Inside an AI agent: how autonomous systems actually decide what to do

"Agentic AI" gets thrown around loosely enough that it's worth being precise about what actually happens when an agent decides to take an action, instead of just answering a question.

A chatbot answers. An agent acts.

A traditional LLM chat interface takes an input and produces text. An agent takes an input, reasons about what needs to happen, chooses from a set of tools it has access to (a database query, an API call, a message send), executes that tool, observes the result, and decides what to do next — in a loop, until the task is done or it hits a stopping condition.

That loop is the entire difference. A chatbot that's good at answering questions about your CRM is not a CRM agent. A CRM agent is one that can actually look up a record, decide the deal needs a follow-up email, draft it, and — depending on how much autonomy it's been given — send it.

The reasoning step is where most of the engineering effort goes

Picking the next tool call sounds simple until you're building it. The agent has to:

1. Understand the current state of the task from everything that's happened so far.
2. Decide whether it has enough information to act, or needs to gather more.
3. Select the right tool from potentially dozens of available ones, with the right arguments.
4. Recognize when a result is unexpected and adjust rather than barreling forward.

Step 4 is the one most demos skip. It's easy to build an agent that works when every tool call succeeds and every API returns what you'd expect. It's much harder to build one that notices a tool returned an error, or an empty result, or something that contradicts an earlier assumption, and actually changes course instead of hallucinating a plausible-sounding continuation.

Autonomy is a dial, not a switch

The most consequential design decision in an agentic system usually isn't the model — it's how much the agent is allowed to do without a human checking in. Fully autonomous agents that can take irreversible actions (send an email, execute a trade, update a medical record) need a much higher bar of testing and logging than agents that draft something for a human to approve.

A useful default: let the agent act autonomously on anything reversible and low-stakes, and require a human approval step for anything that isn't. That single design choice does more for real-world safety than any amount of prompt engineering.

Why the loop needs to be observable

Every iteration of that reason-act-observe loop should be logged: what the agent decided, why (its stated reasoning), what tool it called, and what came back. Not for debugging convenience — for accountability. When an agent does something wrong, "we don't know why" is not an acceptable answer to give a client, and it shouldn't be an acceptable answer to give yourself either.