Agentic Demystified

What it is, what it isn’t, and why the distinction actually matters

If you have spent any time around AI conversations lately, whether that is conferences, strategy decks, LinkedIn posts, or just the usual tech chatter, you have probably noticed one phrase cropping up everywhere. Agentic AI. It sounds powerful, futuristic, and faintly inevitable. And judging by how confidently it is being used, you would think everyone agrees on what it means.

They do not.

We touched on this territory before in The Rise of Autonomous Intelligence, when we talked about AI systems beginning to act with initiative rather than simply responding on demand. Since then, the hype has accelerated. Suddenly everything is agentic. Chatbots are agentic. Copilots are agentic. A workflow with a language model glued in is apparently agentic too.

That is where things start to get confusing

This article is not about talking agentic AI down. It is about clearing the fog. Because agentic AI is a real and important shift, but only if we understand what it actually is.

The confusion is understandable. Most people experience AI today through chat interfaces, copilots, or automated helpers. These systems can feel intelligent, proactive, sometimes even a little uncanny. They respond well, they anticipate needs, and they can give the impression that something autonomous is happening behind the scenes.

But intelligence is not the same thing as agency.

An AI can be extremely capable and still have absolutely no say in what happens next. And that’s the key.

When people talk about agentic AI in a grounded way, they are not talking about how clever the system sounds or how human the interaction feels. They are talking about who is making the decisions. An agentic system is one that can pursue a goal over time, decide what to do next, take actions in the real world through tools or systems, observe the results of those actions, and adapt its behaviour accordingly. Crucially, it does not need a human to prompt it at every step. The goal persists. The system keeps going.

If an AI only does something after you ask it, it may be impressive, but it is not agentic.

One of the cleanest ways to understand this is to think about prompts versus goals. Most AI systems today are prompt driven. You ask a question, the system responds, and the interaction ends. Agentic systems work differently. A goal exists beyond a single exchange. It is stored somewhere outside the model and continues to shape behaviour over time. A prompt is an instruction. A goal is a responsibility. Agency begins when a system is allowed to hold onto that responsibility and decide how best to fulfil it.

This is where a lot of the current confusion comes from, because many existing systems are being relabelled rather than rethought. A chatbot does not become agentic just because it sounds confident. A copilot is still a copilot if it waits for approval. A predefined workflow does not gain agency just because one step uses a language model. If the sequence of actions is fully scripted in advance, you are looking at automation. If a human always decides when the AI may act, you are looking at assistance.

True agency requires something more structural. There has to be a persistent objective, real authority to act rather than merely recommend, a continuous loop of observation and action, some form of memory, and feedback that actually affects future behaviour. None of this lives inside the model itself. These are properties of the system surrounding the model, which is why agentic AI is more about architecture than about model capability.

A useful way to cut through all of this is to ask a very simple question. Who decides what happens next. In scripts and workflows, humans (the programmer) decide. In assistants and copilots, humans still decide. In true agentic systems, the AI decides, within boundaries that humans have defined in advance. There is no need to bring consciousness or free will into it. This is not philosophy. It is delegated decision making.

And this distinction matters, because calling everything agentic may sound exciting, but it hides the questions that actually need answering. Once a system is allowed to act on its own, you have to think about what it is permitted to do, how its decisions are tracked, how it can be stopped or corrected, and who is accountable for its actions. Those questions simply do not arise in the same way with assistive systems, which is exactly why being precise here is important.

A concrete example helps. Imagine a system responsible for maintaining quality or compliance in a digital environment. An assistive AI might identify issues, suggest fixes, and then wait patiently for someone to approve them. An agentic system would monitor continuously, detect issues as they arise, apply corrective actions automatically, verify that those actions worked, escalate only when necessary, and then carry on without being prompted. The underlying AI capability could be very similar. The difference lies entirely in agency.

So the bottom line is this. Agentic AI is not a synonym for advanced AI. It is a specific architectural choice. It describes systems in which AI is trusted to pursue goals, make decisions, and act within carefully designed boundaries. Without boundaries or guardrails it can introduce risks that might be hard to unwind.

As interest in agentic AI continues to grow, clarity matters far more than hype. If we want to build these systems responsibly and effectively, we need to be honest about what the term actually means, and resist the temptation to slap it on every process just to sound clever.