Agent Loop Simulator
Step through an AI agent loop: understand the task, choose an action, call a tool, observe the result, recover from errors, and answer.
An AI agent repeatedly decides what to do next, acts through tools, observes the result, and uses that observation to decide whether to continue, retry, or answer.
Find the weather in Tokyo and tell me if I need an umbrella.
Add a termination rule and fallback
Define when the agent should retry, when it should stop, and what it should tell the user when the weather tool stays unavailable.
What is an AI agent loop?
An agent loop is an iterative control process in which a model or agent interprets the current state, chooses an action, receives an observation, and decides whether to continue or return a final answer.
Why do agents use tools?
Tools allow an agent to access information or perform actions that are not contained in the model itself, such as querying a database, calling an API, running code, or updating another system.
What is an observation?
An observation is the result returned from an action or tool. The agent uses that result as new evidence for the next decision.
What makes an agent reliable?
Production agents need explicit tool contracts, validation, retries, timeouts, authorization boundaries, observability, and termination conditions. The loop alone is not the reliability layer.
Common questions
No. A chatbot can answer directly without taking iterative actions through tools.
No. Agent control can be implemented through explicit state, tool calls, structured decisions, and observable transitions.
Because failures are normal in production; recovery behavior is part of the system design.
No. Plan-and-execute, workflows, state machines, and other orchestration patterns are also common.