasks to completing complex projects autonomously like a member of the user's team. The Kiro autonomous agent is a virtual developer that maintains context and learns over time while working independently, so users can focus on their biggest priorities. The AWS Security Agent serves as virtual security engineer that helps build secure applications by being a security consultant for app design, code reviews, and penetration testing. And the AWS DevOps Agent is a virtual operations team member that helps resolve and proactively prevent incidents while continuously improving an applications' reliability and performance, AWS said. All three agents are available in preview. The Kiro agent is a shared resource working alongside the entire team, building a collective understanding of the user's codebase, products, and standards. It connects to a team's repos, pipelines, and tools such as Jira and GitHub to maintain context as work progresses. Kiro previously was positioned as an agentic AI-driven IDE. The AWS Security Agent, meanwhile, helps build applications that are secure from the start across AWS, multi-cloud, and hybrid environments. AWS Devops Agent is on call when incidents happen, instantly responding to issues and usings its knowledge of an application and relationship between components to find the root cause of a problem with an application going down, according to AWS.

AWS joins Microsoft, Google in the security AI agent race

Preview tool promises quicker reviews and faster flaw-finding for cloud apps Re:Invent AI agents are key to launching applications more quickly - and making them more secure from the start, Amazon says. To that end, the cloud giant has rolled out AWS Security Agent in preview today at its annual re:Invent conference. It's free to use - with usage limits - during the public preview period, but there's no word yet from Amazon as to when it will be generally available. But unlike Google and Microsoft, AWS's approach to agentic AI for security-specific use cases seems a little more subdued with one agent - as opposed to tasking agents with all the security things. "AWS Security Agent is a single frontier agent that proactively secures your applications throughout the development lifecycle across all your environments," AWS Director of Applied Science Neha Rungta told The Register. Security teams define corporate requirements and standards, then the agent conducts automated reviews to ensure these are being met. It also does on-demand penetration testing customized to organizations' applications and reports any security risks. "The penetration testing agent creates a customized attack plan informed by the context it has learned from your security requirements, design documents, and source code, and dynamically adapts as it runs based on what it discovers, such as endpoints, status and error codes, and credentials," said Esra Kayabali, AWS senior solutions architect, in a blog shared with The Register ahead of publication. This task alone can shave weeks or even months off applications' security validation processes, according to Rungta. "Customers have told us that AWS Security Agent's on-demand penetration testing allows them to begin receiving results within hours compared to what would have taken weeks of scheduling and back-and-forth communication between teams," Rungta said. "Others have told us that AWS Security Agent's design time findings helped them save significant development time and effort," she added. "Fixing design time issues before any code is written is painless, whereas it would have been extraordinarily painful had it been flagged by the application security team three months later." AWS says that its Security Agent is more effective than static application security testing and dynamic application security testing tools because the agent is context-aware, meaning it understands the application's code and design, where it will run, and any company-specific security requirements. Users can upload artifacts to provide context about their application being tested, Rungta explained. Plus, customers can give the agent access to their GitHub repositories for additional context in penetration testing, "to post comments on pull requests, and to submit pull requests with remediations for penetration test findings," she added. Humans review these penetration test findings, along with all the design and code review, and can configure the security agent to either automatically submit pull requests with remediations based on these findings, or manually trigger pull requests after review. While Amazon already reportedly uses AI agents to proactively find security flaws and suggest fixes internally, it hasn't been as quick to roll out security-focused agents to customers as its cloud competitors. Microsoft is arguably furthest along in this process of task-specific agents and AI-infused security products with Redmond introducing 11 Security Copilot agents at a press event in March. In August, it touted an autonomous AI agent prototype, called Project Ire, that Microsoft claims can detect malware without human assistance. But in a real-world test of 4,000 "hard-target" files (these files weren't classified by automated systems and would otherwise be manually reviewed by human reverse engineers), the agent only detected 26 percent of all the malware. Meanwhile, Google is also developing its own security-minded AI agents including one that can triage security alerts by analyzing the context of each incident and give the humans in charge advice about which ones merit a response. Another one analyzes malware and determines the extent of the threat it poses. Last month, the Chocolate Factory said yet another AI agent-powered security tool called CodeMender, which automates patch creation, can identify the root cause of a vulnerability, then generate and review a working patch - but it still needs a human to sign off on the fix. ®

Amazon unveils 'frontier agents,' new chips and private 'AI factories' in AWS re:Invent rollout

LAS VEGAS -- Amazon is pitching a future where AI works while humans sleep, announcing a collection of what it calls "frontier agents" capable of handling complex, multi-day projects without needing a human to be constantly involved. The announcement Tuesday at the Amazon Web Services re:Invent conference is an attempt by the cloud giant to leapfrog Microsoft, Google, Salesforce, OpenAI, and others as the industry moves beyond interactive AI assistants toward fully autonomous digital workers. The rollout features three specialized agents: A virtual developer for Amazon's Kiro coding platform that navigates multiple code repositories to fix bugs; a security agent that actively tests applications for vulnerabilities; and a DevOps agent that responds to system outages. Unlike standard AI chatbots that reset after each session, Amazon says the frontier agents have long-term memory and can work for hours or days to solve ambiguous problems. "You could go to sleep and wake up in the morning, and it's completed a bunch of tasks," said Deepak Singh, AWS vice president of developer agents and experiences, in an interview. Amazon is starting with the agents focused on software development, but Singh made it clear that it's just the beginning of a larger long-term rollout of similar agents. "The term is broad," he said. "It can be applied in many, many domains." During the opening keynote Tuesday morning, AWS CEO Matt Garman said believes AI agents represent an "inflection point" in AI development, transforming AI from a "technical wonder" into something that delivers real business value. In the future, Garman said, "there's going to be millions of agents inside of every company across every imaginable field." To keep frontier agents from breaking critical systems, Amazon says humans remain the gatekeepers. The DevOps agent stops short of making fixes automatically, instead generating a detailed "mitigation plan" that an engineer approves. The Kiro developer agent submits its work as proposed pull requests, ensuring a human reviews the code before it's merged. Microsoft, Google, OpenAI, Anthropic and others are all moving in a similar direction. Microsoft's GitHub Copilot is becoming a multi-agent system, Google is adding autonomous features to Gemini, and Anthropic's Claude Code is designed to handle extended coding tasks. Amazon is announcing the frontier agents during the opening keynote by AWS CEO Matt Garman at re:Invent, its big annual conference. The DevOps and security agents are available in public preview starting Tuesday; the Kiro developer agent will roll out in the coming months. Some of the other notable announcements at re:Invent today: AI Factories: AWS will ship racks of its servers directly to customer data centers to run as a private "AI Factory," in its words. This matters for governments and banks, for example, that want modern AI tools but are legally restricted from moving sensitive data off-premises. New AI Models: Amazon announced Nova 2, the next generation of the generative AI models it first unveiled here a year ago. They include a "Pro" model for complex reasoning, a "Sonic" model for natural voice conversations, and a new "Omni" model that processes text, audio, and video simultaneously. Custom Models: Amazon introduced Nova Forge, a tool that lets companies build their own high-end AI models from scratch by combining their private data with Amazon's own datasets. It's designed for businesses that find standard models too generic but lack the resources to build one entirely alone. Trainium: Amazon released its newest home-grown AI processor, Trainium 3, which it says is roughly 4x faster and 40% more efficient than the previous version. It's central to Amazon's strategy to lower the cost of training AI and provide a cheaper alternative to Nvidia GPUs. Executives also previewed Trainium 4, promising to double energy efficiency again. Killing "Tech Debt": AWS expanded its Transform service to rewrite and modernize code from basically any source, including proprietary languages. The tool uses AI agents to analyze and convert these custom legacy systems into modern languages, a process Amazon claims is up to five times faster than manual coding. Stay tuned to GeekWire for more coverage from the event this week.

Amazon's new AI can code for days without human help. What does that mean for software engineers?

Amazon Web Services on Tuesday announced a new class of artificial intelligence systems called "frontier agents" that can work autonomously for hours or even days without human intervention, representing one of the most ambitious attempts yet to automate the full software development lifecycle. The announcement, made during AWS CEO Matt Garman's keynote address at the company's annual re:Invent conference, introduces three specialized AI agents designed to act as virtual team members: Kiro autonomous agent for software development, AWS Security Agent for application security, and AWS DevOps Agent for IT operations. The move signals Amazon's intent to leap ahead in the intensifying competition to build AI systems capable of performing complex, multi-step tasks that currently require teams of skilled engineers. "We see frontier agents as a completely new class of agents," said Deepak Singh, vice president of developer agents and experiences at Amazon, in an interview ahead of the announcement. "They're fundamentally designed to work for hours and days. You're not giving them a problem that you want finished in the next five minutes. You're giving them complex challenges that they may have to think about, try different solutions, and get to the right conclusion -- and they should do that without intervention." Why Amazon believes its new agents leave existing AI coding tools behind The frontier agents differ from existing AI coding assistants like GitHub Copilot or Amazon's own CodeWhisperer in several fundamental ways. Current AI coding tools, while powerful, require engineers to drive every interaction. Developers must write prompts, provide context, and manually coordinate work across different code repositories. When switching between tasks, the AI loses context and must start fresh. The new frontier agents, by contrast, maintain persistent memory across sessions and continuously learn from an organization's codebase, documentation, and team communications. They can independently determine which code repositories require changes, work on multiple files simultaneously, and coordinate complex transformations spanning dozens of microservices. "With a current agent, you would go microservice by microservice, making changes one at a time, and each change would be a different session with no shared context," Singh explained. "With a frontier agent, you say, 'I need to solve this broad problem.' You point it to the right application, and it decides which repos need changes." The agents exhibit three defining characteristics that AWS believes set them apart: autonomy in decision-making, the ability to scale by spawning multiple agents to work on different aspects of a problem simultaneously, and the capacity to operate independently for extended periods. "A frontier agent can decide to spin up 10 versions of itself, all working on different parts of the problem at once," Singh said. How each of the three frontier agents tackles a different phase of development Kiro autonomous agent serves as a virtual developer that maintains context across coding sessions and learns from an organization's pull requests, code reviews, and technical discussions. Teams can connect it to GitHub, Jira, Slack, and internal documentation systems. The agent then acts like a teammate, accepting task assignments and working independently until it either completes the work or requires human guidance. AWS Security Agent embeds security expertise throughout the development process, automatically reviewing design documents and scanning pull requests against organizational security requirements. Perhaps most significantly, it transforms penetration testing from a weeks-long manual process into an on-demand capability that completes in hours. SmugMug, a photo hosting platform, has already deployed the security agent. "AWS Security Agent helped catch a business logic bug that no existing tools would have caught, exposing information improperly," said Andres Ruiz, staff software engineer at the company. "To any other tool, this would have been invisible. But the ability for Security Agent to contextualize the information, parse the API response, and find the unexpected information there represents a leap forward in automated security testing." AWS DevOps Agent functions as an always-on operations team member, responding instantly to incidents and using its accumulated knowledge to identify root causes. It connects to observability tools including Amazon CloudWatch, Datadog, Dynatrace, New Relic, and Splunk, along with runbooks and deployment pipelines. Commonwealth Bank of Australia tested the DevOps agent by replicating a complex network and identity management issue that typically requires hours for experienced engineers to diagnose. The agent identified the root cause in under 15 minutes. "AWS DevOps Agent thinks and acts like a seasoned DevOps engineer, helping our engineers build a banking infrastructure that's faster, more resilient, and designed to deliver better experiences for our customers," said Jason Sandry, head of cloud services at Commonwealth Bank. Amazon makes its case against Google and Microsoft in the AI coding wars The announcement arrives amid a fierce battle among technology giants to dominate the emerging market for AI-powered development tools. Google has made significant noise in recent weeks with its own AI coding capabilities, while Microsoft continues to advance GitHub Copilot and its broader AI development toolkit. Singh argued that AWS holds distinct advantages rooted in the company's 20-year history operating cloud infrastructure and Amazon's own massive software engineering organization. "AWS has been the cloud of choice for 20 years, so we have two decades of knowledge building and running it, and working with customers who've been building and running applications on it," Singh said. "The learnings from operating AWS, the knowledge our customers have, the experience we've built using these tools ourselves every day to build real-world applications -- all of that is embodied in these frontier agents." He drew a distinction between tools suitable for prototypes versus production systems. "There's a lot of things out there that you can use to build your prototype or your toy application. But if you want to build production applications, there's a lot of knowledge that we bring in as AWS that apply here." The safeguards Amazon built to keep autonomous agents from going rogue The prospect of AI systems operating autonomously for days raises immediate questions about what happens when they go off track. Singh described multiple safeguards built into the system. All learnings accumulated by the agents are logged and visible, allowing engineers to understand what knowledge influences the agent's decisions. Teams can even remove specific learnings if they discover the agent has absorbed incorrect information from team communications. "You can go in and even redact that from its knowledge like, 'No, we don't want you to ever use this knowledge,'" Singh said. "You can look at the knowledge like it's almost -- it's like looking at your neurons inside your brain. You can disconnect some." Engineers can also monitor agent activity in real-time and intervene when necessary, either redirecting the agent or taking over entirely. Most critically, the agents never commit code directly to production systems. That responsibility remains with human engineers. "These agents are never going to check the code into production. That is still the human's responsibility," Singh emphasized. "You are still, as an engineer, responsible for the code you're checking in, whether it's generated by you or by an agent working autonomously." What frontier agents mean for the future of software engineering jobs The announcement inevitably raises concerns about the impact on software engineering jobs. Singh pushed back against the notion that frontier agents will replace developers, framing them instead as tools that amplify human capabilities. "Software engineering is craft. What's changing is not, 'Hey, agents are doing all the work.' The craft of software engineering is changing -- how you use agents, how do you set up your code base, how do you set up your prompts, how do you set up your rules, how do you set up your knowledge bases so that agents can be effective," he said. Singh noted that senior engineers who had drifted away from hands-on coding are now writing more code than ever. "It's actually easier for them to become software engineers," he said. He pointed to an internal example where a team completed a project in 78 days that would have taken 18 months using traditional practices. "Because they were able to use AI. And the thing that made it work was not just the fact that they were using AI, but how they organized and set up their practices of how they built that software were maximized around that." How Amazon plans to make AI-generated code more trustworthy over time Singh outlined several areas where frontier agents will evolve over the coming years. Multi-agent architectures, where systems of specialized agents coordinate to solve complex problems, represent a major frontier. So does the integration of formal verification techniques to increase confidence in AI-generated code. AWS recently introduced property-based testing in Kiro, which uses automated reasoning to extract testable properties from specifications and generate thousands of test scenarios automatically. "If you have a shopping cart application, every way an order can be canceled, and how it might be canceled, and the way refunds are handled in Germany versus the US -- if you're writing a unit test, maybe two, Germany and US, but now, because you have this property-based testing approach, your agent can create a scenario for every country you operate in and test all of them automatically for you," Singh explained. Building trust in autonomous systems remains the central challenge. "Right now you still require tons of human guardrails at every step to make sure that the right thing happens. And as we get better at these techniques, you will use less and less, and you'll be able to trust the agents a lot more," he said. Amazon's bigger bet on autonomous AI stretches far beyond writing code The frontier agents announcement arrived alongside a cascade of other news at re:Invent 2025. AWS kicked off the conference with major announcements on agentic AI capabilities, customer service innovations, and multicloud networking. Amazon expanded its Nova portfolio with four new models delivering industry-leading price-performance across reasoning, multimodal processing, conversational AI, code generation, and agentic tasks. Nova Forge pioneers "open training," giving organizations access to pre-trained model checkpoints and the ability to blend proprietary data with Amazon Nova-curated datasets. AWS also added 18 new open weight models to Amazon Bedrock, reinforcing its commitment to offering a broad selection of fully managed models from leading AI providers. The launch includes new models from Mistral AI, Google's Gemma 3, MiniMax's M2, NVIDIA's Nemotron, and OpenAI's GPT OSS Safeguard. On the infrastructure side, Amazon EC2 Trn3 UltraServers, powered by AWS's first 3nm AI chip, pack up to 144 Trainium3 chips into a single integrated system, delivering up to 4.4x more compute performance and 4x greater energy efficiency than the previous generation. AWS AI Factories provides enterprises and government organizations with dedicated AWS AI infrastructure deployed in their own data centers, combining NVIDIA GPUs, Trainium chips, AWS networking, and AI services like Amazon Bedrock and SageMaker AI. All three frontier agents launched in preview on Tuesday. Pricing will be announced when the services reach general availability. Singh made clear the company sees applications far beyond coding. "These are the first frontier agents we are releasing, and they're in the software development lifecycle," he said. "The problems and use cases for frontier agents -- these agents that are long running, capable of autonomy, thinking, always learning and improving -- can be applied to many, many domains." Amazon, after all, operates satellite networks, runs robotics warehouses, and manages one of the world's largest e-commerce platforms. If autonomous agents can learn to write code on their own, the company is betting they can eventually learn to do just about anything else.

AWS goes beyond prompt-level safety with automated reasoning in AgentCore

AWS is leveraging automated reasoning, which uses math-based verification, to build out new capabilities in its Amazon Bedrock AgentCore platform as the company digs deeper into the agentic AI ecosystem. Announced during its annual re: Invent conference in Las Vegas, AWS is adding three new capabilities to AgentCore: "policy," "evaluations" and "episodic memory." The new features aim to give enterprises more control over agent behavior and performance. AWS also revealed what it calls "a new class of agents," or "frontier agents," that are autonomous, scalable and independent. Swami Sivasubramanian, AWS VP for Agentic AI, told VentureBeat that many of AWS's new features represent a shift in who becomes a builder. "We are actually on the cusp of a major tectonic transformation with AI, but agentic AI is truly starting to transform what is the art of the possible, and it is going to make this one of the most truly transforming technologies," Sivasubramanian said. Policy agents The new policy capability helps enterprises reinforce guidelines even after the agent has already reasoned its response. AWS VP for AgentCore David Richardson told VentureBeat that the policy tool sits between the agent and the tools it calls, rather than being baked into the agent, as fine-tuning often is. The idea is to prevent an agent from violating enterprise rules and redirect it to re-evaluate its reasoning. Richardson gave the example of a customer service agent: A company would write a policy stating that the agent can grant a refund of up to $100, but for anything higher, the agent would need to bounce the customer to a human. He noted that it remains easy to subvert an agent's reasoning loop through, for instance, prompt injection or poisoned data, leading agents to ignore guardrails. "There are always these prompt injection attacks where people try to subvert the reasoning of the agent to get the agent to do things it shouldn't do," Richardson said. "That's why we implemented the policy outside of the agent, and it works using the automated reasoning capabilities that we've spent years building up to help customer define their capabilities." AWS unveiled Automated Reasoning Checks on Bedrock at last year's re: Invent. These use neurosymbolic AI, or math-based validation, to prove correctness. The tool applies mathematical proofs to models to confirm that it hasn't hallucinated. AWS has been leaning heavily into neurosymbolic AI and automated reasoning, pushing for enterprise-grade security and safety in ways that differ from other AI model providers. Episodic memories and evaluations The two other new updates to AgentCore, "evaluations" and "episodic memory," also give enterprises a better view of agent performance and give agents episodic memory. An enhancement of AgentCore memory, episodic memory refers to knowledge that agents tap into only occasionally, unlike longer-running preferences, which they have to refer back to constantly. Context window limits hamper some agents, so they sometimes forget information or conversations they haven't tapped into for a while. "The idea is to help capture information that a user really would wish the agent remembered when they came back," said Richardson. "For example, 'what is their preferred seat on an airplane for family trips?' Or 'what is the sort of price range they're looking for?'" Episodic memory differs from the previously shipped AgentCore memory because, instead of relying on maintaining short- and long-term memory, agents built on AgentCore can recall certain information based on triggers. This can eliminate the need for custom instructions. With AgentCore evaluations, organizations can use 13 pre-built evaluators or write their own. Developers can set alerts to warn them if agents begin to fail quality monitoring. Frontier agents But perhaps AWS's strongest push into enterprise agentic AI is the release of frontier agents, or fully automated and independent agents that the company says can act as teammates with little direction. The concept is similar, if not identical, to those of more asynchronous agents from competitors like Google and OpenAI. However, AWS seems to be releasing more than just autonomous coding agents. Sivasubramanian called them a "new class" of agents, "not only a step function change in what you can do today; they move from assisting with individual tasks to complex projects." The first is Kiro, an autonomous coding agent that has been in public preview since July. At the time, Kiro was billed as an alternative to vibe coding platforms like OpenAI's Codex or Windsurf. Similar to Codex and Google's myriad asynchronous coding agents, including Jules, Kiro can code, undertake reviews, fix bugs independently and determine the tasks it needs to accomplish. AWS security agent, meanwhile, embeds deep security expertise into applications from the start. The company said in a press release that users "define security standards once and AWS security agent automatically validates them across your applications during its review -- helping teams address the risks that matter to their business, not generic checklists." The AWS DevOps agent will help developers, especially those on call, proactively find system breaks or bugs. It can respond to incidents using its knowledge of the application or service. It also acknowledges the relationships between the application and the tools it taps, such as Amazon CloudWatch, Datadog and Splunk, to trace the root cause of the issue. Enterprises are interested in deploying agents and, eventually, bringing more autonomous agents into their workflows. And, while companies like AWS continue to bolster these agents with security and control, organizations are slowly figuring out how to connect them all.

Worker-bee AGI: Why AWS is betting on practical agents, not 'messiah AGI' - SiliconANGLE

Worker-bee AGI: Why AWS is betting on practical agents, not 'messiah AGI' At AWS re:Invent 2025, Amazon Web Services Inc. faced a dual mandate: Speak to millions of longstanding cloud customers while countering a persistent narrative that the company is lagging in artificial intelligence. In our view, AWS chose a distinctly pragmatic path. Rather than chasing the holy grail of what we call "messiah AGI," or artificial general intelligence or even competing head-on with frontier-scale large language model, the company emphasized foundational agentic scaffolding and customizable large and small language models. This approach aligns with our thesis that the real near-term value in AI lies inside the enterprise - what we see as "worker-bee AGI" - not in aspirational, generalized intelligence. Skeptics argue that this worker-bee AGI is little more than RPA 2.0, burdened by familiar data silos. Though that critique rings somewhat true, we not view AWS' strategy as merely paving the robotic process automation cow path. Rather, we see the company making a deliberate shift from siloed agentic automation toward what we call "service-as-software" - a new model in which business outcomes, not individual applications, become the primary point of control. In this special edition of Breaking Analysis, we break down AWS re:Invent 2025 through the lens of this emerging paradigm. We'll examine how AWS is repositioning its infrastructure, services and AI roadmap to support a transformation in the operational, business and technology models of virtually every organization and industry. At re:Invent 2025, one theme kept surfacing in our conversations in that we're watching two fundamentally different AI models collide. On one side, we see what we call the messiah AGI quest - led by Open AI Labs PBC Chief Executive Sam Altman and the frontier labs - pushing ever deeper into the earth in search of more intelligence. On the other side sits the enterprise, represented metaphorically by JPMorgan Chase CEO Jamie Dimon and powered by renewable, sustainable data assets that grow stronger with every additional agent deployed. OpenAI and ChatGPT defined the opening chapter of generative AI by tapping a reservoir of free, high-quality internet data. Through GPT-4 and likely GPT-5, the marginal cost of data was essentially zero. Compute was expensive, but the fuel for the models was essentially free. That era is ending, in our view. As freely available high-quality data is depleted, the labs are turning to human expertise - curated reasoning traces from domain specialists - to maintain their performance advantage. We see this as a dramatically different business model. Credible reporting suggests that by 2029 to 2030, OpenAI could be spending 20% to 25% of its revenue just to acquire proprietary expert data. In our view, this is a classic diminishing-returns scenario where you achieve rising marginal costs for increasingly niche improvements. It's the image of Altman digging deeper into the ground for shrinking seams of coal. Enterprises, by contrast, operate on renewable energy, as shown on the right hand side of the title slide. Their proprietary data grows as they deploy more agents, build more models and instrument more workflows. Every incremental deployment generates more signals, more context, more labeled outcomes. They're on an experience curve - one that accelerates as data harmonization improves. JPMorgan Chase is the most obvious example, but it represents a much broader pattern. The more enterprises connect their systems of record with their emerging agent ecosystems, the faster their marginal costs fall. This is why we believe the real economic opportunity lies with worker-bee AGI inside the enterprise, not messiah AGI in the labs. The image of windmills and solar farms on our cover slide symbolizes the renewable nature of enterprise data. In the age of AI, algorithms are increasingly commoditized and developed by specialized labs. But shaping data - aligning it with workflows, policies, context and outcomes - is where competitive advantage resides. This brings us to a critical question: Could OpenAI simply partner with leading enterprises to overcome its data disadvantage? In theory, yes. In practice, we see three barriers: In other words, owning a frontier model isn't enough. To win in the enterprise, the model must be embedded in the workflow to include policy, security, data lineage, context, orchestration, and deterministic logic. This is software, not science, and thus far frontier labs are not enterprise software companies. (Note: Our colleague David Floyer disputes this premise. He believes research labs such as OpenAI and Anthropic will build great enterprise software on top of their LLMs and simplify the adoption of enterprise AI. We'll be digging into and debating this topic with him in future Breaking Analysis episodes.) As a result, the disruption won't manifest in the technology model alone, rather we'll see it in the operational and business models of emerging AI software companies - including pricing. The seat-based pricing model that has defined enterprise software for decades will not survive the agentic era, in our view. Infrastructure consumption models will evolve as well. We believe the winners will shift toward value-based/outcome-based pricing aligned with real business results. This is the heart of service-as-software. And AWS - whether intentionally or instinctively - is leaning directly into this shift. It is missing some pieces, however, which we'll discuss in detail. Let's park the technology model for a moment and examine the coming changes in the operational and business models of enterprises as a result of AI. As we've argued in earlier Breaking Analysis episodes, the shift to AI-driven operations requires a wholesale transformation of how organizations function - not just in their technology stacks, but in their operational and business models. When the industry migrated from on-premises software to software as a service, the burden fell largely on information technology departments and software vendors. Mainstream organizations didn't have to rethink how their companies worked. This time is different in that the entire organization will change. Practitioners often say the hard part of transformation is people and process, not technology. In the agentic era, all three are hard. The operational model, the business model and the technology model must move in concert, and none of them can remain anchored in legacy assumptions. For six decades - despite new devices, new user interfaces and new software delivery models - the industry continued to build in silos. These were essentially craft cells for knowledge work, each optimized locally because that was the only way we knew how to automate. The shift ahead replaces these craft silos with an end-to-end assembly line for knowledge work. That represents a profound process change. The organizational structures built to make machine-like efficiency possible no longer align with end-to-end outcomes. On the business model side, the shift is equally profound. Traditional software economics were built around nonrecurring engineering or NRE costs. Pricing was often seat-based with added maintenance fees, and steadily improving (and predictable) marginal costs as volume grew. Even in the cloud era, despite variable infrastructure costs, vendors benefited from the same model - upfront NRE, light ongoing maintenance and falling marginal costs as scale increased. AI breaks that pattern. In the new model, organizations deliver outcomes, and pricing will be based on value, usage or direct outcome proxies. More important, the foundation of economic advantage shifts from physical capital and depreciating software assets to digital expertise that compounds. Intelligence becomes capital. The more data that flows through the system, the richer the experience base becomes. Cost advantages and differentiation strengthen simultaneously, creating winner-take-most dynamics. This is where tokens enter the equation. Software companies not only face cloud cost-of-goods-sold, they now incur token costs. Though consumers may benefit from improvements in graphics processing unit price-performance, the marginal economics that once accrued to software vendors increasingly accrue to the organizations that climb the AI learning curve fastest. Getting volume and getting there fast becomes critical; once a company builds compounding advantage through expertise, it becomes extremely difficult for others to catch up. This gets back to the difference between models and agents. The model guys are in this "token grind" where through distillation and advances in the frontier of intelligence, their costs are coming down something like 10 to 30 times per year, some obscene number when it's just the raw model and the application programming interface. But when it's an agent and there's a learning loop in there, their prices are much more stable. And so that gets back to what we're going to talk about, and where AWS is focused, which is the surrounding scaffolding needed to build, manage and govern agents. The bottom line is the economics of software are being rewritten. Depreciating assets are giving way to compounding expertise, and organizations that build these systems early will gain durable structural advantage. In our view, this shift underpins the emerging service-as-software model - and explains why enterprise AI will look nothing like the transitions of the past. The technology shift unfolding now is far more complex than the move to cloud. The cloud era changed the operational model for running the same application silos - breaking monoliths into microservices but leaving the underlying fragmentation intact. Data remained siloed, and even the application logic itself stayed isolated within domain-specific systems. What is happening now is much different. For decades, enterprises converted data into a corporate asset through the rise of relational databases. That was a significant milestone, but it didn't unify how businesses reason. In the agentic era, humans and agents need a shared understanding of the rules, processes and semantics that govern the enterprise. That requires constructing a true system of intelligence or SoI - a shared asset that expresses how the business runs. This is something the industry has never done before, and it upends 60 years of accumulated investment in application-centric architectures. Developing a migration path for this transformation is extraordinarily challenging, because every existing enterprise today is built on top of layers of deterministic logic that reflect the constraints of siloed automation. The "Tower of Babel" metaphor on the slide above captures the legacy landscape. Each application has its own language, its own worldview, its own schema. They don't talk to each other. Even when organizations attempt to consolidate through a lakehouse, the silos reappear inside the lakehouse itself - sales, service, logistics and every other domain carries its own schema, definitions and constraints. Asking cross-functional questions - for example, what happened, why it happened, what should happen next - requires stitching these worlds together with extensive data engineering. This is brittle work, and it reinforces the fragmentation that enterprises are now trying to escape. The move from application-centric silos to a unified, data-centric platform is not simply a technology upgrade. It is a rearchitecture of how intelligence is represented inside the enterprise. And it is this shift that will determine whether organizations can deploy agents that truly understand and drive end-to-end business outcomes. The slide above depicts a dead whale being kicked down the beach - an intentionally stark metaphor for the scale of the challenge ahead. The reference traces back to Microsoft Corp. co-founder Bill Gates, who once mocked attempts by Lotus and WordPerfect to bolt graphical interfaces onto DOS-era software. He likened that effort to "kicking dead whales down the beach" - an impossible, grinding task weighed down by accumulated legacy code and technical debt. The situation today is exponentially harder. Instead of a single dead whale, the industry faces a thousand. Sixty years of investment in application-centric silos must now be harmonized into a coherent systems of intelligence. Every layer of legacy logic, schema, workflow and domain-specific constraint has to be reconciled. And at this moment, no one vendor has a complete solution for how to do it. The metaphor captures the reality in that preserving compatibility with decades of siloed systems while attempting to build unified, agent-ready architectures is not only difficult - it borders on impossible without rethinking the foundational architecture of the business. The agentic era demands a level of semantic, procedural and data harmonization that legacy architectures were never designed to support. In this next section, we'll turn to what the broader transformation means for AWS. A series of announcements at re:Invent - general availability milestones, continued investment in Kiro, enhancements across the software development lifecycle, and significant expansions to Nova and Nova Forge - underscore how AWS is positioning itself for the service-as-software era. But before we do that, let's revisit how we see the emerging technology stack evolving. The slide above depicts the transition from application-centric silos to a data-centric platform. At the base sits the data platform - the logical data estate aggregating information from operational systems, websites and external sources. Vendors such as Snowflake Inc., Databricks, and AWS with its Redshift have done remarkable work consolidating these assets. But consolidation is not the same as harmonization. The data platform remains neither machine-readable nor agent-readable. Even humans often require a business intelligence layer to interpret it. Functionally, these systems provide high-fidelity snapshots of what happened - two-dimensional views constrained by the schemas of each domain. At best, with feature engineering and narrow machine learning, they can forecast what's likely to happen within a limited scope. What enterprises need next is a true system of intelligence, represented in green on the slide. We've discussed previously the need for a four-dimensional, end-to-end map of the business. This is the layer that gives both humans and agents a shared understanding of processes, rules and dependencies. Some organizations attempt to build this today with platforms such as Palantir, but doing so often requires forward-deployed engineers or FDEs crafting bespoke solutions at great expense (~$1 million annually for each FDE). Each customer effectively rebuilds the system from scratch. Above that sits the system of agency, shown in yellow. This is the agent control framework - important for orchestration, policy and workflow activation, but not itself the source of intelligence. It draws meaning from the system of intelligence beneath it. For agents to make decisions with the same contextual awareness humans use today - understanding, for example, how delaying an order for lack of a part could jeopardize a major contract - they must be anchored in a harmonized representation of the enterprise. Without that harmonization, neither humans nor agents can reliably see across domains or reason about second- and third-order effects. Humans can run isolated what-if analyses, but scaling those insights across the full enterprise is impractical when the underlying maps must be stitched together manually. The key point is that the emerging software stack requires customers to transform their technology model. The data platform alone is not enough. Systems of intelligence and systems of agency must work in tandem to provide the end-to-end visibility, shared meaning and decision-making context that define the agentic era. The next question we want to address is how AWS maps onto this architecture - and where its recent announcements signal its intent to lead. With the benefit of four days of re:Invent behind us, a clearer picture emerged of where AWS is investing and how it aligns to the evolving software stack shown above. When mapped against the model of data platforms, systems of intelligence, and systems of agency, the green layer - the system of intelligence - is still largely incomplete. It doesn't come out of the box. Enterprises such as JPMorgan Chase, Dell Technologies Inc. and Amazon.com Inc. itself have had to build it themselves, whereas most mainstream enterprises don't have the capability to do so. Where AWS is strong today is in the layers surrounding that gap. Bedrock has matured considerably. A year ago, reliability issues - struggles even with two and three nines - triggered urgent internal focus. That work appears to have paid off. Bedrock now serves as the abstraction layer above LLMs that AWS has long needed. AgentCore, introduced as a control framework for building multi-agent systems, shows real promise as well. Even more compelling are AWS' first-party agents. The Kiro autonomous coding agents, along with new DevOps and security agents, formed some of the most interesting narratives of the week. They still have to earn customer trust, but the conceptual story around them is strong. These agentic capabilities, however, highlight the critical dependency on the missing middle layer. Agents are only as effective as the context they draw from. AI is programmed by data, and without a well-structured system of intelligence to feed them, their potential is limited. At the bottom of the stack, the data platform continues to evolve. SageMaker's lakehouse features - especially S3's expansion into table formats such as Iceberg - signal S3's shift from a simple get/put object store into something more structurally aware. Neptune, AWS' graph database, receives far less public attention, including its knowledge graph capabilities, yet it represents an important ingredient for shaping contextual data. in our view. This is where the divergence between AWS and Microsoft is instructive. Microsoft prefers to articulate where customers should be three to five years out and seed early product iterations - Fabric IQ being a recent example, potentially even designed to counter Palantir Technologies Inc.'s momentum. AWS, by contrast, tends to stay one step ahead of customers' stated needs, solving problems incrementally rather than prescribing long-horizon architectures. Even so, Neptune already plays a role in bottom-up AI workload development, helping customers structure data for agentic applications. And one can imagine a future where Neptune joins S3 buckets and S3 tables as a more central mechanism for harmonizing data, though that would represent a strategic evolution for AWS. The broader takeaway is that AWS is prioritizing agent-specific tooling. As Swami Sivasubramanian, Amazon's vice president of agentic AI, put it in his keynote, the company wants to be "the best place to build agents." On that dimension, progress is evident and deserving of high marks. But the system of intelligence - the contextual substrate agents depend on - is still an open frontier across the industry. Vendors such as Palantir and Celonis SE operate directly to address this space, but no one vendor has yet solved the problem at scale. AWS may lean on Neptune, expand the capabilities of S3, or take a different path altogether. What's certain in our view is that the green layer - the harmonized representation of data and process knowledge - is the piece that will ultimately determine how far agentic architectures can go. The DevOps agent emerged last week as one of the most consequential pieces of AWS' agentic strategy. When mapped onto the emerging software stack - systems of intelligence in green and systems of agency in yellow - the DevOps agent sits in the layer that orchestrates digital operations. Understanding why this matters requires looking at the complexity of modern enterprise estates. A DevOps agent must reason across sprawling, heterogeneous environments composed of infrastructure, middleware, application components and thousands of microservices. Unlike a monolithic system such as SAP, where a single vendor created an end-to-end framework with well-understood internals, AWS environments are far more open-ended. The DevOps agent is designed to look across all of digital operations, identify when something breaks, determine the root cause and, when confidence is high enough, execute a remediation plan - potentially without human intervention. This introduces an important concept in that DevOps agents function as training wheels for the broader system of intelligence. They represent a prototype of what an enterprise-wide reasoning layer could become. Digital operations are only one slice of the problem. The full system of intelligence must encompass all business operations, and that requires a 4D map of the enterprise that shows how systems connect, their dependencies and how actions in one domain influence outcomes in another. AWS described this mapping capability as topology - an attempt to learn a representation of how Amazon services and infrastructure components interrelate. Conversations with vendors such as Datadog Inc., Dynatrace Inc., Splunk Inc. and Elasticsearch B.V. revealed that each has deep visibility into its own domain and partial insight into adjacent ones. Dynatrace, for example, places a probe on every host, enabling it to construct a causal graph - a kind of twin of digital operations. The DevOps agent uses these external views when needed. When it identifies a problem but lacks sufficient visibility to diagnose it, it can call out to partners such as Dynatrace and say, in effect: Run the deeper query for me. This bottom-up collaboration hints at how a general system of intelligence may ultimately emerge - not as a single monolithic product but as an aggregation of increasingly broad platforms across an ecosystem. This raises a broader architectural question related to top-down versus bottom-up approaches. A pure bottom-up approach risks "boiling the ocean" - digging two tunnels from multiple directions without a guarantee that the tunnels meet in the middle. A purely top-down approach lacks grounding in operational reality. The workable model appears to be a hybrid where the the outcome is defined top-down, then stitched together with the necessary data bottom-up to support that outcome. In practice, this might look like deploying the DevOps agent for a specific service area and augmenting it with Dynatrace data to achieve end-to-end visibility. Once that outcome works, extend it incrementally to additional services. This creates the scaffolding for a system of intelligence one outcome at a time - a middle-out expansion if you will, anchored by practical workflows. The DevOps agent therefore serves two purposes: 1) It solves a tangible operational problem today; and 2) It lights the path toward an enterprise-wide intelligence layer that will ultimately govern agentic systems. Kiro, first announced at the AWS Summit in New York, took on new significance last week. Anyone who still believes AWS is behind in AI need only look at the cadence and depth of innovations emerging around Kiro and its autonomous coding capabilities. Despite that, the keynote significantly understated its importance, in our view. Developers we spoke with emphasized that Kiro is quickly becoming central to their workflows. And the reason is that Kiro goes beyond the vibe-coding paradigm that has dominated the last year. Developers, by the way, still love Cursor, which continues to sets the standard. For months, the conversation in AI-assisted development has centered on scaffolding - the supporting structures that transform rapidly evolving models into agentic applications. Tools such as GitHub Copilot pioneered "code complete" - hit tab, finish your line. Cursor advanced the state of the art by customizing the integrated development environment so developers could chat with the agent, enabling longer-horizon tasks. Google's Antigravity, which came from the Windsurf acquisition, followed the same path. These tools are powerful, but they remain forms of vibe coding. The artifact is still the code. You live in the code. You fix the code. The scaffolding evolves as models advance. Kiro breaks from this model and enters new ground. Instead of writing code and letting the agent assist, Kiro starts upstream - with the requirements document. The agent works at the ideation level, partnering with the developer to flesh out specifications, tighten ambiguities, highlight missing details and introduce best practices. The requirements document then feeds into a design document, creating a clear specification. Only then does Kiro generate code. And crucially, the code is disposable. When the software needs to evolve, you don't traverse through the codebase trying to predict what will break. You update the requirements, adjust the design and regenerate the code from the spec. This reverses decades of software engineering logic. The artifact is no longer the code - it is the specification. Though Cursor can function similarly, it is still code-first out of the box. Even more striking is the potential impact on legacy systems. AWS' Transform product can reverse-engineer a design document from an existing codebase. AWS believes it can go further and generate the requirements specification itself. That opens the door to something the vibe-coding world has not addressed - brownfield modernization. Instead of writing elaborate migration plans or manually dissecting aging monoliths, enterprises may be able to produce specifications from legacy code and move forward iteratively, regenerating components as needed. This is why Kiro is so significant, in our opinion. It elevates the abstraction from writing code to defining intent. This aligns with the broader shift toward agents, systems of intelligence, and outcome-oriented architectures. When AWS first introduced Kiro, it gave a nod to vibe coding because it was the trend of the moment. But even then, they hinted that Kiro represented something more. The story last week at re:Invent emphasized this is not another flavor of assisted coding. It is a new model for how software gets built, maintained and evolves. AgentCore emerged as one of the most pivotal announcements of the week because it represents the scaffolding required to make agents production-grade. Bedrock has steadily matured into the foundational abstraction layer for model access, but AgentCore sits alongside it as the operational backbone for multi-agent systems. It is the control infrastructure that governs how agents behave, how they coordinate and how they remain within defined boundaries. AgentCore provides the essentials of memory, runtime services, utilities like code interpreter and - critically - observability and policy enforcement. These capabilities matter because agents are not like traditional software components. Governing a data platform is relatively straightforward. Policies answer simple questions like who can access which data, under what conditions? Even column-based or tag-based controls remain manageable within deterministic systems. Agents are different. They take actions. They chain actions. They invoke tools. They do so with varying parameters, in varied contexts, and with probabilistic reasoning. The policy framework must therefore answer far more complex questions such as: Should this agent be allowed to take this action, with these parameters, in this context, in pursuit of this goal? That level of governance is exponentially more sophisticated than anything required in legacy data or application systems. This is why AgentCore is significant. Putting agents into production requires deterministic enterprise scaffolding - strong guardrails, clear boundaries, actionable observability and the ability to enforce policy across dynamic behaviors. This is heavy enterprise software, not something LLM vendors are likely to deliver. Frontier labs focus on models, not operational governance. Enterprises need a control plane that can manage armies of agents reliably, safely and consistently. Again, this is a debate inside theCUBE Research, which we'll continue to explore. AgentCore begins to fill this gap. It establishes the governance fabric that agents must operate within and provides the layer into which downstream capabilities - systems of intelligence and systems of agency - will ultimately connect. It is the early architecture of agent-native operations, and AWS appears intent on owning this part of the stack. Among all the news coming out of re:Invent, Nova Forge stands out as the most strategically significant. The framework aligns directly with our thesis that enterprises will differentiate by owning their data, customizing their models, and building systems of intelligence that reflect their proprietary workflows. Nova Forge serves as the mechanism that makes this possible. It is the first offering from a major U.S. vendor that delivers open weights and training data, giving customers the ability to adapt the model to their own environments. The availability of training data is a critical distinction. While startups continue to build on lower-cost open-weight models such as DeepSeek, those offerings generally include weights only - not the underlying data. Nova Forge allows enterprises to substitute, augment or extend the training corpus with their own information, creating a model tuned to their specific context and competitive advantage. This introduces several strategic dynamics. Customizing a model through pretraining or reinforcement learning effectively welds an enterprise to that version of the model. When the next, more capable model arrives months later, the tuning process must be repeated. The complexity is not just operational - it can introduce behavioral instability as models shift. This is why some frontier-model providers, particularly those whose businesses depend on API consumption, actively discourage customers from performing deep reinforcement learning. Their pitch is if you avoid the heavy customization, you sacrifice some tight integration but stay aligned with the rapid improvement cycle of frontier models. This perspective underscores a broader debate about how the enterprise AI stack will evolve. One argument holds that frontier-model companies will become the next great software vendors, providing layers of tooling atop their LLMs to simplify enterprise integration. The counterargument is that mainstream enterprises - and the ISVs serving them - will need far more specialization than a handful of frontier models can deliver. Agents are not just RPA 2.0; they represent much more. As these agents proliferate, organizations will require a diverse portfolio of models, many of them small and highly specialized. In that world, frontier models play a role - but as orchestrators, handling complex planning and reasoning. The bulk of enterprise differentiation will come from customized, specialized models trained on proprietary data. Nova Forge is the first major signal that AWS intends to support this path by delivering open weights, open data and the ability for customers to shape the intelligence layer of their enterprise directly. A major subplot at re:Invent was AWS' forceful claim that it is the best place to run Nvidia Corp. GPUs. That declaration hits with irony, given the persistent narrative that AWS lacks allocation, faces political challenges with Nvidia, and is focused on its own silicon efforts - Trainium in particular. Meanwhile, public commentary from outlets such as CNBC continues to over-index on competition to Nvidia, framing tensor processing units, Trainium and other accelerators as existential threats. The reality is more nuanced. GPUs are expensive, and performance per watt is the defining metric. Volume determines learning curves. And on that dimension, Nvidia remains in the driver's seat. High volume gives Nvidia cost advantage, supply advantage and architectural advantage. Unless the company stumbles, the position is theirs to lose. Nvidia also locked up the leading process nodes at Taiwan Semiconductor Manufacturing Co. securing massive capacity. It is reminiscent of Apple Inc. 15 years ago: No competitor could match its handset performance because Apple had tied up the most advanced silicon for entire product cycles. No alternative supplier could close the gap. AWS' historical challenge in securing Nvidia volume traces back to its own infrastructure strategy. The company spent years building Nitro, designing composable data centers, expanding instance types, optimizing storage and networking, and refining hypervisors. That approach made AWS infrastructure-smart - but not the earliest or largest buyer of full-stack Nvidia systems. When the ChatGPT moment hit, Nvidia wanted to sell integrated data center kits, not just racks. Providers that lacked infrastructure sophistication were forced to buy everything, and in return they received the larger allocations. That included Microsoft and the neoclouds, and Oracle as well - though in Oracle's case, the scale-up design served the needs of its database architecture. Then came a second, largely unspoken issue: failure rates. For the last 12 months, the volume GPU has been the GB200. Multiple sources reported that failure rates were as high as 50%. No one talked about it publicly. The industry was reluctant to make Nvidia look bad and risk a reduction in allocation. But the implications were that supply would continue to be constrained. This was the year hyperscaler capital spending surged, and Wall Street was left wondering where the returns were. Utilization was low for reasons almost nobody understood externally. The shift began in October, when the GB300 surpassed the GB200 in volume. The GB300 installs more smoothly, runs more reliably and appears to deliver meaningfully higher quality. Neocloud providers such as Lambda reported excellent performance from GB300 systems. As GPU specialists willing to buy full stacks, they secured allocations consistent with that model. This dynamic contributed to the broader narrative of the "GPU-rich" and the "GPU-poor." The GPU-rich could advance model training, inference and agentic workloads aggressively, while the GPU-poor were stuck rationing capacity. But now the dynamics may change as infrastructure-smart providers - those that invested in custom silicon and composable data centers - begin to leverage their own accelerators for meaningful workloads. There is enough Trainium deployed inside AWS to run frontier models such as Anthropic for inference at scale. Training remains a different story, despite public commitments on both sides. Anthropic's leadership has acknowledged that training on Nvidia would require far fewer chips, which explains why it negotiated a larger deal with Google to run on TPUs - even as AWS invested billions in the company. Still, TPU volume will remain limited. Google will not match Nvidia's scale unless Nvidia falters. Nvidia sells to Google's competitors. Neoclouds depend on Nvidia allocation and are unlikely to compromise it. Neither Amazon nor Microsoft is going to shift to TPUs. Volume determines trajectory, and Nvidia's volume remains unmatched. Could TPUs create pricing pressure? Possibly. A credible competitor gives buyers negotiating leverage. Nvidia's 70% margins may face some compression. But the software ecosystem, tooling, libraries, and developer familiarity remain heavily skewed toward Nvidia. The lesson from this week is that AWS is becoming more assertive about the GPU story because it believes its infrastructure - combined with Nitro, Trainium and rapidly improving Nvidia integration - positions it strongly for the next phase of AI infrastructure. But the industry-wide GPU story is more complicated than energy constraints and data center buildouts. It includes an unspoken constraint in that high GPU failure rates, massive CapEx tied up in underutilized infrastructure, and a rapidly maturing next-generation GPU cycle is reshuffling the hierarchy of who is GPU-rich and who is GPU-poor. The slide above tells the story of two worlds. On the left sits the pursuit of messiah-level AGI - the frontier labs chasing grand breakthroughs. On the right is the hard, unglamorous scaffolding required to make AI useful in enterprises. The contrast underscores a fundamental divide. The frontier narrative often centers around singular breakthroughs and personalities, while the enterprise ecosystem is focused on building practical systems. The hyperscalers, ISVs and large incumbents are constructing the heavy scaffolding at both the agent layer and the data layer. AgentCore represents the control and governance framework. But the other half of the equation is even more foundational - bringing together data and action in a consistent, governed system. Actions - meaning tools and callable workflows - must pair with data, which provides the context that guides what an agent should do. When those come together coherently, the result is a knowledge graph, and that "4D Map" becomes the system of intelligence. This is hard enterprise software work, and frontier LLM vendors are not database vendors or workflow vendors. They are a long way from building this substrate. Enterprises that operate at scale - Amazon.com, large banks such as JPMorgan, and major technology firms that include Dell - confirm that they must build this system of intelligence themselves. It does not exist out of the box. Our early work, such as the "Uber for All" model explored years ago, hinted at the idea of people, places, things, activities - drivers, riders, prices, locations - brought together into a unified digital representation of an enterprise. That remains the ambition today: a real-time, four-dimensional map of the business. It's why the agentic era will take most of a decade to unfold, in our view. Analytics-oriented vendors can provide slices of this capability today, but only within confined analytic domains. Building the full operational twin of the enterprise - harmonized, contextualized and ready for agents - is a far larger undertaking. The slide above shows a camel covered in logos - more logos than a camel has fleas. The image captures the current frenzy. The market's reaction to the word "agent" has resembled a Pixar moment when someone yells "squirrel," and the dogs instantly lose focus. In this cycle, someone yells "agent," and venture capital runs at full speed. The problem is that most of these agent startups are built on only the thinnest layer of scaffolding. Some domain agents - such as those in legal tech - carry embedded knowledge of workflows. But the vast majority lack the heavy lifting required for real enterprise value. They do not solve the integration challenge between data, action space, and governance. They do not provide the agent control apparatus. They do not offer the system of intelligence. The companies doing that work are the hyperscalers and the major software vendors. The differentiation will not come from shiny agent UIs or narrow features. It will come from the depth of integration with data systems, workflow systems and the governance and control planes that bind agents to enterprise policy and process. That's why this era will be defined not by the number of agents a vendor can showcase, but by the scaffolding that underpins them. And that scaffolding is where the real enterprise value will accrue in our view. The final slide above ties the entire discussion together, showing how the emerging software stack will take shape and where different players fit. The center of gravity - the high-value real estate - is the system of intelligence layer. Companies such as Celonis and Palantir are furthest along, in our view, though they take different approaches. Palantir remains services-heavy but has made significant software advances. Other graph-centric and process-centric vendors such as ServiceNow Inc., SAP SE, Salesforce In. and essentially every SaaS company with embedded process logic, are now targeting this same layer. To participate meaningfully, these players must build out their data platforms and confront a major business-model transition. As they merge process logic with data and enable agents to take increasingly autonomous action, they weaken their traditional seat-based subscription models. This is an innovator's dilemma in real time. For example a $50 billion dashboard-centric industry (BI) faces disruption. Efforts to introduce "talk to your data" interfaces are part of that response, but they do not change the underlying economics. Below the system-of-intelligence layer sit the data platforms: Snowflake, Databricks and the lakehouse constructs of SageMaker and S3. Above it sits the system of agency - Bedrock and the agent-control frameworks. And throughout the stack, governance is ubiquitous; every major player is adding governance layers to address the risks of agentic systems. Meanwhile, the software development lifecycle is shifting from a linear pre-gen AI workflow to one that is nonlinear, interactive and deeply agent-driven. As the hardware stack is redefined - compute, storage and networking - the entire software stack is being redefined with it. Zooming out from re:Invent 2025 and the broader move toward service-as-software, the momentum feels like early baby steps toward a decade-long transformation. Two major trends are emerging: The bubble is not bursting, but it is growing. There may be some deflation on the consumer side, particularly around OpenAI's positioning, but enterprise activity is only beginning. Building worker-bee AGI beyond basic RPA 2.0 and microservices 2.0 is a long, challenging road. But the productivity gains across software development and operations tools will be large and immediate. Over time, those agentic capabilities will migrate into every enterprise process, creating the marginal-economics advantage that defines the service-as-software model. The broader market dynamics may feel bubblicious - echoing Ray Dalio's comment that we're "80% of the way into the bubble." Whether that was macro commentary or a push for his preferred trades, the point stands in that bubbles don't burst until something pricks them. We're not there yet. History shows pullbacks of 10% or more can happen repeatedly during an expansion, as they did in the dot-com era. Recent market disruptions look more like early tremors than the hard correction that would unwind current valuations. The agentic era is coming. Its shape is becoming somewhat more clear. But the real transformation - the unified data foundation, the system of intelligence, the governed agent layer - will take the better part of a decade to mature.

Meet AWS' Frontier Agents Built to End Developers' 2 AM Nightmares | AIM

The company said the agents are autonomous, scalable, and capable of operating for extended periods without intervention. When AWS introduced Kiro earlier this year, the company presented it not just as another AI assistant but as a new way to rethink how software should be developed. At AWS re:Invent 2025 in Las Vegas, the cloud giant made no secret of its push to win over developers. With the launch of Kiro Powers and Frontier Agents, AWS says it is closer than ever to solving one of the oldest problems in software engineering by helping developers ship production-ready code faster, more reliably, and with less frustration. The company said the agents are autonomous, scalable, and capable of operating for extended periods without intervention. It said the approach was shaped by three insights: that teams gain more value when agents pursue broader goals, run multiple tasks in parallel, and operate independently for long durations. The launch of Frontier Agents includes the Kiro Autonomous Agent, the AWS Security Agent, and the AWS DevOps Agent. In an exclusive interaction with AIM, Amit Patel, who leads engineering for Kiro, described 2025 as a period of discovery, rapid evolution, and unexpectedly strong customer demand. Explaining why such agents are needed, Patel said that DevOps problems always strike at the worst possible moment."These things happen at 2 o'clock in the morning," he quipped. Patel said that the DevOps Agent is built to prevent that, identifying incidents, analysing root causes, and even fixing them before teams are paged. The company said the agent has handled thousands of escalations internally, identifying root causes in an estimated 86% of cases. On the other hand, the Kiro Autonomous Agent can plug into Jira or GitHub and pick up backlog items on its own. "Engineers can focus on building features," Patel explained. Meanwhile, the agent can look at tickets and fix them. Patel sees this as a breakthrough for reducing technical debt, one of the biggest productivity drains for engineering teams. The Security Agent can catch problems early, continuously check code, and remove the manual overhead of repeated audit cycles. Patel said that one of the most important lessons from early user tests of Kiro was that simple code completion, now a commodity feature across AI coding tools, was nowhere near enough. That push led to one of Kiro's defining capabilities, 'spec-based development'. Developers can describe requirements in natural language, generate a design, break the work into tasks, and then have the system generate code, all within a structured workflow. Patel described it as a way to preserve the fluidity of AI-assisted coding while forcing the system to think like an engineer rather than a text predictor. Moreover, the new feature, Kiro Powers, gives AI agents extra skills whenever needed. It can pull in the right tools and knowledge on demand, such as Stripe, Figma, or Supabase integrations, by loading only the MCP tools and guidance required for the task. Patel explained why this matters. "Inside Amazon, some teams load up 50 or 60 MCP servers... and you get a context problem. That leads to poorer results." Kiro Powers solves this by loading tools only when needed, keeping the context window clean. "It dynamically loads the relevant context at the relevant time," Patel said. "It improves performance, reduces cost, and avoids context problems." Postman is one of the early adopters of this tool. Eventually, AWS wants Kiro Powers to be compatible with tools outside its platform and to adopt a more open-ecosystem approach than many competitors. Speaking about Kiro, Patel said that although enterprises tend to move slowly, interest is already surging. "It's only been a couple of weeks since GA, but we've had a lot of enterprise interest," he said. Internal teams at AWS have become some of Kiro's biggest users. Moreover, Patel noted that enterprises are already asking for more robust governance controls. "One customer asked if they could have a Kiro Power specific to their enterprise, loaded on every installation and always used," he said. "They don't want deviations from coding patterns." Asked specifically about India, Patel said AWS isn't segmenting capabilities by geography but expects strong adoption. "It's going to be very interesting for India because we have such a big tech community," he said. "Bangalore is the AI hub of India." Patel also spoke about how pricing models are likely to change. Kiro currently follows a seat-plus-credits structure, but background agents may require a different approach. "For asynchronous and cloud-based agents, you'll likely see a usage-based model," he said.

AWS supercharges agentic development with AgentCore - SiliconANGLE

AWS broadens access to agentic development as autonomous software gains traction Agentic development is accelerating at Amazon Web Services Inc., opening the door for a far wider range of builders to create autonomous software. What once required deep machine learning expertise is now becoming accessible to developers across the spectrum, thanks to tools that dramatically lower the entry barrier. With SDKs such as Strands and IDEs such as Kiro, creators can code agents locally and deploy them to the cloud with full enterprise-grade governance. AWS is positioning that accessibility as a defining pillar of its platform strategy, according to Marc Brooker (pictured), vice president and distinguished engineer of agentic AI at AWS. "There are two big things going on here," he said. "One of them is building agents locally; building an agent on my laptop has become very accessible to normal developers. It's no longer something that requires a lot of scientific expertise. Even for people without traditional software development skills, you can build an agent in this vibe coding mode of line by line, requirement by requirement." Brooker spoke with John Furrier at AWS re:Invent, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how agents, data and next-generation infrastructure are reshaping cloud development. Several shifts are continuing the long arc of increasing abstraction in software development. The industry moved from machine code to servers, to cloud, to serverless -- and now into agent-driven apps built from business requirements rather than syntax, according to Brooker. "With agents, we're using AI models to do that planning, to figure out that path to achieving the goals," he said. "That means you can give them more open-ended goals, and you can give them more autonomy to go off and discover things and find facts and bring them together." For developers wondering where agents live in AWS, AgentCore is a new runtime that provides a serverless, secure execution environment. In essence, AgentCore is becoming the "EC2 + Lambda moment" for agentic computing, Brooker noted. "AgentCore is the console for agents; it's the core building block, but then you can bring in anything else in AWS or anything else outside AWS," he said. "There are open protocols like MCP that allow you to connect to wherever your data is, whether it's in AWS, whether it's in one of our database services, whether it's in S3 or in a third-party software as a service." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of AWS re:Invent:

AWS Unveils AI Agents That Can Independently Handle Software Development

The DevOps Agent can resolve and proactively prevent incidents Amazon Web Services (AWS) unveiled frontier agents, a new class of autonomous and scalable artificial intelligence (AI) agents for enterprises. The company said that it wanted to develop AI agents that do not require constant monitoring and those which can only complete one task at a given time. There are three specialised agents in this class, which were first announced at AWS re:Invent 2025, and are aimed at software development teams. These include a coding assistant dubbed Kiro, a security analysis tool, and an operations monitoring agent. AWS Unveils New Class of Frontier AI Agents In a newsroom post, the cloud services provider announced its first three frontier agents, highlighting their key characteristics. These AI agents can operate autonomously and work towards a goal instead of a single task. The company claims that these are also scalable, meaning they can perform multiple tasks simultaneously and organise complex tasks across multiple agents. AWS claims these agents can also operate independently. Coming to the three agents, first is Kiro. It is described as a virtual developer, which can manage coding tasks, navigate repositories, and maintain context over long workflows. AWS says Kiro can handle complex multi-step work, like large code refactors or cross-repository updates, without needing repeated instructions. Second is AWS Security Agent. It is a tool that can scrutinise code for vulnerabilities, perform penetration testing simulations, and help build applications with security baked in from the start. It is said to help integrate security review into the development lifecycle, rather than as a separate post-development step. The third agent is AWS DevOps Agent. It is an operations-oriented agent that monitors deployments and system health, helps detect and respond to incidents, and assists with ongoing reliability and performance improvements. It can analyse logs, coordinate diagnostics and recommend mitigation steps for issues. Under the hood, these agents combine generative AI models, memory architectures and automation tooling, effectively becoming an extension of a developer or DevOps team. For instance, Kiro can clone repositories, analyse dependencies, plan tasks, submit pull requests and even run automated test suites. While the initial launch focuses on software development workflows, AWS has hinted that these frontier agents could eventually be extended to other domains, such as data pipelines, customer support automation, infrastructure management, depending on user needs.

AWS rolls out autonomous AI agents to bolster Nvidia-led cloud push - The Economic Times

Per the company blog post, the agents are autonomous, scalable, and independent of human intervention. The move follows Amazon's AWS cloud computing unit laying out plans to adopt Nvidia's technology in its AI computing chips as part of the former's efforts to get major AI customers to use its services.Software giant Amazon unveiled three artificial intelligence (AI) agents -- Kiro, AWS Security Agent, and AWS DevOps -- on Tuesday. In a blog post, the company said the frontier agents are "autonomous, scalable, and independent of human intervention." The development comes after Amazon's AWS cloud computing unit on Tuesday laid out plans to adopt Nvidia's technology in its AI computing chips as part of the former's efforts to get major AI customers using its services. Amazon AI agents The Kiro autonomous agent is designed for software development, the tech giant wrote. It focusses on software development workflows, handling tasks such as bug triage, code coverage improvements, and multi-repository changes. Developers can assign tasks directly from GitHub, and the agent learns from earlier work to handle similar tasks better in the future. Teams can also connect Kiro to tools like Jira and Slack so it can keep track of ongoing projects and updates, the blog post reads. The AWS Security Agent checks software for risks during early design stages and when code is updated. It also performs automated penetration tests, which is normally a slow, manual process, and provides fixes along with its findings. The AWS DevOps Agent assists teams when outages or performance issues occur. It looks at logs, alerts, and recent changes to quickly pinpoint what went wrong. AWS said the agent has been able to identify root causes in most internal tests. It also reviews past incidents to recommend ways to make systems more reliable. By automating routine development, security, and operations tasks, the new agents can help teams focus on more important work while still keeping control over final decisions, said AWS. Meanwhile, in a recent development, Amazon said it would invest up to $50 billion to expand AI and supercomputing capacity for US government customers, in one of the largest cloud infrastructure commitments targeted at the public sector. Last month, OpenAI signed a seven-year, $38 billion deal to buy cloud services from Amazon that will give OpenAI access to hundreds of thousands of Nvidia graphics processors to train and run its AI models.

AI-native agents turn to model-driven design - SiliconANGLE

AI-native agents are breaking brittle workflows, ushering in fresh programming mindsets As organizations look to roll out artificial intelligence initiatives, they're quickly discovering that the fixed workflows and step-by-step procedures that work so well in traditional programming come up short when designing models for AI-native agents. Because agents operate in open-ended, dynamic environments and make decisions the developer did not explicitly script, trying to specify every step in advance is counterproductive. A better idea is to work with the model, take advantage of its capacity for reasoning and allow it to generate the needed logic dynamically, according to Clare Liguori (pictured), senior principal engineer at Amazon Web Services Inc. "For complex tasks in the past -- when models weren't as capable -- people would go back to those same programming paradigms that they're familiar with [and] create a workflow. As it turns out, that's a very brittle approach," Liguori told theCUBE. "When [AWS] went back to the model-driven approach, we found that we can actually guide and steer the agent and let the model come up with the workflow itself, and still have the capability to do really complex tasks in the agent. It's a very different mindset among younger companies, younger developers, that are coming up in this completely non-deterministic AI world." Liguori spoke with John Furrier at AWS re:Invent, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how the need to simplify model development for AI-native agents is impacting traditional programming techniques. A major challenge in the development of AI-native agents is the large amount of "structural glue" -- such as orchestration code and guardrail logic -- that goes into model development. Instead of building the model, developers spend most of their time on the boilerplate surrounding it, which can account for as much as 90% of enterprise AI efforts. This is not just a structural inefficiency that increases the cost of AI engineering, but it actually leads to serious quality issues, according to Liguori. "We found that frontier models, especially, are so capable of reasoning and driving the tool selection and things like that in the agents, that the more that you do around it, the worse your agent gets," she said. Not having to focus on the structural "surround" also simplifies ongoing agent management, she added. This shift lets teams focus on business logic and outcomes instead of constantly reworking low-level plumbing. "As the models are getting better, which we're seeing every few months, you don't have to do anything to your agent, other than change which model you're using for your agent to suddenly get better," Liguori explained. "You don't have to change your entire software stack around it." The recognition that the inherent complexity involved in model development can be an obstacle to enterprise deployment of AI-native agents was a big factor behind AWS's decision to integrate TypeScript as an orchestration language for building agents, using its open-source AI agent framework Strands. Taking advantage of TypeScript opens the door for model development to a much broader set of developers, Liguori noted. "We wanted to make it so easy for anybody who is not an AI expert to be able to write an agent in a few lines of code," she said. "I've had product managers come to me and say, 'I wrote a Strands agent, and this is amazing.'"

Amazon's AWS Brings AI Customization Capabilities To Enterprise

AWS also had a number of big announcements related to their agent development tools. One of the most important is an extension to the company's existing Bedrock AgentCore framework for building and deploying agents. One of the biggest challenges in creating a successful AI deployment within enterprise companies today is fully tapping into the unique requirements, data sets and existing infrastructure that every organization has. I'd argue that every new Bob O'Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting and market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech. AWS Factories allow enterprises to deploy AWS-powered AI infrastructure on-premises, leveraging custom silicon and software, positioning AMZN to capture hybrid AI demand, especially in regulated industries. Nova Forge enables enterprises to inject proprietary data at multiple training stages, creating fully customized models with advanced capabilities, moving beyond standard RAG-style fine-tuning. AMZN's embrace of multi-cloud integration and customizable agent evaluation tools reflects a pragmatic, ecosystem-focused approach, aiming to facilitate adoption alongside competing vendor solutions.

AWS and the rise of agentic cloud modernization - SiliconANGLE

From lift-and-shift to one-and-done: Inside AWS' agentic cloud modernization push The historic delay between moving workloads to the cloud and optimizing them for use is disappearing, thanks in large part to the emergence of agentic cloud modernization. Enterprises are no longer content with multi-year timelines for digital transformation. Amazon Web Services Inc. is now using artificial intelligence to provide the velocity required to combine migration and modernization into a single step, according to Sriram Devanathan (pictured), director of software development at AWS Transform. "The need to move, the impetus to move, is much higher than before," Devanathan said. "People are seeing the benefits of agentic AI kick in ... They're looking at, 'How do I modernize my systems, not just lift and shift, but how do I truly make them ready for this new age?'" Devanathan spoke with John Furrier at AWS re:Invent, for an exclusive interview on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed AI-driven migration and the future of enterprise cloud transformation. Legacy systems often suffer from a lack of available human expertise, particularly with older programming languages. AWS has already analyzed nearly one billion lines of COBOL code to assist customers, achieving up to 80% automation in some migration tasks, Devanathan noted. This is achieved through agents that perform deep dependency analysis and decomposition. "You have an assessment agent that is ... able to understand all your systems [and] draw relationships between the existing components," Devanathan said. "Then after doing the dependency analysis, you can get to the next level, which is decomposition." This shifts the workflow from a sequential "migrate then modernize" approach to a simultaneous transformation. Customers are increasingly rejecting the old, slower methodologies in favor of immediate value, Devanathan explained. "I think people are seeing the speed kick in, and so we're very much hearing people tell us, 'Well, I actually don't want to do that,'" he said of customer sentiment. "'I don't want to do this two-step thing. I want to do it in one step.'"

AWS' agentic AI turning assistants into teammates - SiliconANGLE

Developer workflow poised for upheaval as AWS advances continuous-learning AI agents For all the hype around artificial intelligence coding assistants, many treat every session as a blank slate -- no memory of yesterday's preferences, context or institutional knowledge. That's the gap agentic AI aims to close, not just helping developers write code but functioning as persistent, context-aware teammates. Developers typically spend only 20% to 30% of their time writing code, with the rest consumed by DevOps, on-call rotations, security and operational overhead, according to Swami Sivasubramanian (pictured), vice president of agentic AI at Amazon Web Services Inc. AWS' Frontier agents are designed to absorb that toil as agentic teammates that learn codebases and build context over time. "The problem right now with most AI assistants [is] that they always act more like an intern instead of a very tenured teammate within the company," Sivasubramanian said. "That means every day is like day one and you keep training the intern." Sivasubramanian spoke with theCUBE's John Furrier during theCUBE's coverage of AWS re:Invent. TheCUBE, SiliconANGLE Media's livestreaming studio, explored how agentic AI aims to reshape software development through intelligent, memory-equipped teammates. The productivity gains from AWS' approach are exponential, not incremental. Where traditional AI tools promise 20% to 30% improvements, AWS is targeting five- to tenfold gains through agents that handle autonomous planning, DevOps and security testing, according to Sivasubramanian. "Every app nowadays has a compute storage database and some AI backed in," he said. "But every app by next year is going to be agentic, and that is going to be a given in my mind." The shift extends to how these systems learn at scale. When AWS runs upgrades across thousands of repositories, insights from early migrations inform later ones, allowing agents to improve continuously, according to Sivasubramanian. "When we are upgrading 100,000 repos, then suddenly we can learn what worked in one repo and what failed and then we can apply it in a different context on ... let's say the 1,000 and first repository," he said. "That means this agent is continuously learning. It gets better fast, really quick after [the] first time." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of AWS re:Invent:

Build without limits: AWS outlines an easier path for agentic AI deployment - SiliconANGLE

Build without limits: AWS outlines an easier path for agentic AI deployment After unveiling a major expansion of the Nova foundation model platform to encompass frontier artificial intelligence reasoning on Tuesday, Amazon Web Services Inc. today shifted its focus to the tools and platforms supporting agents. A key element for AWS is ease of use, a recognition that AI's inherent complexity can be an obstacle to enterprise deployment and affect confidence in an agent's ability to handle critical tasks. "Agents give you the freedom to build without limits," Swami Sivasubramanian (pictured), vice president of AWS Agentic AI, said in his keynote remarks at AWS re:Invent. "Who can build is rapidly changing. How quickly you can build is also changing. Yet building and scaling these agentic systems are harder than the problems they are trying to solve." The company announced releases designed to streamline the development process through enhancements to tools such as Amazon Strands Agents. Strands is an open-source SDK launched by AWS in May. It takes a model-driven approach to building and running AI agents using just a few lines of code. AWS announced today that it's bringing Strands to the TypeScript programming language, generally considered to be more resistant to errors and bugs. "In just the last few months, Strands has been downloaded more than 5 million times," Sivasubramanian noted. "You want the ability to rapidly deploy agents at scale." Along with the addition of TypeScript, edge device support for Strands is now generally available. AWS also announced feature in preview that will allow developers to systematically validate agent behavior, measure improvements, and deploy with confidence during development cycles. The latest releases were designed to address the proliferation of new uses for the tool as downloads continue to rise, according to Clare Liguori, senior principal software engineer for AWS Agentic AI. "We heard from customers that they needed to be able to guide agent behavior in production," Liguori told SiliconANGLE in an exclusive interview during the conference. "What I've learned in my 11 years at AWS is that customers will always use your products in ways you did not expect." Today's announcements from AWS also focused on an issue which has hindered widespread agentic deployment: model accuracy and efficiency. Organizations are reluctant to spend significant amounts of time and money on customizing models so that agents can perform seemingly routine tasks. "Today's models are not the most efficient," Sivasubramanian said. "The key to success here is quality over quantity. What if we removed the complexity and cost while still giving you access to these advanced fine-tuning techniques?" The answer, according to AWS, is to make it easier for developers to customize models by using reinforcement learning. Today's announcement of a Reinforcement Fine Tuning feature in Amazon Bedrock, along with serverless model customization capabilities in Amazon SageMaker AI, are designed to allow companies to create agents using advanced large language models without the need for massive amounts of processing power. "It's a way for customers to build models that perform better for them over time, without additional expertise required," Sivasubramanian told the re:Invent gathering. "You can choose the right approach based on your comfort level." Along these lines, today's announcement of updates for Amazon SageMaker HyperPod was also structured to support more reliable and efficient model training experiences. A new "checkpointless" training feature can preserve the model training state across distributed clusters, an important consideration as GPU usage and cluster size have grown exponentially. "This is a paradigm shift in model training," Sivasubramanian said. "Now you can recover training from faults in minutes across thousands of AI accelerators." It's one thing to build an agent to check a company calendar and find documents. It's quite another to employ agents for building a rocket that can be placed into orbit around the earth. Today's keynote at re:Invent included an appearance by William Brennan, vice president of enterprise technology at Blue Origin LLC, who described the aeronautics firm's adoption of agentic AI. Executives at the company have described the company's vision to scale up AI and lower the cost of access to space. That has included the development of an internal AI platform called BlueGPT, along with agentic workflow systems to design and manufacture space systems. "Agentic AI has exploded at Blue Origin," Brennan told the re:Invent audience. "Everyone at Blue is expected to build and collaborate with AI agents. We believe in a world where we can agentically design an entire rocket." The announcements this week from AWS reinforced the company's key message that it's heavily focused on becoming the central resource for building and running AI agents. For that strategy to be successful, organizations will have to be confident that autonomous technology can be trusted, a reality that Sivasubramanian fully acknowledged. "We need to be able to trust that agents will perform as they are expected," Sivasubramanian said. "The future of agentic AI is not on agents that can do everything, it's on agents we can rely on to do everything."

AWS simplifies AI agent customization with automated reinforcement learning - SiliconANGLE

AWS simplifies AI agent customization with automated reinforcement learning Amazon Web Services Inc. wants to solve the efficiency challenges of artificial intelligence agents and reduce their overall inference demands, and it's tackling the problem with more advanced model customization tools. The company announced today at its annual customer conference AWS re:Invent that it's introducing a new Reinforcement Fine Tuning or RFT feature in Amazon Bedrock, and serverless model customization capabilities in Amazon SageMaker AI. These new capabilities will make it easier for developers to customize AI models using reinforcement learning and potentially increase their accuracy, reliability and efficiency over the base models. It's an important capability, AWS said, because when companies create AI agents to automate business tasks, they generally want to base them on the most advanced large language models available. But these models generally have extraordinarily high inference demands, especially when they're being asked to power AI agents that have to reason through problems and employ third-party tools. That results in agents using massive amounts of processing power, even for simple, routine tasks such as checking calendars and searching for documents. These tasks can in fact be done reliably using much less powerful models, and that's why the ability to customize can be so useful for agentic developers, AWS said. Previously, customizing models required extensive machine learning expertise and advanced infrastructure resources and would take months to do, but Amazon says its new features dramatically simplify the task. By making model customization more accessible, enterprises will be able to develop more efficient, customized AI agents that can get by with a lot less processing power. RFT in Amazon Bedrock is being rolled out alongside the new AgentCore capabilities announced yesterday, and makes it simple for any company to apply reinforcement learning to streamline AI models and make them more efficient. With reinforcement learning, models acquire new knowledge through a trial and error process that's combined with human feedback. When models display good behavior they'll be "rewarded," while bad behavior is "corrected." This technique rewards not only good answers but also good reasoning processes that increase efficiency, AWS said. The models will remember which behaviors they should apply over time. Reinforcement learning has been shown to be very effective, but the challenge has always been its implementation. Traditionally, it required a complex training pipeline and massive compute, and either human experts with the time to sit there and provide feedback, or access to a more powerful AI model that can evaluate each model response. With RFT on Amazon Bedrock, reinforcement learning has been made a whole lot easier, so it can be accessed by any developer, AWS said. Amazon Bedrock is a fully managed AI platform that provides access to high-performance foundation models from dozens of top AI companies, as well as tools for transforming these models into AI agents and generative AI applications. To undertake reinforcement learning, developers just select the model they want to customize and then point it towards the model's history of interactions or upload a training dataset. Then they select a reward function, which could be rule-based or AI-based, or a ready-to-use template, and the fine tuning process will be entirely automated by Amazon Bedrock. It eliminates the need for extensive machine learning knowledge - all that's required is a "clear sense of what good results look like," the company said. To begin with, Amazon Bedrock RFT only supports Amazon's Nova 2 Lite model, but the company promised to add support for dozens of additional models in the coming weeks. A similar update is coming to Amazon SageMaker AI, which is a more advanced AI machine learning platform that allows companies to design, develop and deploy their own models and customize them. It's being enhanced now with serverless customization capabilities that promise to accelerate this process dramatically. Developers will be able to access reinforcement learning in Amazon SageMaker AI through an agentic experience, where a dedicated AI agent guides them through the process, or alternatively via a self-guided approach that allows for more extensive control over the customization process. "With the agentic experience, developers describe what they need in natural language and then the agent walks through the entire customization process, from generating synthetic data to evaluation," the company said. Whichever option developers choose, they'll be able to access multiple reinforcement learning techniques, including learning from feedback, learning with verifiable rewards, supervised fine-tuning and direct preference optimization. At launch, the new features are compatible with Amazon's Nova family of models, as well as Llama, Qwen, DeepSeek and GPT-OSS. In a related update, Amazon SageMaker Hyperpod is getting access to a new checkpointless training feature to support more reliable model training experiences. Amazon SageMaker Hyperpod is a service that's designed to automate the infrastructure requirements for AI model training, but in cases where hardware or software failures occur, it's often very slow to recover, taking up to an hour in some cases. With checkpointless training, Amazon SageMaker Hyperpod can now recover automatically from infrastructure faults in a matter of minutes, with zero intervention required by the customer, Amazon said. It works by continuously preserving the model's state across the entire compute cluster as it's being trained, so if any faults occur it can quickly fix them and pick up where it left off, without having to start over. In addition, Amazon announced that it's bringing the open-source AI agent framework Strands Agents to the Typescript programming language. TypeScript is an alternative to JavaScript that's more resistant to errors and bugs. With this update, developers can use the Strands Agents framework to develop their entire agentic stack in the TypeScript language, Amazon said.

'Why not?' At re:Invent, AWS answers with big step into frontier AI model reasoning and agentic services - SiliconANGLE

'Why not?' At re:Invent, AWS answers with big step into frontier AI model reasoning and agentic services Amazon Web Services Inc. envisions a world in which billions of AI agents will be working together. That will take a significant advance in frontier model reasoning, and the company made several major announcements today to make this dream a reality. "Why not?" is the cloud giant's central re:Invent conference tagline this week, and AWS built on the theme by announcing a major expansion of its Nova foundation model platform with the launch of Nova Forge, a "first-of-its-kind" service to train and build frontier AI models. Frontier model reasoning involves advanced problem-solving capabilities for cutting edge AI frameworks, moving from basic information retrieval to problem-solving and logical deduction. While models have advanced rapidly, getting them to comprehend and act while using proprietary data has been a challenge. Nova Forge is designed to address one of the biggest complaints among enterprises using AI. "Almost every customer I talk to wishes they could teach a model to understand their data," AWS Chief Executive Matt Garman (pictured) said in his keynote remarks on Tuesday. "Today you just don't have a frontier model that deeply understands your data and your domain." The new service will allow organizations to build optimized variants of Nova by blending proprietary data with Nova's frontier capabilities. The goal is a customized model that combines the knowledge and reasoning power of Nova's engine with a deeper understanding of each organization's specific business. "AI's ability to understand your company data is what delivers huge value," Garman said. "The wizardry comes when you can deeply integrate a model with your intellectual property." Garman noted that AWS already has several notable customers who have implemented Nova Forge. These include the online conversation platform Reddit Inc., which brought its own domain data into Forge's pretraining process. As Garman explained in an exclusive video conversation with SiliconANGLE prior to the conference, Reddit is using Nova to read context, reduce false positives and scale to millions of communities without increasing engineering complexity. In addition to Forge, Amazon's Nova platform is also being expanded with "Lite" and "Pro" reasoning models, along with "Sonic," a speech-to-speech model that can unify text and speech understanding to generate real-time, human-like conversational AI. A significant portion of Amazon's releases this week have involved new capabilities for AI agents. This is understandable, since "the next 80% to 90% of enterprise AI value will come from agents," as Garman told SiliconANGLE. The cloud giant announced Policy in Amazon Bedrock AgentCore, designed to actively block unauthorized actions through real-time, deterministic controls that operate outside of the agent code. By setting clear boundaries, organizations can clamp down on unauthorized data access, inappropriate interactions and system-level mistakes. "This is a little bit like raising a teenager," Garman told the re:Invent gathering. "You really want to be able to control specific actions with those tools." AWS also launched AgentCore Evaluations to gain visibility into agent behavior and results. The offering simplifies the complicated processes and infrastructure previously required to ensure agentic quality. Developers will also have the ability to write custom evaluators using preferred large language models and prompts. AWS is launching a new class of frontier agents, billed as an "extension" of an organization's software development team. The Kiro autonomous agent will maintain persistent context across sessions, learning from a developer's pull requests and feedback. Kiro will also independently figure out the best process for work completion, sharing changes so the user stays in control of what gets incorporated. "It runs alongside your workflow, maintaining context," Garman explained. "It actually learns how you like to work." Frontier agents were also unveiled today to incorporate security expertise throughout the development cycle, and to support DevOps functions such as correlating telemetry, code, and analyzing deployment data to pinpoint root causes of problems and reduce mean time to resolution. The company revealed that its use of DevOps Agent had resulted in an estimated root cause identification rate of over 86%. As is common with the opening keynote at AWS re:Invent, the company packed as many releases as it could into a two-and-a-half-hour window. As he was nearing the end of his remarks, Garman took it upon himself to deliver 25 additional announcements, mostly dealing with new instances and database enhancements, in 10 minutes, which may be something of a tech conference record. "The true value of AI has not yet been unlocked," Garman said. "It's turning from a technical wonder into something that delivers real value. Dive deep into the details and start inventing."

Exclusive: AWS CEO Matt Garman declares a new era: Agents are the new cloud - SiliconANGLE

Exclusive: AWS CEO Matt Garman declares a new era: Agents are the new cloud After 13 years covering Amazon Web Services Inc. up close -- including watching more than a decade of reinvention from the front row -- I've learned to feel when the ground is shifting. And this year, that feeling is unmistakable. AWS re:Invent 2025 opens with a charge in the air as more than 60,000 attendees descend on Las Vegas -- developers, founders, Fortune 100 chief information officers, national security leaders and hyperscale cloud architects. But as the headlines break, most people will still be left wondering what's really moving under the surface. That deeper story came through in my exclusive recent conversations in Seattle with AWS Chief Executive Matt Garman, Chief Marketing Officer Julie White and other senior AWS leaders. Combined with SiliconANGLE and theCUBE reporting and data from our wired community insiders, a clearer picture emerges: AWS is declaring the true arrival of agentic artificial intelligence -- and rebuilding the cloud from the silicon up. While most of the world remains fixated on generative AI, AWS is already moving past it in what it describes as a real, enterprise-oriented way -- not just buzzwords. Garman was blunt: "The next 80% to 90% of enterprise AI value will come from agents," he told me. These aren't chatbots or copilots. They are autonomous, long-running, massively scalable digital workers -- agents that operate for hours or days, learn organizational preferences and collaborate in swarms. This vision is anchored in a full-stack architecture: custom silicon, new model families, sovereign AI factories and an agent runtime built to eliminate the heavy lifting that has slowed enterprise adoption. AWS believes the agent era will be every bit as transformative as the cloud era was in 2006 -- and they're engineering for that scale today. One of the clearest signals of this shift is AWS' formal embrace of AI Factories, its new sovereign-scale infrastructure footprint -- a concept I've been talking about on theCUBE Pod for over a year. In those conversations, I argued that AI would evolve beyond traditional cloud regions and edge stacks into campus-scale, high-density AI systems purpose-built to turn enterprise data estates into continuous intelligence engines. Garman and AWS essentially validated that view, describing AI Factories as "highly opinionated AWS-managed AI systems" deployed directly inside customer data centers. These are not edge appliances or Outposts-style racks. They are full-blown AI campuses -- the same architectural pattern behind Project Rainier, the 500,000-Trainium2 build with Anthropic. As Garman put it: 'The campus is the new computer." For a select group of customers -- sovereign nations, defense agencies and hyperscale enterprises -- AI Factories deliver cloud-consistent services entirely on their own turf. Everyone else consumes the same architecture from AWS regions. It's exactly the industrialization trend I've been forecasting: AI isn't just a service anymore -- it's an infrastructure category, and AWS is now manufacturing it at global scale. Garman is also straight up about the addressable market for these massive builds. As he told me, "99.999% of customers will never purchase an AI factory." In my conversations with Fortune 100 CIOs and chief technology officers over the past year, this has been the recurring theme: Enterprises aren't struggling because they lack giant infrastructure -- they're struggling because they lack practical, production-ready paths to adopt AI at scale. The bottlenecks have been governance, identity, security, data quality, model drift and the sheer operational burden of stitching together immature tools. I've said repeatedly on theCUBE that the enterprise AI stall wasn't about GPU scarcity -- it was about plumbing scarcity. Garman's point reinforces that: The vast majority of enterprises don't need sovereign-scale clusters; what they need is cloud-delivered factory patterns that abstract away complexity and let them plug into hardened infrastructure without reinventing it. Only a very small cohort -- sovereign nations, U.S. government and sensitive agencies, and a handful of the largest global enterprises -- requires fully dedicated on-premises AI Factories. For everyone else, the "factory" shows up through regional AWS services, Trainium-powered clusters, Bedrock, Nova Forge and AgentCore. And that's exactly what enterprises have been asking for: the ability to access industrial-grade AI without industrial-grade buildouts. At the silicon layer, AWS has tightened its vertical integration. Trainium3 is now generally available, packaged as a two-rack "Ultra Server" that Garman called "the most powerful AI system available today." It's optimized for both training and for the increasingly heavy inference loads enterprises are putting into production. Garman was matter-of-fact about the impact: More than 50% of Bedrock tokens already run on Trainium -- a meaningful performance and cost moat for AWS. Then came the next reveal: Trainium4, preannounced with an expected an eightfold compute increase over Trainium3 and significantly higher memory bandwidth. Combining it with 3.8 gigawatts of new data-center power added over the past year, AWS is signaling that it intends to dominate the cost-performance race for frontier inference. On the edge-versus-cloud debate, Garman's view is decisive: Heavy intelligence centralizes to the cloud. The edge is compute- and power-constrained and best suited for lightweight inference (think wake-word detection on Alexa). The data breadth, model variety and power required for real capability live in the cloud. The emerging pattern, he says, looks like an application programming interface call to smarter, larger systems, not full frontier models at the edge. Silicon is only half the story. AWS also unveiled the Nova 2 model family -- Lite, Pro and higher-performing variants -- covering high-volume reasoning, real-time speech-to-speech and other demanding workloads. Early benchmarks, according to Garman, place them head-to-head with Claude 3.5, GPT-4.5 and Gemini Flash. The deeper breakthrough is Nova Forge, a system Garman described as the first true open-training pipeline for enterprise frontier models. Fine-tuning has been the ceiling for most enterprises. Forge blows past that limit by letting companies inject proprietary data early in the training process -- at checkpoints inside Nova -- performing co-training alongside Amazon's curated datasets. Customers insert their proprietary data, train within their own VPC, and can run the resulting model serverlessly on Bedrock -- without the model or customer data flowing back to the Nova team. The output is a private, frontier-grade model that deeply understands the company's data and never leaves their boundary. Garman's blunt view: "Generic tokens are useless unless they know your business." Reddit is already demonstrating the impact. By bringing its own domain data into Nova Forge's pretraining process -- w ithout additional fine-tuning -- Reddit achieved a kind of "social intuition" generic systems miss: It reads context, reduces false positives, flags real threats and scales to millions of communities without scaling engineering complexity. Banks, pharma giants and large manufacturers are lining up for the same capability. The economics are compelling: Instead of spending $50 million to $100 million to train a frontier model from scratch, enterprises can create a domain-specific frontier model for a small fraction of that -- and even distill smaller models from it. In short, Forge delivers frontier capability without frontier cost. If Nova Forge is the new model substrate, AgentCore is the new runtime. White explained that it solves the biggest enterprise blocker of the past year: Teams were spending months reinventing repetitive foundational systems -- identity, policy, security, memory, observability, drift detection -- just to make early agents safe and deployable. AgentCore is composable. Teams can mix and match secure compute, memory and Agent Observability, and pair them with models from Nova, Anthropic, OpenAI, Meta Platforms' Llama, Qwen or Google Gemini -- or open source -- without re-platforming. It's the identity-and-access-management moment for agents, moving from prototypes to production in regulated workflows and mission-critical operations. AWS' Kiro -- the integrated development environment and the command-line interface -- helped pioneer agentic development: not just code completion or "vibe coding," but directing agents to perform long-running tasks and collaborate as a swarm. At re:Invent, AWS introduced the Kiro Frontier Agent -- a long-running agent that works like a team of junior engineers operating overnight, with instructions and autonomy, and the ability to scale out. With substrate and runtime in place, AWS introduced Frontier Agents -- long-running, autonomous agents that can work for hours or weeks, scale both up and out, and learn team preferences over time. Beyond engineering, the first wave includes cloud operations, security and penetration-testing agents -- systems that triage incidents, probe defenses and enforce policy in production. "Three to six months in," Garman told me, "these agents behave like part of your team. They know your naming conventions, your repos, your patterns." The constraint is no longer developer hours -- it's imagination and prioritization. Stepping back, the pieces form AWS' most coherent AI platform strategy yet. The flow works like this: Infrastructure → silicon → models → custom frontier training → agent runtime → frontier agents → enterprise workflows This is not a bundle of features. It is a systematic rearchitecture of the cloud for a world where billions of agents operate across industries. AWS is leaning heavily into capital spending: silicon, power, networking, sovereign footprints and global supply chain scale. This is a multiyear expansion cycle, not a bubble. If you cover this industry long enough, you learn that bubbles form when capital chases stories, not systems. What's happening now is the opposite. The spend is going into power, silicon, networking ,intelligent interconnections, sovereign large scale computing footprints, fast-emerging smart AI edge factories and full-stack platforms that unlock measurable productivity. This massive innovation wave is rooted in infrastructure, not narratives, and the companies leaning in understand that AI is no longer optional -- it's a societal revolution that can yield competitive advantage with compounding returns. On talk of an "AI bubble," Garman shrugs. Customers buy where ROI shows up in the P&L. With 3.8 gigawatts of new data-center power landed in the past year and demand "across the stack," he's betting enterprise value -- not hype -- sustains the buildout. In 2006, AWS changed the world by abstracting servers and letting developers build without friction. Today's shift is analogous -- only larger. With AI Factories, Trainium, Nova Forge, AgentCore, Kiro and Frontier Agents, AWS is rebuilding the cloud around agentic systems that can learn, reason and act. While other outlets will chase this week's headlines, our reporting makes one thing clear: AWS is not chasing the AI trend. It is industrializing it.

AWS puts AI agents to work on truly autonomous software development - SiliconANGLE

AWS puts AI agents to work on truly autonomous software development Amazon Web Services Inc. is using its annual AWS re:Invent 2025 extravaganza this week in Las Vegas to show how it's putting artificial intelligence agents to work in enterprise environments. The cloud giant is moving on from the days of passive generative AI chatbots, building highly autonomous and massively scalable digital workers that can collaborate with humans and each other, and work under their own steam, potentially for days on end, with only minimal supervision. At the core of this vision is Amazon's full-stack architecture, including its powerful new Nova large language models, its custom Trainium3 AI processors and an agentic runtime that eliminates all of the hassles of getting agents up and running and ensuring they're secure. In an exclusive interview with SiliconANGLE, AWS Chief Executive Matt Garman said autonomous agents are the company's top priority in terms of AI development. "The next 80% to 90% of enterprise AI value will come from agents," he said. Amazon's agentic push will be led by a newer, more sophisticated class of AI agents that it calls "frontier agents," which promise to deliver a "step-function change" in what digital workers can achieve. Rather than just assisting human workers like today's AI coding assistants, for example, they can undertake complex projects independently, in the same way as people do. The company is kicking things off with three new frontier agents - Kiro, AWS Security Agent and AWS DevOps Agent, each one focused on a different aspect of the software development lifecycle. While AI coding tools have already performed miracles in terms of accelerating productivity, they have also created a lot of friction for human developers. These days, many coders find themselves having to act as the thread that holds multiple AI agents together, constantly rebuilding context when switching tasks, manually coordinating cross-repository changes and stitching together information from different tickets, pull requests and chats so they can properly guide them. In effect, it means developers have become coding agent overseers, but that prevents them from focusing on more creative priorities, which was the whole point of AI automation in the first place. With Kiro, AWS is helping developers eliminate this fraction and ensure that agentic processes keep moving along nicely without constant human oversight. As a truly autonomous agent, Kiro is uniquely able to maintain persistent context across sessions and will continuously learn from, and remember, pull requests and human feedback, so it gets better over time. It's able to handle multiple tasks at once, ranging from triaging bugs to improving code coverage, across multiple code repositories. Users can ask it questions, describe the task they want it to do in natural language, or just tell it to work through the tasks in their GitHub backlog, and it will do so all by itself, figuring out what it needs to do without any human input, Amazon said. Changes will be shared as proposed edits and pull requests, ensuring the developer maintains overall control. While Kiro handles the grunt work, the AWS Security Agent is all about oversight, proactively identifying risks and taking steps to mitigate them once an issue has been identified. Whereas existing security agents only provide generic recommendations, AWS Security Agent offers tailored guidance throughout the software development lifecycle and can perform comprehensive testing at any stage. The agent works by proactively reviewing design documents, scanning pull requests and comparing these with organizational security rules and its list of common vulnerabilities. All the user has to do is define their security standards once, and the agent will automatically validate them across every application that's hosted on AWS. In addition, it can also perform penetration testing on demand so security issues don't hold back development velocity, Amazon said. It validates any problems it finds and then generates remediations to fix those issues, before applying them once developers give it the go ahead to do so. The final piece of the puzzle, AWS DevOps Agent works to maintain the underlying infrastructure that distributed applications depend on - monitoring microservices, cloud dependencies and telemetry to gain a comprehensive understanding of its behavior. The company said AWS DevOps Agent will be on call 24/7, ensuring it's ready to respond instantly the moment any incidents occur. It draws on its knowledge of the customer's applications and its relationship with the various infrastructure components to identify the root cause of any system failure or performance issues quickly, employing observability tools such as Amazon CloudWatch, Dynatrace, Datadog, New Relic and Splunk. By mapping each application's resources with its telemetry, code, and deployment data, it can rapidly pinpoint root causes and accelerate resolution times, while delivering fewer false alerts, Amazon said. With AWS DevOps Agent, teams can also move away from reactive firefighting and became much more proactive, using it to analyze the cause of historical incidents and prevent them from recurring. By learning from these experiences, it can offer targeted recommendations to enhance observability, infrastructure optimization, deployment pipeline enhancement and application resilience, the company promised. Garman said all three frontier agents can be set up once and then be left to work for weeks on end, scaling up or out as required. As they do this, they will learn each customer's preferences over time, improving their performance the more they're used. "Three to six months in," Garman said, "these agents behave like part of your team. They know your naming conventions, your repos, your patterns." Over time, Amazon expects these frontier agents to help organizations shift to an "agentic culture" where AI is no longer just an assistant, but an extension of their human teams. Ultimately, it sees its frontier agents delivering outcomes autonomously throughout every step of the development lifecycle, transforming how software is built, secured and operated.