What are Agent Loops?

A chatbot answers once and stops. An agent keeps going. The difference is the loop. An agent loop is a repeating cycle of thinking, acting, and checking results that continues until the task is actually finished, not just until the model has produced a reply.

How an Agent Loop Works

Each pass through the loop follows the same shape. The agent observes its current context, including the goal and anything it has learned so far. It reasons about the best next step. It acts, usually by calling a tool such as a search, a database query, or a file edit. It then observes the result of that action and folds it back into context. The loop runs again with this new information and keeps running until the agent decides the goal is met or hits a stop condition like a step limit.

Reason, Act, Observe

The canonical pattern behind most agent loops is reason, then act, then observe, popularized by the ReAct approach. Reasoning lets the agent plan and decide. Acting lets it change the world or gather new facts. Observing lets it respond to what actually happened rather than what it assumed. Removing any one of the three breaks the loop: reasoning without action is just thinking out loud, and action without observation is flying blind.

Why Loops Make Agents Autonomous

A single model call cannot recover from a mistake, because it never sees the outcome. The loop is what gives an agent a feedback signal. If a tool call fails, the agent sees the error and tries a different approach. If a result is incomplete, it gathers more. This ability to incorporate results and self-correct across many steps is what lets agents handle multi-step tasks that a single response cannot.

Agent Loops in Marketing Workflows

Real marketing work is rarely one step. Publishing an optimized page might mean researching a topic, drafting copy, generating an image, checking it against brand guidelines, applying SEO fixes, and deploying the result. An agent loop drives each of these as a discrete action with a checkpoint, so the agent can verify each step and adjust before moving to the next instead of producing one unchecked draft and stopping.