As someone who’s been in AI since 2013, watching the word “agent” get stuck on everything that’s really a simple LLM call is wearing on me.

By now, you’ve seen a lot of companies promise you solutions that are “agentic.” Let me tell you why that’s a misconception, and why it’s blocking you in realizing value from AI.

What the word used to mean

Starting with what the word used to mean. An agent senses its environment and acts on it, and a rational agent picks the action that best serves a goal. Keep it running and you get a loop: sense, act, see what changed, act again. The goal was the point. The autonomy was never the selling point.

Most of what gets sold as agentic today doesn’t resemble that at all. Often, it’s a single LLM call. Input in and answer out. Basically a glorified function.

And I mean this mislabeling isn’t necessarily the problem right? I think this happens to all types of technologies becoming mainstream. The problem I see, is that the word “agentic” is used as marketing language to note that something is more intelligent and therefore should work better.

It shapes what you expect to pay for, and what you forget to ask. If a product is described as agentic, people stop asking which steps it runs and in what order. Those are the questions that tell you whether it will hold up.

Why real agents are rarely where the ROI is

The reality is that even the real agents that run a loop and pick their own steps, are probably not where your ROI is right now. The thing that makes an agent powerful is that it decides its own path while it’s running. That same thing makes it hard to trust.

You can’t test a path you never defined, and you shouldn’t put something in production that you can’t check.

This gets practical quickly. Your testing has to cover behaviour rather than a fixed set of cases, and when something goes wrong you have to reconstruct what the system decided and why. Both are doable, and both cost more than most business cases assume.

What actually paid off

Every build I’ve worked on that actually paid off leaned on deterministic workflows. Fixed steps that run the same way every time. Where an LLM did the work, it did it inside a narrow boundary we could test against a clear measure. It looked less “impressive”, but it held in production.

A narrow boundary means the model is asked to do one thing you can score. Pull these fields out of this document. Decide which of these four categories this message belongs to. You can run a few hundred examples through it and know where you stand before anyone depends on it.

The steps around it stay ordinary software. That is what makes the whole thing reviewable, and it is why these builds tend to survive their first bad week.

Where an agent does earn its place

The agent earns its place in the spots where the path genuinely can’t be fixed in advance. That’s a smaller set of problems than the noise suggests.

Open-ended research is one of them, where the next question depends on what the last answer turned up. Investigation work across several systems is another. What they share is that you could not write the steps down beforehand even if you wanted to.

So before you reach for an autonomous agent, look at where a workflow you can trust would already do the job. That’s usually where the value has been hiding!

Originally posted on LinkedIn