The Rise of Agentic AI: Transforming Enterprise Automation and Cybersecurity

Could agentic AI save us from the cybercrisis?

Sponsored feature The cyberthreat landscape is evolving fast, with highly organized bad actors launching ever more devastating and sophisticated attacks against often ill-prepared targets. In this worrying scenario, the time taken to discover and respond to an attack has become critical. Providers of managed detection and response (MDR) services, whose job it is to protect their customers against this malicious onslaught, now have a potent new weapon they can build into their platforms: agentic AI. While generative AI relies on prompts for simple tasks, agentic AI breaks down complex tasks into simpler ones that it can then complete autonomously. A set of such agents will often work together, using its own specialized training to carry out parts of a more complex task. It looks set to be a game changer, with Gartner forecasting a third of AI use cases will use agentic AI to fulfill their role by 2028. Agentic AI is already recasting the entire MDR market, taking it beyond generative Ai models and towards something much more autonomous. MDR providers can use various forms of agentic AI to speed up the kinds of tasks traditionally handled by human security operations center (SOC) analysts. This kind of automation has the power not only to accelerate detection and response, but to bypass human error. It promises to help address the skills shortage affecting the market for experienced security professionals. Powered by agentic AI, so the theory goes, MDR platforms should continuously adapt and learn from real-time threats. They will provide more potent responses and prompt remediation to contain cyber attacks well before they can disrupt essential operations. But there's a catch that senior security professionals need to be alert to before they appoint an MDR provider. Not all are using agentic AI in the same way (and some are not using it at all). So customers must take great care when navigating a crowded market for the best choice of partner. Organizations need to understand how a potential security partner is using agentic AI, how they measure the outcomes from this technology, and how transparent and collaborative they are in sharing these insights. Are they using the sort of basic AI that does little more than filter out possible threats from a sea of data? Or more sophisticated AI models that build reasoning and multi-stage task solving into the mix? And how do they use human experience alongside their AI deployment ? To better understand how the market should be navigated, The Register spoke to Dustin Hillard, CTO of threat detection and response specialist eSentire. An experienced data scientist, Hillard has spent the past 15 years focused on automating security and understanding network behavior through machine learning. He points out that some MDR providers position agentic AI automating all of the duties of a regular SOC analyst, eliminating humans from the process altogether. The danger here, he says, is that such agents might fail to classify cyber activity appropriately, causing false positives and negatives that add to a security team's workload rather than reducing it. That might create unfortunate consequences that an experienced human security analyst could avoid. Not all MDR providers are equally transparent when it comes to sharing insights, either. "Many MDR providers are talking about how they can take away the human role," he warns. "But at eSentire we are trying to take the expertise of humans and use AI to amplify it. Humans are still in the loop and making the critical decisions , and we argue that should always be the case ." Agentic AI's potential value is indisputable, especially when combined with human experience. "Within minutes of a security signal being received by our SOC, our agentic AI system, Atlas AI, kicks off a pre-investigation. While the investigation would take human SOC analysts at least five hours on their own, Atlas AI takes seven minutes" he notes. "It can assess the situation and collect all the essential data before putting it in front of an analyst for the final call, whether to escalate the investigation or close it out." eSentire's approach to using AI in its SOC operations is based around an agentic workflow that mimics a SOC analyst's investigation process. Its Atlas AI engine poses security questions, pulls research, and compiles the findings into a report. This can help determine whether an employee's computer has been compromised or not and it uses a confidence score from one to 10. It then shares that report and all the pertinent data within the analysts. "eSentire's SOC analysts always have the final say as to whether a threat is truly a threat, and they will decide on next steps no matter the report's assessment," explains Hillard. "Atlas AI means our analysts are faster out of the investigation starting blocks, so they can validate threats with confidence, and act in record time," he adds. "We track our first host isolation rate as a measure of how successful our service is at protecting customers. A 99.3% first-host isolation rate prevents lateral spread of a threat with minimal delay." eSentire's approach to using AI for detection and response stems from years of experience. In 2018 it acquired leading AI solution developer Versive, along with Hillard, who was its CTO. It integrated Versive's innovative IP throughout its Atlas XDR platform and SOCs. eSentire continued to build out its AI capabilities, launching the Atlas AI Investigator in 2023, an AI powered tool that provides access to investigation, response and remediation tools through simple natural language interaction. "Our approach is different from others," Hillard says. "The first layer of our agentic AI is a data mesh that collects information from all our customers' environments and brings all of this rich data into one place. This allows our human agents to learn from that data in a scalable fashion." The next layer handles orchestration, using telemetry tools. Customers can tweak the automation dial here to involve human analysts as much as they like. The agent combines telemetry and threat intelligence data. Then it's about having a thorough understanding of the customer's environment, preferences, and business practices. The agentic system uses an LLM to assess the security situation and present hypotheses. If Atlas AI determines the investigation needs to be escalated, it will suggest remediation and containment actions in the context of all the information that's been gathered. Human agents can decide whether to escalate the investigation or close it out. The speedy and accurate results that agentic AI enables mean that an organization can demonstrate full compliance with data regulation requirements in the immediate aftermath of an incident. Decision trails, mobile oversight and compliance alignment with standards like SOC 2 and GDPR help keep SOCs audit-ready. It is possible to produce a detailed report for regulatory scrutiny based on data from the very early stages of an attack.

The enterprise AI paradox: why smarter models alone aren't the answer

Despite the hype, most AI projects are still stuck in the sandbox In boardrooms and investor meetings, artificial intelligence is now table stakes. AI tools are everywhere. Analysts are forecasting trillions in potential value. McKinsey estimates that generative AI alone could boost the global economy by up to $4.4 trillion a year. And yet, in the enterprise? Something's not clicking. Despite the hype, most AI projects are still stuck in the sandbox; demo-ready, not decision-ready. The issue isn't model performance. It's operationalization. Call it the Enterprise AI Paradox: the more advanced the model, the harder it is to deploy, trust, and govern inside real-world business systems. At the heart of this paradox, McKinsey argues, lies a misalignment between how AI has been adopted and how it generates value. Horizontal use cases, notably tools like Microsoft's Copilot or Google's Workspace AI, proliferate rapidly because they're easy to plug in and intuitive to use. They provide general assistance, they summarize emails, draft notes, simplify meetings, and so on. Yet these horizontal applications scatter their value thinly, spreading incremental productivity improvements so broadly that the total impact fades into insignificance. As the McKinsey report puts it, these applications deliver "diffuse, hard-to-measure gains." In sharp contrast, vertical applications (those baked into core business functions) carry the promise of significant value but struggle profoundly to scale. Less than 10 percent of these targeted deployments ever graduate beyond pilot phases, trapped behind technological complexity, organizational inertia, and a lack of mature solutions. LLMs are extraordinary. But they're not enough. The real enterprise challenge isn't building a big, clever model. It's orchestrating intelligence, across systems, teams, and decisions. The world's most innovative companies don't want a single mega-model spitting out answers from a black box. They want a system that's intelligent across the board: data flowing from hundreds of sources, automated agents taking action, results being validated, and everything feeding back into an improved loop. That's not one model. That's many. Talking to each other. Acting with autonomy. And constantly learning from a dynamic environment. This is the future of enterprise AI, and it's what's known as agentic. Agentic AI systems are different from monolithic LLMs in one key way: they think and act like a team. Each agent is a specialist, trained on a narrow domain, given a clear role, and capable of working with other agents to complete complex tasks. One might handle user intent. Another interfaces with an internal database. A third enforces compliance. They can run asynchronously, reason over real-time data, and retrain independently. Think of it like microservices, but for cognition. Unlike traditional generative AI, which remains largely reactive (waiting passively for human prompting) agents introduce something entirely different. "AI agents mark a major evolution in enterprise AI - extending gen AI from reactive content generation to autonomous, goal-driven execution," McKinsey researchers explain. This isn't some speculative vision from a Stanford whitepaper. It's already happening, in advanced enterprise labs, in the open-source community, and in early production systems that treat AI not as a product, but as a process. It's AI moving from intelligence-as-an-output to intelligence-as-infrastructure. If agentic systems are the answer, why aren't more enterprises deploying them? Because most AI infrastructure still assumes a batch world. Systems were designed for analytics, not autonomy. They rely on periodic data snapshots, siloed memory, and brittle pipelines. They weren't built for real-time decision-making, let alone a swarm of AI agents operating simultaneously across business functions. To make agentic AI work, enterprises need three things: Live data access - Agents must act on the most current information available Shared memory - So knowledge compounds, and agents learn from one another Auditability and trust - Especially in regulated environments where AI decisions must be traced, explained, and governed This isn't just a technology problem, it's actually an architectural one. And solving it will define the next wave of AI leaders. Enterprise AI isn't about making better predictions. It's about delivering better outcomes. To do that, companies must move beyond models and start thinking in systems. Not static models behind APIs, but living, dynamic intelligence networks: contextual, composable, and accountable. The Agentic Mesh, as McKinsey calls it, is coming. And it won't just power next-gen applications. It will reshape how decisions are made, who makes them, and what enterprise infrastructure looks like beneath the surface. It isn't simply a set of new tools bolted onto existing systems. Instead, it represents a shift in how organizations conceive, deploy, and manage their AI capabilities. To really make this work, McKinsey says it's time to wrap up all those scattered AI experiments and get serious about what matters most. That means clear priorities, solid guardrails, and picking high-impact "lighthouse" projects that show how it's done. The agentic mesh isn't just a fancy architecture - it's a call for leaders to rethink how the whole enterprise runs. Because real enterprise transformation won't come from scaling a smarter model. It will come from orchestrating a smarter system. We list the best AI chatbot for business.

Smarter networks in the Agentic AI revolution

Agentic AI transforms networks into intelligent, adaptive systems A new era of intelligent automation is underway. We are seeing digital agents take on responsibilities that once required constant human supervision. Today, these systems, can make independent decisions, implement them, and continuously learn from their experiences. Simply put, agentic AI listens, learns, and develops strategies capable of revolutionizing how we work, especially in network operations where it shifts from reactive to proactive, improving resilience and security. It can automate network management, real-time threat detection, and traffic optimization, enhancing efficiency, strengthening security, and boosting network performance for seamless and secure operations. But how can it be implemented, where can it have the biggest benefit, what is the role of human oversight and what lessons can we learn from the introduction of agentic AI? In this article, I'll cover these key points and give advice to businesses looking to harness its potential. The successful implementation of agent-based AI systems requires careful planning. Firstly, it is important to clearly define goals and key performance indicators for their use. Then, a major challenge is the seamless integration of the solutions into the existing IT network infrastructure. Training and operation of the systems also require the availability of sufficient and high-quality data. Finally, there are ethical considerations of implementing agentic AI that companies need to address from the outset, such as data privacy, protection, governance, human oversight and transparency, to ensure trust is built. Agentic AI requires guidelines over which data it can access, from where, and whether it is able to share certain data externally. This is imperative to consider within an AI strategy to ensure that your customers and your organization are protected from data and regulatory breaches, such as the EU AI Act. If your implementation plan takes these considerations into account, nothing stands in the way of the effective use of agentic AI. With digital agents, businesses can streamline their operations, meeting rising customer service expectations. A report by Gartner predicts that by 2029, AI will resolve 80% of common customer service issues without human intervention. These agents analyze customer sentiment in real time and provide tailored responses enhancing customer engagement. Agentic AI is now playing a pivotal role in network infrastructure and cybersecurity, helping organizations move beyond traditional, rule-based systems. Unlike conventional tools that passively monitor and alert, digital agents can actively observe network behavior, identify anomalies in real time, and take autonomous action to resolve emerging threats. This enables a faster response to incidents, reducing downtime, and therefore helps avoid costly disruptions. Agentic AI is already being embedded across networking and security infrastructure to deliver real-time, measurable value. The NSaaS model (Networking and Security as a Service) is evolving into something more dynamic, where agentic capabilities enable smart routing, adaptive policy enforcement, and predictive resource allocation. These enhancements ensure better performance, greater visibility, and stronger protection for global customers operating in complex conditions. There is growing demand for integrated cybersecurity and networking solutions from cloud providers, with many organizations viewing this convergence as essential to enterprise resilience. In this setting, agentic AI offers a unique advantage; it blends machine learning with autonomous decision-making, allowing digital agents to adapt in real time while maintaining stable and efficient network operations. This shift from static systems to intelligent, self-improving agents is reshaping how businesses think about their digital foundations. With this strategic mindset, early adoption of agentic AI gives network providers a chance to get ahead of the curve with smarter services, improved reliability, and a more personalized customer experience. While we are still at the beginning of the AI journey and its potential is yet to be fully realized, McKinsey found that 77% of companies are either using AI or exploring its potential. It has already changed workflows, it still requires a level of human management, but agentic AI enables new possibilities. It can become more than a support tool. It can become an active participant in business operations, freeing up resources and creating greater efficiency. In networking specifically, the benefits are becoming clear. While machine learning has been used for tasks like digital twins and anomaly detection, agentic AI can manage these processes autonomously. This reduces the need for human intervention at every step and enables networks to become more resilient, secure, and adaptive to real-time demands. Nevertheless, learning and development around AI in the workforce remains a business imperative. Counterintuitively, while flawed data is often tolerated in human decision-making, we don't have the same leniency with AI. According to Gartner, 30% of generative AI projects are abandoned after the proof-of-concept stage, primarily due to issues related to data quality, risk management or high costs, highlighting the difficulty organizations face in AI initiatives and importance of getting implementation right from the off. For some companies, agentic AI could mark a shift from promise to performance - where AI becomes not just an experiment, but a business-critical capability aligned to strategic goals. As businesses advance their AI capabilities, agentic systems offer a strategic step forward. They enable organizations to align automation with core objectives, turning networks into adaptive ecosystems. For organizations moving from passive AI and aiming to build intelligent, responsive systems, agentic AI is the key enabler. This is not just a technological shift, but a reimagining of what network infrastructure can achieve when paired with AI designed to think, act, and adapt. We've featured the best AI chatbot for business.

The Age of Agency: why Agentic AI will redefine the future of work

Rethinking how humans and machines co-evolve across businesses We are in the midst of a Copernican shift in enterprise intelligence. We are no longer just automating or augmenting tasks - we are delegating intent. This is not just IT automation; it's agency. And it's changing everything... In this new paradigm, Agentic AI doesn't just support human workers - it collaborates with them, anticipates their needs and acts independently to drive outcomes. It marks a seismic shift in how we think about intelligence at work. Agentic AI is about autonomous execution. It doesn't wait for prompts. It plans, decides and acts -- often without human intervention. This is not just a new toolset; it's a new mindset. Are we prepared to rethink how work is designed, how systems are integrated and how humans and machines co-evolve across the enterprise? Generative AI (GenAI) has captured the public imagination with its ability to generate text, images and code. But it is fundamentally reactive - dependent on human input to produce output. Agentic AI, by contrast, is proactive. It understands goals, decomposes them into tasks, orchestrates tools and adapts its strategy in real time. It's the difference between a brilliant assistant and a self-directed colleague. In 2025, forward-thinking enterprises are no longer choosing between GenAI and Agentic AI - they're combining them. GenAI fuels ideation and content creation. Agentic AI delivers execution at scale. Agentic AI is already transforming how businesses operate. In finance, agents autonomously monitor compliance, flag anomalies and initiate remediation workflows. In manufacturing, they optimize supply chains in real time. They can use the internet, make purchases and approve orders. However, the real revolution is internal. Agentic AI will become the connective tissue of the enterprise - linking systems, surfacing insights and taking action across silos. It's not just about doing more with less. It's about doing what was previously impossible. Agentic AI isn't just accelerating existing workflows - it's reimagining them. In finance, autonomous agents now reconcile transactions in real time, detect anomalies before they escalate and dynamically adjust forecasting models based on live market signals. In HR, agents are transforming talent management by continuously scanning internal and external data to identify skill gaps, recommend personalized learning paths and even initiate retention interventions before attrition risks materialize. Meanwhile, in Sales & Marketing, agents orchestrate hyper-personalized campaigns, adapt messaging based on behavioral signals and autonomously optimize pricing strategies across channels. These aren't incremental improvements - they're structural shifts that collapse cycle times, eliminate friction and unlock entirely new value pools. The result is a more fluid, responsive enterprise where strategy and execution are no longer separated by process drag. The fear that AI will replace humans misses the point. Agentic AI doesn't eliminate human work -- it elevates it. It frees professionals from the tyranny of the inbox and the spreadsheet, allowing them to focus on judgment, emotional intelligence and radical innovation. Imagine an operations lead at a manufacturing firm working with an AI agent that monitors equipment health, predicts maintenance needs and autonomously adjusts production schedules to avoid downtime. Or a commercial strategist at an energy company whose agent tracks global commodity prices, models regulatory impacts and recommends contract renegotiations in real time. These agents aren't waiting for instructions - they're anticipating change, acting on insight and reshaping how decisions are made. This isn't science fiction - it's already happening in the most forward-thinking enterprises. To harness the full potential of Agentic AI, organisations must go beyond deployment. They must design for trust. That means: This is not just a technical challenge - it's a leadership imperative. As Agentic AI becomes embedded in workflows, a new skillset is emerging. Prompt engineering is just the beginning. Professionals must learn to supervise agents, interpret their outputs and orchestrate multi-agent systems. This is the new digital fluency. Business leaders, as well as HR and L&D, must act now to build these capabilities. The future belongs to those who can lead teams of humans and machines. Agentic AI is not just the next wave of automation. It's a new form of intelligence - one that acts with intent. As we enter this new era, the question is not whether we will use Agentic AI, but how we will shape it to reflect our highest aspirations. I tried 70+ best AI tools.

How specialized AI agents unlock accurate enterprise automation

The rapid adoption of AI agents is taking enterprises by storm. According to Deloitte it is predicted that 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept, growing to 50% in 2027. AI agents address two top-priority challenges -- siloed data and fragmented processes. Bestowed with cognitive abilities -- such as learning, problem-solving and decision-making -- AI agents continually adapt and evolve to enhance their performance in automating tasks and executing complex operations. They are being embedded in enterprise processes, executing critical tasks that require reasoning, adaptability, and action. They're able to transform raw, unstructured data into structured information, analyze it and make decisions or recommendations based on learned patterns. However, AI agents aren't standalone entities. AI agents work best when they operate within a network -- handing off tasks to robots, or escalating concerns and complex problems to human team members. This holistic approach is called agentic automation. Agentic automation -- which combines robots, AI agents, and people -- can automate even the longest, most complex processes end-to-end. Working effortlessly across disparate systems, it helps make businesses more autonomous and productive while enhancing the experiences of customers and employees. Amidst excitement around AI agents and agentic automation, a significant question has surfaced for enterprises -- how many agents does a business need? Should organizations adopt one general agent for every process or many specialized agents for specific tasks? Here's the answer. While generative AI is very powerful, it's still nowhere near matching humans' cognitive capabilities when it comes to repeatable tasks. This is, in part, because humans have a great degree of specialization. To best support human decision-makers and leaders, the architecture that works best leverages highly specialized agents that work in coordination to solve a process or task alongside humans. A single, monolithic agent attempting to accomplish too much often results in low accuracy. However, when specialized agents work together, enterprises can simplify development and debugging experiences and focus on which parts of a process need better accuracy. Consider a customer service setting. Today, a single customer service representative may receive emails and tickets into a service desk solution, assess the importance and timeliness of requests, decide how to triage those requests and then answer the top 20 FAQs. A single AI agent wouldn't be able to replicate all those behaviors. Instead, it would be more effective to use a specialized AI agent for each leg of that process. One AI agent could focus solely on the initial ranking of the severity of queries. Another agent would diagnose the query based on which FAQ type it is. Another agent can specialize in answering every FAQ based on their knowledge, and finally, another drafts the response based on FAQ input. Meanwhile, the customer service representative could oversee each of these agents to ensure they are performing accurately and efficiently, while adding their unique, human touch to the experience. Knowing the power of specialized agents -- especially in improving accuracy -- UiPath recently acquired Peak, a UK-based company offering specialized agentic solutions that make accurate predictions for companies in order to optimize inventory and pricing decisions. Now, UiPath delivers specialized agents that empower industry executives with intelligent, data-driven decision-making and automation capabilities. The specialized AI agents gather and transform data, apply fine-tuned models and formulate recommendations -- all presented to decision makers via a clear dashboard for easy visibility. As more enterprises shift to agentic automation, work will be performed by a combination of AI agents, humans and traditional rules-based software. Each modality of performing work offers its own strengths and weaknesses. Traditional software, for instance, is fast, accurate and inexpensive -- but it lacks creativity and can't make complex decisions. Generative AI, on the other hand, can make decisions but is slower and lacks important business context, which leads to mistakes. Enterprises must distribute tasks across all three modalities of work to optimize the end result. That's why having an orchestration layer that hands tasks off from one modality to another is so critical -- ensuring all roles are assigned strategically. That way, all entities work smoothly together while maximizing their strengths to deliver optimal outcomes. Without orchestration, AI agents can still operate in silos to accomplish specific tasks, but they won't have the information they need to ensure those tasks are performed in a way that helps reach a larger goal in optimizing processes and longer-running workflows. Orchestration is also critical to ensuring each modality functions responsibly and remains compliant. Many enterprises are already implementing specialized AI agents to work on complex tasks that require meticulous details and fluid information, meaning agent accuracy is mission-critical. Enterprises that figure out how to unleash the benefits of agentic automation in a specific, targeted and tangible way will not only boost productivity internally -- they will lead the way in maximizing the business value of AI.

Agentic AI reshapes cybersecurity beyond the hype - SiliconANGLE

Elasticsearch weighs promise and risks of agentic AI adoption Artificial intelligence is driving a new frontier in cybersecurity, with agentic AI emerging as the next major leap beyond last year's large language model hype. This evolving technology promises autonomous, goal-driven decision-making -- but it also raises new challenges in oversight, trust and responsible deployment. While early adopters see game-changing potential in streamlining threat detection and response, security leaders are quick to point out that speed without accountability can create fresh vulnerabilities. They stress the need for clear governance frameworks, transparent model behavior and shared responsibility between vendors and customers. In their view, the real opportunity lies not just in building smarter AI agents, but in ensuring they operate within guardrails that protect both systems and reputations, according to Mike Nichols (pictured), vice president of product management at Elasticsearch B.V. "Everything is agentic now, it was AI last year, now it's agentic AI this year," Nichols said. " I wonder if people know what it means when they're asking for it. I would hate to be in the customer's shoes right now, trying to hear the reality." Nichols spoke with theCUBE's Jackie McGuire at the Black Hat USA event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They unpacked the reality behind today's AI buzzwords, the importance of agentic models over LLM hype and why human analysts remain irreplaceable in modern security operation centers. (* Disclosure below.) The AI hype train is somewhat stalled at the moment, with numerous prototypes and flashy product demos that show little practical utility. Thus, the rise of agentic AI could serve as a much-needed course correction. Unlike monolithic LLMs trained on noisy internet data, agentic models leverage a suite of AI tools -- regression, deterministic and probabilistic models -- each selected for its specific task. This allows for more control, accuracy and transparency, according to Nichols. "There's definitely some good stuff out there," he said. "But I think, as always, you have to sift through it all to figure out where there are some beneficial things that are in the environment." Despite growing fears that AI might eliminate entry-level analyst roles, the opposite scenario will eventually ring true, according to Nichols. Rather than replace human talent, AI should augment it -- automating mundane tasks such as querying logs or accessing siloed systems. This frees up analysts to do what they do best: critical thinking, pattern recognition and decision-making. "One of the best SOC analysts I ever met was a meteorologist who just understood weather patterns and brought that data science in," Nichols said. "I think we can get more of those people and break the barrier of who can be in the security team, which would be great ... diversify the entire place. But I think AI's going to allow us to get more humans involved, not remove humans. I don't think that's a good concept to follow." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the Black Hat USA event:

A practical framework for CIOs to succeed in the agentic era - SiliconANGLE

Today's chief information officers are juggling more priorities than ever -- from driving digital transformation and managing risk, to reducing cost and navigating constant regulatory shifts. In the midst of all this, autonomous artificial intelligence agents keep rising to the top. Not as hype. But as a practical lever for doing more with limited time, talent and budget. Why? Skilled labor shortages are real. Expectations for seamless, multichannel experiences continue to rise. And the threat landscape keeps expanding. In that context, digital labor isn't a nice-to-have. It's essential. But the sentiment I gain from CIOs most is: "Where do I begin?" Historically, CIOs managed infrastructure while business leaders focused on outcomes. Today, CIOs are leading from the front -- strategic enablers deeply embedded in driving results. In many cases, they're equal partners in transformation. As customer expectations climb and technology grows more complex, CIOs are orchestrating seamless, personalized experiences across digital and physical channels. That means integrating systems, managing cloud migrations, piloting AI and balancing it all under tightening budgets and rising demands. That's why the conversation around agents isn't just about innovation -- it's about sustainability. For teams already stretched thin, every new initiative must be measured not only by its promise, but by its feasibility. That's what makes this moment complex. Agents are clearly promising, and many CIOs see their potential to transform how work gets done. But they're also asking, "Where does this fit?" "Should I pause existing transformation work to make room for agents?" "Do I reprioritize, restructure, or divest, just to get started?" This is where many leaders get stuck. They're balancing mission-critical projects across the stack, and adding one more thing can feel like a risk they can't take, even if it's transformative. But this isn't an "or" scenario. It's an "and." Agents aren't a competing initiative. They're a lever for accelerating what's already on your roadmap -- helping you innovate faster, strengthen security, and optimize at scale. That's why CIOs need a practical framework that recognizes where they are and helps them scale agentic capabilities responsibly and effectively over time. Most organizations will begin here. Agents fetch data and return insights. For example, one global brokerage handles 54,000 calls daily, and advisers used to spend 90 minutes prepping for each. With agents, prep time now takes minutes. Results like this build momentum and prove that agentic AI can deliver value today. Agents begin to act -- updating records, coordinating tasks, scheduling services. This stage expands both technical complexity and organizational trust. Agents manage multistep workflows and decision trees. What qualifies as "complex" depends on your industry and risk tolerance, but the concept is the same: greater autonomy within well-defined boundaries. Agents collaborate across domains, systems, even organizations. Think supply chain agents interacting with logistics partners. This is the frontier: The architecture required is significant, but so is the potential. The framework aims to provide a simple, accessible way for CIOs to make informed decisions about how, and how far, to want to scale agentic capabilities. Not every company should begin with a customer-facing agent. If you're in a highly regulated industry, you might start with colleague-facing agents that assist with guidance, task execution, or data retrieval, with a human still in the loop. Others may start more conservatively. One strong entry point is agents that audit existing data for completeness and consistency. It may not feel flashy, but it builds a strong foundation for future use cases. The real question isn't technical capability, but organizational readiness. Do you have the right data, infrastructure, and governance in place? Are your teams engaged and aligned? Because you're not just deploying a large language model; you're building a platform. That means trust frameworks, secure orchestration, role-based access, audit trails, and scalable systems. Automation is just the starting point. This is more about unlocking capacity for higher-value, strategic work. Rising complexity and persistent labor shortages are forcing CIOs to rethink how work gets done. The stakes are too high, and the pace too fast, to keep relying on legacy models. That's why autonomous agents aren't a futuristic concept. They're becoming a foundation for how enterprises scale, adapt and deliver. This is a shift in architecture, and it's not one to tackle in isolation. Whether you're exploring use cases or scaling up, now's the time to embed agentic thinking into your transformation strategy. The organizations that align their people, priorities, and platforms will be the ones to unlock the full potential of digital labor and shape the future of intelligent work. When I land in my hometown of Toronto, the billboards are for personal injury lawyers. In San Francisco, it's AI agents. That contrast says a lot. Adoption looks different depending on where you are -- by region, by industry, by mindset. But no one can afford to stand still. This is as big as the internet. And it's already happening.

Enterprise AI hits an inflection point, the agentic era is here! | AIM

Scaling impact in the agentic era requires a shift in approach to AI by enterprises, from passive tools to agentic systems that can act autonomously within business processes. The next wave of enterprise AI isn't just generative, it's agentic. What was once a curiosity is now a top priority for business and technology leaders. Agentic AI is redefining workflows through intelligent, autonomous systems that handle complex tasks, adapt in real-time, and keep humans in the loop for oversight. Breaking free from automation's limits, AI now drives enterprise growth and transformation. Despite strong interest, most initiatives are still in the early or pilot stage. Realising the full potential of agentic AI demands a shift from use cases to business processes, siloed AI teams to cross-functional transformation squads, and changes to the AI architecture for interoperability. Sigmoid is one of the companies helping drive this change. Founded in 2013, Sigmoid was built on two core beliefs: better data leads to better decisions, and most organisations lack the systems to use data effectively. Since then, it has helped large enterprises become data-driven by aligning data and AI with business goals, and continues to build custom AI solutions that deliver tangible business outcomes. From Automation to Autonomy Agentic AI holds immense potential to streamline operations, elevate decision-making, and drive innovation across industries. By 2027, half of all business decisions will either be automated or supported by intelligent agents. This represents a fundamental shift in how decisions are made and executed across functions. "As businesses move beyond efficiency and prioritise growth and value-chain reinvention, Agentic AI is leading this shift by enabling intelligent, autonomous systems that accelerate innovation. Sigmoid empowers enterprises to gain a competitive advantage by transforming business processes, leveraging AI agents powered by strong data foundations and contextual knowledge," said Lokesh Anand, CEO and Co-Founder of Sigmoid. To enable this transformation, Sigmoid provides comprehensive agentic AI services spanning consulting, redesigning business processes, developing and deploying custom agents, and efficiently managing them in production. For instance, Sigmoid built an Agentic AI tool for a global consumer goods company to automate audit and compliance checks of marketing content across channels. Trained on inputs from legal and regulatory teams, the tool reduced approval times by 50%, delivering real-time checks on text and visuals to uphold brand standards and regulatory compliance. Building the Foundation for an Agentic Era AI agents are only as effective as the data and infrastructure that support them. According to Nasscom, 68% of enterprises are focused on improving data governance, while 62% are investing in integrating structured and unstructured data. These efforts signal a broader shift as organisations adopt Agentic AI for scalable data engineering to meet growing demands for speed, flexibility, and reliability. As enterprises transition, they confront long-standing challenges such as fragmented architectures, inconsistent data, and rigid automation tools that cannot adapt to changing inputs. Agentic AI provides a more flexible alternative. Built on large language models (LLMs) and multi-agent frameworks, these systems can reason, learn from feedback, and operate independently across data ingestion, transformation, quality, and governance. Sigmoid deploys these systems across enterprise data workflows to reduce manual effort and accelerate the path to insight. A global MedTech leader faced slow R&D cycles due to fragmented data across SAP systems and unstructured Design History Files. Sigmoid's Agentic AI solution enabled intelligent search and summarization, resulting in a 20% improvement in R&D process efficiency. Effective Agentic AI requires governance, observability, and traceability from the start. Along with lifecycle management and responsible AI practices, these elements establish a foundation for long-term reliability. Use of AI-ready data to support AI initiatives requires evolutionary changes to data management and upgrades to data architecture, skills, and processes.A leading infant nutrition brand partnered with Sigmoid to modernise data operations using Agentic AI. Agents monitored system health, classified issues, and triggered automated fixes- helping maintain uptime, reduce operational noise, and enhance observability across complex data environments. The solution enabled 70% faster issue detection, improved reliability, and cut manual effort and overhead costs by 30%. The Five Pillars To thrive in the agentic era, enterprises must reinforce five core pillars. First, talent readiness requires upskilling teams and defining new roles aligned with AI‑enabled workflows. Second, robust architectures demand building scalable, modular systems that seamlessly integrate AI. Third, responsible governance involves creating clear frameworks to manage autonomous behavior and ensure ethical, accountable deployment of intelligent agents. Fourth, AI‑ready data calls for improving data quality, governance, accessibility, and real-time usability to enable reliable, scalable AI operations. Finally, enterprise integration means shifting from isolated pilots to organization‑wide programs by embedding AI into core processes and driving outcome‑oriented delivery models. Sigmoid is leveraging agents to unlock the full potential of vertical use cases by automating complex business workflows. When a global chocolate manufacturer experienced delays in responding to detailed customer requests, it chose a smarter approach rather than conventionally expanding its sales team. Sigmoid deployed an agentic AI solution leveraging R&D data to recommend tailored products. AI agents managed each step of the sales inquiry process, reducing response time from 10 days to 10 minutes, doubling portfolio utilization, and increasing sales by 5%. The enterprises driving this shift are not pursuing novelty. They are fundamentally rethinking decision-making, system integration, and AI's role in everyday operations. Sigmoid is helping build an agentic ecosystem that is transforming enterprise workflows.

Black Hat 2025 shows agentic AI moving from theory to practice

At Black Hat 2025, agentic artificial intelligence is demonstrating tangible value within cybersecurity, marking a significant evolution from its largely theoretical presence at Black Hat 2024. This advancement represents a shift towards proactive, autonomous systems that can independently investigate, respond to, and predict potential cyberattacks. This transformation fundamentally alters security operations. Dr. Anya Sharma, Chief Scientist at DarkTrace, stated, "We're seeing a fundamental change in how security operations are conducted. Agentic AI allows us to automate tasks that were previously impossible, freeing up human analysts to focus on strategic initiatives." This automation extends beyond simple threat identification, enabling more complex security functions. One notable application of agentic AI is in threat hunting. This process, traditionally manual and intensive, is being revolutionized as agentic AI continuously scans networks for anomalies. It correlates data from diverse sources, proactively identifying potential threats before they fully materialize. Ben Carter, CISO of GlobalTech Enterprises, commented on its impact, stating, "The ability to autonomously hunt for threats is a game-changer. We've seen a dramatic reduction in dwell time and a significant improvement in our overall security posture." Agentic AI also plays a crucial role in incident response. It automates the triage of incidents, isolates compromised systems, and initiates remediation procedures. This capability significantly reduces the time required to respond to cyberattacks, thereby minimizing potential damage. The efficiency gained in these critical moments can mitigate widespread impact. Despite these advancements, the integration of agentic AI introduces new challenges for security professionals. A comprehensive understanding of how these systems operate is required, along with methods to validate their findings and ensure resistance against manipulation by malicious actors. Sharma emphasized the necessity of trust in these systems, stating, "Trust is paramount. We need to build systems that are transparent, explainable, and auditable." The ongoing development in cybersecurity suggests a future characterized by collaborative efforts between human expertise and agentic AI, enhancing the effectiveness and efficiency of security professionals.

Making the Leap: How Agentic AI Is Transforming the Future of Work

By Weston Morris, Sr.Director, Global Strategy Digital Workplace Solutions, Unisys Artificial Intelligence is advancing at breakneck speed, and this year marks a significant turning point with the rise of agentic AI. As businesses face mounting pressure from talent shortages and rising productivity demands, agentic AI is becoming a strategic imperative. It enables systems to autonomously execute commands on behalf of employees while adapting to ever-changing environments. As a result, the agentic AI market is expected to grow to over $47 billion by 2030 and redefine the workplace by driving greater productivity, personalization and efficiency. Agentic AI can also accelerate skills development and enable more meaningful collaboration across teams, which will be critical as organizations compete in a rapidly evolving global economy. Driving Productivity Through Intelligent Automation Unlike traditional automation, which typically follows rigid, predefined scripts, agentic AI can understand nuance, context and make real-time decisions to remove roadblocks before they escalate. Business leaders have already reported up to a 40% increase in task efficiency through AI-driven automation. By handling routine and repetitive tasks, agentic AI takes this further, allowing employees to redirect their time toward higher-value work that demands creativity, strategic thinking, and collaboration. For example, a manufacturing firm could use an agent that monitors potential supply chain problems based on global news events and notifies decision makers to make changes in suppliers. This agent could be expanded to monitor inventories. Another agent could monitor changes in customer orders. Soon, an overarching agent could even sequence these agents together to autonomously make changes to orders in the supply chain. According to Gartner, 80% of customer service issues will be resolved by agentic AI by 2029. By decreasing reliance on lengthy approval chains and empowering teams to act on AI-supported insights, businesses are becoming faster, leaner and more responsive to change. Accelerating Personalized Learning and Career Growth Agentic AI is not just transforming how people work but accelerating their growth. By taking over repetitive tasks and streamlining day-to-day workflows, these systems permit employees to reinvest their time into developing new skills. In fact, nearly half (44%) of employees using AI tools say they've already used the time saved to build new skills or advance their careers. This shift creates a powerful feedback loop: as AI takes on more busy work, employees gain space to focus on higher-value contributions and professional development. Unsurprisingly, nearly four in five (79%) workers believe that gaining AI skills will enable faster career progression. By integrating generative and agentic AI into daily operations, businesses are enhancing productivity and fostering a culture of continuous learning, ensuring they remain competitive in a rapidly changing talent landscape. Enhancing collaboration in hybrid work environments In hybrid work environments, collaboration is increasingly complex, fragmented by time zones, asynchronous schedules and a growing ecosystem of digital tools. Agentic AI is helping organizations overcome these challenges by proactively streamlining coordination. These systems can automate scheduling, manage communication flows and reduce common logistical roadblocks. For example, AI-powered companions embedded in tools like Zoom can now suggest meeting times based on each participant's local hours, reschedule conflicts and send tailored reminders, ensuring that all team members remain connected regardless of location. Its capacity for continuous, adaptive support sets agentic AI apart in hybrid settings. These systems integrate collaboration platforms with third-party applications, learning from user behavior to optimize workflows in real time. Operating around the clock, AI agents monitor project milestones, flag updates, and keep tasks on track, even outside standard working hours. The result is a more synchronized, resilient approach to teamwork that keeps business moving, no matter when or where employees work. Looking Ahead Agentic AI is no longer a future concept. It's already reshaping how forward-thinking enterprises operate, learn and compete. Companies adopting these systems already see tangible returns across their sales, maintenance and operational functions. By streamlining workflows, accelerating skills development and enabling collaboration across hybrid environments, these systems are laying the foundation for a new era of work. As these systems mature, they'll redefine workplace culture, leadership and human potential. Organizations that embrace agentic AI today will stay ahead of the curve, unlocking new levels of creativity, agility and growth. (The author is Weston Morris, Sr.Director, Global Strategy Digital Workplace Solutions, Unisys, and the views expressed in this article are his own)

Agentic vs. Traditional AI: The Autonomy

Join the DZone community and get the full member experience. Join For Free Artificial intelligence has developed, and the appearance of agentic artificial intelligence is a significant turning point. Unlike conventional AI agents, which depend too much on human cues, agentic AI systems (such as OpenAI's Auto-GPT 3.0, Google's Gemini Pro 1.5, and Meta's LLaMA 3) demonstrate autonomy, initiative, and adaptive decision-making. This blog post examines how agentic AI differs from traditional agents, what innovations are leading the transformation, and what this means for the world of automation and work. Understanding Traditional AI Agents Regular AI agents have been developed to handle specific tasks under strict confines. Such systems tend to be reactive and use preset stipulations or models to interpret input and generate output. Some of these systems are ancestral forms of virtual assistants, such as Siri and Alexa, or recommendation engines and rule-based chatbots. Key characteristics of traditional AI agents include: * Reactive behavior: They only respond to the user input or actions of the environment. * Predefined logic: They are bound by rules and models built during development. * Task-specific: They are created to deal with a specific range of problems or fulfill pre-set tasks. * Statelessness or minimal context: The majority of traditional agents do not have persistent memory and learn from long-term interactions. * Single-step reasoning: These agents usually perform just one task at a time without breaking down or sequencing complex objectives. While these systems have been useful for automation and user interaction, they lack true autonomy. They function more as tools than collaborators or problem-solvers. Defining Agentic AI Agentic AI is the name for a new class of systems of artificial intelligence, meaning systems capable of formulating goals and planning action to attain such goals and operating independently on sets of multi-step tasks. These systems demonstrate elements of like reasoning, initiative, and adaptability. The word "agentic" is based on agency, meaning the opportunity to act independently, make decisions, and pursue goals. Agentic AI is characterized by: * Goal-driven behavior: These agents could adopt loft-level goals and figure out how to achieve these goals. * Autonomy: Agentic AI can trigger tasks independently of constant user input. * Multi-step planning: They can act out often intricate sequences of actions. * Tool use: These systems are able to access APIs, do web searches, retrieve and manipulate data, and interact with software tools to achieve tasks. * Reflective learning: There are even some models that incorporate feedback loops to enable them to assess the effectiveness of their actions and change their strategies. * Persistent context and memory: Whereas the traditional agents tend to forget knowledge during interactions and therefore lack continuity and improvement, the agentic systems often hold on to knowledge during interactions, which brings out continuity and improvement in a given area. Recent Advances in Agentic AI (2024-2025) The growth of agentic AI is fuelled by game-changing advances from the best AI labs: * OpenAI's Auto-GPT 3.0: Released on April 4, 2025, Auto-GPT 3.0 enables AI agents to work independently to complete multi-step tasks, such as market research and workflow automation. The major exclusive functions are contextual memory, self-evaluation loops, and integration with API and web, which makes it a favorite to automate knowledge work by businesses. * Google DeepMind's Gemini Pro 1.5: Unofficially launched in early 2025, Gemini Pro version 1.5 provides memory of a million-token long context and advanced reasoning. It is particularly good in document summarization, research, and tool integration ,allowing for effortless support of enterprise productivity. * Meta's LLaMA 3 Agent Framework: Meet's 2025 release is about the multi-agent collaboration with agent-to-agent communication in a 3D environment, and scalability. An excellent solution for smart cities, immersive learning, and AR/VR. Comparative Analysis: Traditional AI vs. Agentic AI Why Agentic AI Matters The move towards agentic AI isn't a mere technical breakthrough; it is a paradigm shift in relationship to how we allow our machines to interact with us. Some of the most compelling implications are listed below: * Enhanced productivity: Tasks taken over by agentic AI do not end with individual steps but, rather, whole workflows. This minimizes the load on human users, and the complex tasks can be delegated. * Smarter personal assistants: AI assistants are better equipped to handle everything from project scheduling to personal goals, even with persistent memory and long context understanding. * Improved enterprise automation: Agentic systems can be used to run businesses' operations, to handle customer support on their own, and to synthesize large-scale data for use in decision-making for businesses. * Breakthroughs in research: Agentic AI can independently read literature, formulate hypotheses, and design an experiment, speeding up innovation. * Foundations for AGI: Agentic AI is a vital stepping stone, even though we are not in artificial general intelligence (AGI) just yet. It brings about self-direction and adaptation, two of AGI's core needs. Challenges and Ethical Considerations Although agentic AI is generating great excitement, no less important are the concerns and obstacles that it poses, which will require an answer. * Reliability and trust: An agent can remain self-delusional or make a poor decision or chase aims that differ from those he or she intended. Solid testing and fail-safes are necessary. * Alignment and safety: And the more autonomous agents become, the harder, but at the same time more vital, it is to ensure that they correspond to human values and goals. * Data privacy and security: With more integrations and memory, such agents frequently need to access sensitive data, thereby creating some privacy concerns. * Computational costs: Agentic models are resource hungry and therefore costly in terms of scaling up or in terms of operation on a continual basis. * Job displacement: Similar to other AI revolutions, there is a realistic possibility of agentic systems replacing jobs in both customer service and administration, and even in the creative area. Future Outlook In the future, agentic AI is expected to manifest itself as an automated component of digital ecosystems. In light of ongoing improvements in long-context reasoning, tool orchestration, and ethical alignment, we might experience: * Autonomous digital workers who run entire business units. * Collaborative AI groups that assist in making medical diagnoses, engineering, and research. * Teaching timeless agents that change along with the human users. Agentic AI is the next generation of AI. Unlike traditional AI agents that operate as reactive tools, agentic systems reason, plan, and act autonomously. These systems are becoming the stage for a world where machines cease to be assistants and become collaborators, all made possible because of innovations from OpenAI, Google, and Meta. Just as with any technology that is transformative, success is not only in availability and capability, but also in careful implementation, ethical considerations, and societal adaptation. Whereas the questions are no longer whether machines could act, the questions now are whether we were prepared for their current autonomy.