The Rise of AI Agents: Transforming Business Automation and Decision-Making

Finding value from AI agents from day one

Although still so early in its development that there lacks consensus on a single, shared definition, agentic AI refers loosely to a suite of AI systems capable of connected and autonomous decision-making with zero or limited human intervention. In scenarios where traditional AI typically requires explicit prompts or instructions for each step, agentic AI will independently execute tasks, learning and adapting to its environment to refine decisions over time. From assuming oversight for complex workflows, such as procurement or recruitment, to carrying out proactive cybersecurity checks or automating support, enterprises are abuzz at the potential use cases for agentic AI. According to one Capgemini survey, 50% of business executives are set to invest in and implement AI agents in their organizations in 2025, up from just 10% currently. Gartner has also forecast that 33% of enterprise software applications will incorporate agentic AI by 2028. For context, in 2024 that proportion was less than 1%. "It's creating such a buzz - software enthusiasts seeing the possibilities unlocked by LLMs, venture capitalists wanting to find the next big thing, companies trying to find the 'killer app," says Matt McLarty, chief technology officer at Boomi. But, he adds, "right now organizations are struggling to get out of the starting blocks." The challenge is that many organizations are so caught up in the excitement that they risk attempting to run before they can walk when it comes to deployment of agentic AI, believes McLarty. And in so doing they risk turning it from potential business breakthrough into a source of cost, complexity, and confusion. The heady capabilities of agentic AI have created understandable temptation for senior business leaders to rush in, acting on impulse rather than insight risks turning the technology into a solution in search of a problem, points out McLarty. It's a scenario that's unfolded with previous technologies. The decoupling of Blockchain from Bitcoin in 2014 paved the way for a Blockchain 2.0 boom in which organizations rushed to explore the applications for a digital, decentralized ledger beyond currency. But a decade on, the technology has fallen far short of forecasts at the time, dogged by technology limitations and obfuscated use cases. "I do see Blockchain as a cautionary tale," says McLarty. "The hype and ultimate lack of adoption is definitely a path the agentic AI movement should avoid." He explains, "The problem with Blockchain is that people struggle to find use cases where it applies as a solution, and even when they find the use cases, there is often a simpler and cheaper solution," he adds. "I think agentic AI can do things no other solution can, in terms of contextual reasoning and dynamic execution. But as technologists, we get so excited about the technology, sometimes we lose sight of the business problem." Instead of diving in headfirst, McLarty advocates for an iterative attitude toward applications of agentic AI, targeting "low-hanging fruit" and incremental use cases. This includes focusing investment on the worker agents that are set to make up the components of more sophisticated, multi-agent agentic systems further down the road.

AI Agents: the next big phase of artificial intelligence

The challenges and opportunities of scaling AI agents for large-scale automation Artificial intelligence (AI) has entered a new phase of its evolution - one where models do not just reason but also act. Welcome to the age of AI agents: where systems can independently execute complex tasks, collaborate with other agents, and operate autonomously at scale. This shift is poised to unlock transformative gains in productivity and efficiency across every industry. Traditionally, AI interactions have centered around a single, often large, model designed to perform a variety of tasks. However, with AI agents, this is changing. Instead of relying on one massive model to handle everything from start to finish, AI agents break down tasks into smaller, specialized components, each handled by different agents. Compare this to moving from a single craftsman to an intelligent network of specialist workers, making AI more specialized and efficient. For example, today, if someone asked an AI to design a new computer chip, the task would be processed end-to-end by one model. In the world of AI agents, that same request would be divided among a network of agents - each responsible for specific aspects like layout, simulation, and optimization - working together to deliver the result faster and more intelligently. Beyond responding to specific requests and tasks, the impact of AI agents will be transformative. They are set to drive large-scale automation, bringing greater adaptability, intelligence and autonomy to processes that were previously manual or considered to be inefficient. At the same time, AI agents are set to reshape workplace operations and practices, by enhancing how repetitive tasks like document management, customer support, and workflow orchestration are handled. The pivot towards AI agents is also set to influence AI investment strategies. The Arm AI Readiness Index report reveals that 80 percent of organizations surveyed have an AI budget, with 87 percent expecting it to grow. Businesses are increasingly prioritizing AI tools and platforms that support modular, scalable agent ecosystems. The impact of AI agents will be widespread and cross-industry. Sectors like finance, insurance, healthcare, retail, logistics, and creative services are already exploring a variety of use cases where AI agents can be adopted, ranging from fraud detection to automated underwriting, and even content creation. The potential is staggering. Moreover, AI agents will not be confined to one environment, with workloads covering a wide range of systems. In mobile, imagine saying "book me a flight" or "sort my photos," and having a local network of AI agents coordinate these requests seamlessly. AI-first wearables may soon allow us to blend the physical and virtual worlds by using agentic AI to reason, predict, assist, and adapt. For example, you may glance at a flower, asking "what flower am I looking at?" -- and your smart glasses will instantly identify it, offering care tips or fun facts. Even virtual assistants in the home could use AI agents to control devices and complete everyday household tasks more efficiently. On a larger scale, future autonomous vehicles could deploy multiple AI agents to handle various workloads, like navigation, object detection, real-time decision-making, and passenger interactions. Meanwhile, in cloud or enterprise settings, AI agents will power next-generation customer service and decision-making systems for improved responses. A key enabler of AI agents is the rise of smaller AI models. These are easier to customize for specific tasks, more power-efficient to run, and faster to deploy across distributed systems. By using a collection of smaller models rather than one giant model, businesses can optimize both the performance and power-efficiency that are critical for everything from mobile devices to datacenters. In fact, as explained in the Arm Silicon Reimagined report, many of these smaller models are already providing great results in terms of AI capabilities and performance, while running entirely on the device. AI agents represent more than just the next evolution of AI - they signal a fundamental shift in how work gets done, decisions are made, and value is created. Autonomous, task-driven systems powered by AI agents have the potential to enhance productivity, streamline operations, and enable entirely new customer experiences. By moving beyond standalone AI models to networks of multiple specialized AI agents, organizations in any industry can unlock faster, smarter, and more cost-effective ways of operating across every function. As AI agents become more capable, collaborative, and context-aware, they will redefine our expectations of technology - not simply as tools, but as proactive, intelligent collaborators. The organizations that embrace this shift early will not only boost efficiency, but also uncover new opportunities for innovation, differentiation, and growth in this new AI world. We list the best business cloud storage.

Building Context-Aware AI Agents

Join the DZone community and get the full member experience. Join For Free Artificial intelligence is on a rapid evolutionary track, and the once awe-inspiring conversational capabilities of ChatGPT raise very few eyebrows these days. AI developers are shifting into a higher gear, and these days, the focus is all about agents. They're building more advanced AI systems that transform large language models into thinkers, decision-makers, and action-takers, which can automate many kinds of work. To create an AI agent, the developer must assign an LLM to a specific role, assign it a clear goal to accomplish, and provide access to the necessary resources for the agent to fulfill its mission. When AI agents are focused on a clearly defined objective and can utilise APIs, web browsers, search engines, and databases as humans do, they can autonomously determine how to perform the assigned task. Agentic AI represents an entirely new paradigm for developers, enabling multiple agents to collaborate on complex, multistep tasks and redefine the nature of business automation. How Did We Get Here? One of the most important capabilities for AI agents is their ability to understand context. LLMs can be taught to remember what was said earlier during a conversation or in previous sessions, and they can take this into account when it comes to making decisions, without any changes made to their underlying code. This in-context learning is what enables LLMs to adapt and respond more effectively to complex queries. AI agents are further enhanced by retrieval-augmented generation (RAG), which is a popular technique that enables LLMs to augment their knowledge with data from dynamic sources beyond their initial training sets. This is what makes it possible to customize an LLM's responses for a given context, such as providing customer service support for a specific organization. A more recent development is multimodal models, or MLLMs, which enable AI agents to explore and navigate through a graphical user interface. MLLMs combine the capabilities of LLMs, which perform well at tasks involving natural language processing but struggle when it comes to processing visual elements, and large vision models or LVMs, which excel at processing visuals but do not possess the advanced reasoning skills of traditional LLMs. By blending an LVM's visual processing with an LLM's reasoning, MLLMs can analyze and understand both text and images. Navigating the Web A key skill for any AI agent is the ability to explore, understand, and take actions online, which means developers need to teach it how to surf the web using a browser. Browser Use One of the most popular tools for this is Browser Use, an open-source framework that helps to make the internet "readable" to AI agents. Browser Use enables agents to go beyond their visual recognition capabilities by breaking down each website into a structured text. Once this is accomplished, AI agents can process what they're seeing online in a more deterministic way, including dynamic, embedded web elements that computer vision-based agents might miss. This means it can understand all of the options available on a specific web page and identify what it needs to do. Scraping Browser AI agents also need a specialized browser that allows them to navigate the web at scale, avoiding the various pitfalls set up by web publishers to try and prevent automated bots from navigating through them and importing their data. With Bright Data's Scraping Browser, AI agents gain access to a variety of tools that can help them to do this at an unprecedented scale. With unlimited concurrent sessions, thousands of agents can explore the web continuously, thanks to API and script management integrations that provide granular control. It also offers a range of mechanisms for getting around the bot-blocking tools implemented by sites such as Amazon and Facebook that aim to curtail autonomous traffic. These include browser fingerprinting, automated retries, advanced Captcha solvers, and a library of more than 150 million proxy IP addresses. Sequential Task Execution Now that our AI agents are set up to explore the web, the next step for developers is to teach them to execute tasks sequentially, in a logical order, so they can undertake complex work involving multiple steps. When AI agents are tasked with gathering context from multiple sources and reasoning across them, they often struggle. Some examples of this might include adaptive surveys, which require an agent to perform sentiment analysis in real-time and then ask follow-up questions. Similarly, tasks such as supplier risk assessment, customer churn analysis, and forecasting bottlenecks in manufacturing operations involve pulling data from multiple domains. Agentic Teams To address this, developers must devise a method for unifying input data and integrating it so that their AI agents can gain a comprehensive understanding of the information they're drawing on. The easiest way to do this is to employ teams of specialized AI agents that are each trained to understand or work on a specific domain or task. By using Crew AI's open-source agentic AI framework, developers can quickly spin up a team of AI agents that can collaborate to perform multi-step tasks. These agentic teams will split a task between them, with each one focusing on whatever aspect falls within its capabilities, leaving the other tasks to an agent that's better suited for it. Once their work is complete, they'll combine the results. Standardized Interactions These AI agent teams may require access to a range of different software tools to complete their assigned tasks, which is where the Model Context Protocol comes into play. The open-source MCP is rapidly emerging as the de facto way for AI agents to interact with software, APIs, and services because of the way it standardizes context sharing and action execution, allowing those agents to operate in dynamic, multi-tool environments. MCP provides AI agents with structured access to almost any API, data source, or tool, enabling natural and flexible workflows within applications while reducing the custom logic required for integration. Just as APIs transformed the way software communicates, MCP is set to become a universal language for agent-tool interaction, providing support for chaining tools across domains to enable more powerful compound actions. Cross-Domain Context We'll also need a semantic layer to link the information found in structured datasets with the live, unstructured data that's sourced from the internet. Wren AI offers a powerful semantic layer that helps developers to standardize cross-domain data, which is often stored in incompatible formats, so it can be amalgamated and interpreted consistently by AI agents. Crucially, it provides the business context that agents need to work with structured enterprise data, so it can be tagged and aligned with web-based data to create comprehensive knowledge graphs. By mapping different cross-domain entities in this way using a knowledge graph, AI agents can more accurately identify context-based relationships between them. Armed with this ability to execute sequential tasks, developers will be able to create AI agents that can generate more relevant cross-domain insights by contextualizing external, web-based data against internal metrics. For instance, an AI agent might be able to connect an external news story regarding supply shortages to update the risk score in an organization's internal procurement system, taking into account the company's existing stocks, the expected duration of the shortage, and the ability to source alternatives from different suppliers. Automation at Unprecedented Scales AI agents represent a dramatic evolution of LLMs, which have transformed from providing simple, grounded responses based on their pre-trained data into intelligent entities that can actively explore and interact with their environments and complete assigned tasks. When developers combine data and web exploration with logical reasoning and decision-making, AI agents can perform more complicated, multi-step tasks with greater autonomy and accuracy. It will usher in a new era of more robust and flexible task automation by LLMs with almost human-level understanding and problem-solving skills. AI agents are becoming much more "human" in terms of what they can do, and we're only just beginning to realize the possibilities this will unlock for enterprise acceleration.