How do AI agents work?

AI agents work by giving a language model a goal, access to tools, and a reasoning loop: the model perceives its current context, decides what action to take, executes that action through a tool call or sub-agent invocation, observes the result, and repeats until the task is complete or a stopping condition is met.

The perceive-reason-act loop

At each step, the agent has a context window containing the original task, results from previous steps, and any relevant memory. The language model reads this context and produces a decision: call a tool, produce an output, ask a clarifying question, or declare the task complete. That decision is executed by the agent framework, the result is added to the context, and the loop runs again. The loop continues until the model outputs a stop signal or an external limit is reached. The model itself does not execute actions — it decides what to execute, and the framework executes it.

Tools and the environment

Tools are what make agents capable of affecting the world outside the model. A tool is a function with a name, a description, and a parameter schema — information the model reads to know what the tool does and how to call it. Tools can be anything the framework exposes: web search, code execution, database queries, file reads and writes, API calls, or invocations of other agents. The set of tools available to an agent defines the scope of what it can do. Limiting that set is the primary mechanism for limiting an agent's blast radius.

Memory and state

Agents maintain state in several ways. In-context memory is whatever fits in the current context window — sufficient for short tasks but expensive and limited by window size. External memory uses a retrieval step to load relevant past information into context at each step. Long-term state is stored in databases or files that persist across sessions. Different task types need different memory strategies: a coding agent completing a single task needs little beyond in-context memory; a personal assistant operating across weeks needs durable external memory with retrieval.

How do AI agents work? — FAQ

Does the language model itself decide what tools to call?

Yes. Tool selection is a reasoning output of the model. The framework presents available tools with descriptions, the model decides which tool to call and with what arguments, and the framework executes the call. The model cannot execute the call itself — it can only describe what should be called.

What happens if an agent gets stuck in a loop?

Without safeguards, an agent can call the same tool repeatedly, either because the tool keeps returning unexpected results or because the model loses track of progress. Well-designed agent frameworks set hard limits: maximum number of steps, maximum tokens consumed, maximum number of calls to any single tool. When a limit is hit, execution stops and the failure is logged. Setting those limits appropriately for each task is part of the engineering work.