Model an agent loop
Trace a task through observation, decision, action, and feedback.
- Describe the recurring stages of a tool-using agent loop.
In this track, loop engineering means designing the repeated cycle that lets an AI system make progress on a task. A typical loop receives an observation, decides what to do next, takes an action, and observes the result. It repeats until a completion condition is met or it hands control back to a person. The loop is an application design: the model proposes decisions, while software controls execution and records state.
A small example
1observations = ["Need current weather", "Forecast found: 18 C"]
2for observation in observations:
3 print("Observe:", observation)
4 print("Decide: continue if the goal is not met")Observe: Need current weather Decide: continue if the goal is not met Observe: Forecast found: 18 C Decide: continue if the goal is not met
The cycle is sometimes described as observe, think, act, and observe again. Avoid treating a model’s internal reasoning as the system’s control logic. Make actions, results, and completion checks explicit so the application can handle errors and explain what happened.
Key takeaways
Describe the recurring stages of a tool-using agent loop.
Bound the loop, validate actions, and make its outcome observable.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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