The Agent Loop: full course guide
Unlike simple chatbots, Agentic AI can think about its mistakes. Implement a Python-based agent loop where an LLM creates a plan, executes a step, checks the result, and pivots if the outcome is wrong.
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What is inside this guide
01The Agent Loop Explained
- Chatbot vs Agent: Why Planning Changes Everything
- The Perceive → Plan → Act → Reflect Cycle
- Structured Output for Plan Generation (JSON Mode)
- Step-by-Step vs Parallel Execution Strategies
- When to Stop: Success Conditions and Max Iterations
02Execution & Observation
- Executing a Plan Step with Tool Calls
- Capturing and Parsing the Execution Result
- Comparing Expected vs Actual Outcome
- Injecting Observation Back into the LLM Context
- Handling Partial Success and Ambiguous Results
03Self-Correction & Pivoting
- Detecting Failure: Heuristics vs LLM-as-Judge
- Generating a Corrected Plan on Failure
- Limiting Re-Plan Depth to Prevent Infinite Loops
- Logging the Full Decision Trace for Debugging
- Capstone: Python Agent Loop That Self-Corrects on Wrong API Output
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