Agents operate in loops
A result becomes input to the next decision.
Loop Engineering·Intermediate·9 min
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.
01
Experience
Work the problem before reading the explanation.
Lesson path
01
User
Understand task
02
Agent
Choose action
03
Tool
Call weather tool
04
Observe
Observe result
05
Refine
Retry or refine
06
Answer
Final answer
Loading deterministic lab runtime…
02
Reflection
Turn the outcome into a rule you can reuse.
Key takeaways
Tools extend capability
Actions connect the model to external systems.
Observations guide decisions
Tool output changes what the agent does next.
Reliability needs recovery
Retries and termination rules belong in the design.
03
Learn More
Connect the experience to concepts, references, and transfer.
Concept guide
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
Is every chatbot an agent?
No. A chatbot can answer directly without taking iterative actions through tools.
Does an agent need hidden chain-of-thought?
No. Agent control can be implemented through explicit state, tool calls, structured decisions, and observable transitions.
Why simulate tool errors?
Because failures are normal in production; recovery behavior is part of the system design.
Is ReAct the only agent pattern?
No. Plan-and-execute, workflows, state machines, and other orchestration patterns are also common.
The weather values and timing are illustrative; the control-flow concepts are the learning target.
LEARNING CONTEXT
Learning context
An Agent loop is a control system with explicit stopping, recovery and escalation policy.
Not seen
Mental models
- S05-M01Act → observe → verify loop
- S05-M03Bounded autonomy, termination and escalation
Suggested backfill
No shipped prerequisite is required before entering this incident.
Transfer the model
A tool keeps returning a retriable error. Define the point where the Agent should retry, change strategy, or escalate.
04
Next
Carry the idea into another problem or build.
Build challenge
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.
You completed the Foundation learning path.
Next: continue into failure-first production Labs.
Join Early Access →