The next wave of Artificial Intelligence doesn’t wait for your prompts. It watches, decides, and acts on its own. Here’s breaking down the difference between today’s AI and tomorrow’s agentic systems- and what may go wrong when AI agents go rogue.



Meet agentic AI–the autonomous evolution of artificial intelligence that’s already trans- forming how businesses operate. Unlike the chatbots and image or video models that sparked the generative AI revolution, these systems don’t just create content when prompted. They independently monitor situations, make decisions and take actions across multiple systems with- out human intervention.



Regular AI tools need you to tell them exactly what to do every time. Agentic AI is a little different. “There will be as many agents as there are business processes. And there is an enormous amount of business processes in the world,” says Richard Riley, General Manager, low-code and agent platforms, at Microsoft. “Think of AI agents like a football team. You don’t want eleven goalkeepers. You need specialists in different positions who understand their role but can seamlessly work together.”



This blend of personal AI assistants and autonomous agents represents a massive shift in how organizations will operate going forward. We’re not heading toward a future with a handful of all-purpose AIs but rather an ecosystem of specialized agentic AI systems. Each AI agent will handle a specific task while collaborating with others. An AI agent designed to detect fraudulent trans- actions needs different capabilities than one scheduling maintenance or providing customer support. This specialization makes perfect sense but creates a new challenge: how do we manage dozens or even hundreds of specialized agents without creating digital chaos?



Eugene De Souza, Regional Cloud Ecosystem Lead at Red Hat, a leading provider of enterprise, open source solutions, breaks down the difference between today’s AI and tomorrow’s agentic systems: “Generative AI is like a chef who creates a fantastic dish based on a reci- pe, but needs you to order it first. It’s reactive and lacks autonomy. Agentic AI is more proactive–like a maître d’ who not only takes your order but decides what dishes to prepare, manages the kitchen, and ensures everything is served perfectly without constant instructions.”



Agents of orchestration



This shift isn’t just about automation–it’s about fundamentally changing who (or what) initiates actions in our digital systems. Today, companies are developing orchestration layers–systems that coordinate multiple agents, manage their priorities, and ensure they work together rather than at cross purposes. These platforms are becoming the command centers for an increasingly autonomous digital workforce.



“AI is giving people expertise on demand,” says Colette Stallbaumer, Microsoft Copilot General Manager. “It’s providing capabilities that individuals wouldn’t other- wise have access to.” What makes this approach different from previous waves of workplace automation is how AI agents enhance human capabilities rather than just taking over tasks. Agentic AI can monitor data streams 24/7, instantly processing information that would take humans hours or days to review. When an agent detects anomalies or opportunities, it can either act within predefined parameters or alert its human counterparts with already-analyzed data and recommendations.



Despite this progress, agentic AI still faces major limitations. It struggles with context–it doesn’t always know when unusual circumstances mean it should break from standard procedures. It also can’t plan long-term very well–it’s good at executing the next step but not at developing complex strategies that unfold over time. And, of course, an AI agent lacks common sense, the background knowledge humans take for granted. Without these capabilities, agentic systems sometimes make decisions that tick all the technical boxes but miss the bigger picture.



“Users need to accept that today’s agents will sometimes get things wrong,” says De Souza. “It’s part of the innovation process.” This acceptance of imperfection is crucial because it changes how we implement AI systems. Rather than expecting perfection from day one, companies are learning to deploy agentic AI in controlled environments where it can learn from its mistakes without causing harm. A great example comes from the manufacturing industry where agentic AI is being used to monitor production lines and automatically schedule maintenance before equipment fails.



Financial institutions are also turning to agentic systems to scan millions of transactions for signs of fraud, flagging sus- picious activity for human review. That said, Riley does acknowledge that many businesses find the concept of agentic AI overwhelming at first. His recommendation is straightforward– begin with existing automated processes that can be enhanced with agentic technology. From there, companies can either empower end-users to build their own agents for specific needs or use the technology to tackle problems that were previously too complex to automate.



Strategic collaboration



“I think the next major evolution will be multi-agent systems,” predicts De Souza. “Think of it as a team of specialists rather than a single assistant. They could function like entire departments working toward a common goal.” This digital collaboration mirrors how human organizations function, with specialized roles working together. The difference is that agentic AI operates continuously, exchanges information instantly, and scales on demand. A system that works for one company can be quickly replicated to a thousand locations, creating consistent operations at a scale impossible with human-only teams.



“The future of AI isn’t one AI doing everything—it’s multiple AI agents collaborating in an open, secure, and scalable way,” adds Riley. As these technologies evolve, we’re seeing a fundamental reimagining of how work happens. The division of labor between humans and machines is being redrawn in ways that go far beyond simple automation of repetitive tasks. Agentic AI is increasingly handling not just the execution but also the monitoring, analysis, and even planning stages of work. The autonomous revolution has begun, and it’s accelerating. The question isn’t whether agentic AI will transform business but how quickly, and whether organizations are prepared for a future where some of their most productive team members aren’t human at all. “Think of agents as the new apps in this AI-powered world. It’s how people will work moving forward,” says Stallbaumer. “Agents exist on a spectrum. Some are very simple, some are highly complex,” adds Riley. “But they can now solve complex business problems that were previously too com- plicated for traditional automation.”

Tag: Autonomous AI

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