Former Google Engineer Explains How AI Agent Loops, Harness, and Evals Work in 20 Minutes.

The logic is simple: trace each run → pass it through an LLM evaluator → identify failures → fix them → roll out a new version.

This is how agents gradually improve.

• Agent loops: The core mechanism that allows agents to learn from their mistakes.
• Memory: Stores information about previous runs, helping to avoid repeating errors.
• Harness: A tool for testing and integrating agents into various environments.
• Evals: Performance assessment of agents to identify their weaknesses.

Watch on YouTube 😜 Check it out and save this framework.