Why does an agent loop matter?

The loop turns a single model response into an adaptive workflow. It lets the system recover from partial information, use tools, check outputs, and continue until the goal is complete.

What belongs inside an agent loop?

Most agent loops include a goal, short-term state, tool calls, observations, stopping criteria, error handling, and evaluation signals.

Feedback-driven AI agents

Agent Loop

Q: What is an agent loop?

An agent loop is the repeated cycle an AI agent uses to plan a next step, act with tools or messages, observe the result, reflect on progress, and choose what to do next.

An agent loop is the control pattern that lets an AI agent plan, act, observe, reflect, and improve until a real task is finished.

Read the workflow Compare patterns

Definition

What is an agent loop?

An agent loop is a repeated decision cycle for AI systems. Instead of producing one final answer, the agent keeps a compact state, chooses a next action, calls tools when useful, reads the result, and decides whether to continue or stop.

The pattern is useful when the work needs multiple steps: research, coding, data cleanup, customer operations, testing, or any task where the next move depends on what just happened.

Core workflow

The five moves inside a useful loop.

01
Plan

Translate the goal into the next small decision, with constraints and a stopping condition.
02
Act

Send a message, call a tool, query data, write code, or make another bounded change.
03
Observe

Read the output, error, page state, test result, or human response produced by the action.
04
Reflect

Compare the observation against the goal and decide whether the loop is still on track.
05
Improve

Update state, narrow the next action, recover from failure, or finish with evidence.

Patterns

Choose the loop shape that fits the risk.

Showing 4 patterns

Tool loop

Best for search, code, databases, and file operations where the model needs fresh observations before continuing.

Approval loop

Best for payments, destructive operations, publishing, or any workflow where a person must confirm intent.

Critic loop

Best for drafts, generated plans, test output, and quality gates that improve with explicit review.

Recovery loop

Best when failures are expected and the agent needs retries, narrower actions, or a fallback path.

Use cases

Where agent loops help.

The agent loop pattern is strongest when the system can inspect reality after each step and improve the next move.

Software agents that edit files, run tests, and fix failures.
Research agents that search, compare sources, and cite evidence.
Ops agents that inspect logs, execute runbooks, and escalate risk.
Data agents that profile tables, transform records, and validate output.

Checklist

Make the loop observable.

Goal: State what done means before the loop starts.

State: Keep only the context needed for the next decision.

Tools: Give every action a clear input, output, and failure mode.

Limits: Use budgets, retries, and human approval where risk rises.

Evidence: End with tests, citations, logs, or another verifiable result.

FAQ

Common agent loop questions.

Is an agent loop the same as a chatbot?

No. A chatbot usually answers a message. An agent loop keeps working across steps, using observations to choose the next action.

Does every AI app need an agent loop?

No. A direct model call is often better for simple classification, extraction, or one-shot writing. Use a loop when feedback changes the next step.

What makes an agent loop reliable?

Clear state, narrow tool interfaces, observable outputs, retry limits, tests, and human approval for high-impact actions.