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How Do AI Agents Work? The Perceive–Decide–Act Loop Explained
A step-by-step explanation of how an AI agent actually processes a task, from receiving a goal to taking action and adapting to results.
Direct answer
An AI agent works through a repeating loop: it receives a goal and current context, uses a language model to decide the next action, executes that action through a tool or API, observes the result, and feeds that result back into the next decision — repeating until the goal is met or a stopping condition is reached.
Last reviewed 2026-08-29
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Step by step
- 1. Input — the agent receives a goal (a user instruction, a triggered event) along with any available context.
- 2. Reasoning — the underlying model evaluates the goal and current state to decide what action, if any, would move toward the goal.
- 3. Tool selection — the model chooses which available tool or API to invoke, and with what arguments, based on its reasoning.
- 4. Execution — the tool call actually runs — a database query, a file write, an API request — outside the model itself.
- 5. Observation — the result (success, error, returned data) is captured and added to the agent's context.
- 6. Loop or stop — the agent decides whether the goal is met, whether to try a different approach, or whether to stop and report back.
What makes this different from a single model call
A single chat completion has no step 4 through 6 — it generates text and stops. What makes the loop 'agentic' is that steps 3-5 can repeat multiple times autonomously, with the model's own output from one iteration shaping the input to the next, without a human manually re-prompting at each step.
This loop structure is also why agents fail differently than chatbots: an error at step 4 (a tool call that fails) can compound if the agent misinterprets the failure at step 5 and takes a worse action at the next iteration — which is why bounded scope and observable logging matter more for agents than for single-turn chat interfaces.
Where this loop is implemented
Frameworks like LangGraph, CrewAI, and the Model Context Protocol together define most of the standard plumbing for this loop today: MCP standardizes how tools are exposed to the model, and orchestration frameworks handle the loop control, memory, and multi-step state management around it.