How AI Agents Actually Work: The Agent Loop, Explained

Every AI agent — from a research assistant on your phone to an autonomous coding system — runs on the same surprisingly simple core. Once you see it, agent products stop looking like magic and start looking like engineering. This is that explanation, minus the math.

The agent loop

An agent is a language model running inside a loop with four beats:

1. Plan. Given a goal, the model breaks it into steps. “Find flight options” before “book hotel near the venue.”
2. Act. It executes the current step — writing a section, calling a tool, querying a source, or asking a clarifying question.
3. Observe. It reads the result of its own action. Did the search return anything useful? Does the draft contradict the brief?
4. Repeat or finish. Based on what it observed, it revises the plan, takes the next step, or decides the goal is met and delivers.

That’s it. The model generates text exactly as a chatbot does — but the loop structure turns isolated answers into progress toward a goal. A chatbot is one trip around a quarter of this loop. An agent might go around it thirty times before you see anything.

The four parts every agent needs

1. A model (the brain)

The reasoning engine — today, typically a frontier LLM like Claude Fable 5. Model quality matters more for agents than for chatbots, for a brutal mathematical reason: errors compound. A model that’s right 95% of the time per step finishes a 10-step task correctly only ~60% of the time. Most of the “agents suddenly work now” story is models pushing that per-step number up.

2. Instructions (the role)

A system prompt defines who the agent is, what good output looks like, and what it must never do. This is why specialist agents beat one generalist: a “Research Analyst” told to cite reasoning and flag uncertainty behaves measurably differently from a blank chatbot handed the same question. (It’s also why apps ship pre-built expert agents — the role engineering is done for you.)

3. Tools (the hands)

Anything the agent can do rather than say: search, code execution, file reading, calendar access. Tool calls are how an agent touches reality — and how reality pushes back, because tool results feed the observe step. Standards like MCP (Model Context Protocol) exist so any agent can plug into any tool without custom wiring.

4. Memory (the thread)

Within a task, the agent’s context window holds the goal, the plan, and everything observed so far. Across tasks, some agents keep durable notes. Memory is what makes step 14 consistent with step 2 — and running out of it is one of the classic ways agents fail.

Multi-agent systems: the loop, stacked

Once one agent works, an obvious question follows: why not several? In a multi-agent session, each specialist runs its own small loop, and their outputs become each other’s inputs — the researcher’s findings feed the strategist, whose plan feeds the writer. The key mechanic is that each agent reads what the others produced and adds only what’s missing, like colleagues rather than parallel interns. Done well, this builds critique into the process: the first draft you see has already survived an internal review.

Where agents still fail (and what good ones do about it)

• Hallucination under momentum. An agent mid-plan would rather invent a fact than stop. Good agents are instructed to flag uncertainty; good products make sources and reasoning visible so you can check.
• Loops and rabbit holes. “Research the market” can recurse forever. Step budgets and time limits keep the loop honest.
• Goal drift. Thirty steps in, the agent is polishing something you didn’t ask for. Frequent re-grounding against the original goal — literally re-reading the brief — is the fix.
• Confidently finishing early. The most human failure mode there is. Self-review steps (“does this output satisfy every requirement?”) catch most of it.

None of these are solved with certainty — they’re managed, the way human teams manage the same failure modes with checklists and reviews. Anthropic’s engineering guide Building Effective Agents is the canonical read if you want the practitioner’s view.

Autonomy is a dial, not a switch

“Agent” doesn’t mean “unsupervised.” In practice there’s a spectrum: an agent that drafts and waits for approval, an agent that executes a whole plan and reports back, and everything between. Consumer agent apps sit deliberately on the supervised end — the agent does the legwork; you keep the judgment calls. That division of labor is the entire point: you’re not replaced as the decision-maker; you’re relieved as the typist.

The takeaway

An AI agent is a language model, a role, tools, and memory — wrapped in a loop that plans, acts, observes, and repeats until the goal is met. Everything else you’ll read about agents is an elaboration of that sentence. For the broader picture (definitions, history, use cases), start with our full guide: What is agentic AI?