How Do AI Agents Actually Work? The Loop, Explained

Underneath almost every AI agent — from a coding assistant to a personal agent like agint — is the same basic loop: the model decides what to do next, calls a tool to do it, reads back what happened, and repeats until the task is done. Everything else — memory, planning, multi-agent setups — is built on top of that one loop.

Step 1: The model decides what to do next

Given the goal and everything it knows so far, the language model picks the next action — which tool to call and with what input. This is different from a normal chat reply: instead of writing an answer for a human to read, it's writing a structured instruction the system can execute, often called "tool calling" or "function calling."

Step 2: A tool actually does it

The chosen tool runs — a web search, a code interpreter, a calendar API, a browser action, an email send. This is the part a plain chatbot doesn't have at all: the ability to reach outside the conversation and cause something to actually happen.

Step 3: The result comes back, and the model reads it

Whatever the tool returned — search results, an error message, a file, a calendar conflict — goes back into the model's context. This is the "observe" step, and it's what lets an agent react to reality instead of just executing a pre-written plan blindly.

Step 4: Repeat, or stop

Based on what it just observed, the model decides whether the goal is met, whether it needs to try something else, or whether it should stop and ask a human — for approval, for missing information, or because it's hit a wall. That stopping condition is what separates a useful agent from a runaway one; well-designed agents are conservative about continuing without a human when the next step matters — sending money, publishing something, deleting data.

Where memory fits in

Memory is what lets an agent carry context between separate tasks, not just within one loop. A short, editable memory sheet — the pattern agint uses — means the agent reads who you are and how you work before every reply, rather than starting from zero each conversation, and you can see and correct exactly what it "knows" about you.

Where approval fits in

The loop above, left unchecked, will happily send an email or spend money without asking — that's the actual risk with fully autonomous agents. The fix most serious agent products converge on is an approval gate: the agent drafts, plans, and prepares everything, then stops and asks before anything that sends, publishes, or spends actually happens. It's a deliberate design choice, not a limitation — and it's the one agint is built around at every step, not just the risky ones.

Multi-agent systems, briefly

Some frameworks run several agents at once, each with a role — a researcher, a writer, a reviewer — coordinating with each other rather than one agent doing everything alone. That's the same decide-act-observe loop, just with multiple models talking to each other instead of one model talking to tools directly.