Learn Loop Engineering

Build reliable observe, act, and learn cycles for AI systems.

Start learning 6 lessons · about 1 hours · free
About this track

Learn to design multi-step AI agent loops with explicit state, well-scoped tools, stopping conditions, recovery paths, end-to-end evaluation, and human oversight. This track uses loop engineering to mean the application design around repeated model decisions and actions.

Before you start
  • Basic familiarity with AI assistants is helpful.
  • Basic programming concepts are helpful but not required.
  • A modern browser with JavaScript enabled.
  1. Unit 1 · 0/2 lessons

    Loop foundations

    Model the agent cycle and preserve useful progress state. Badge: Progress keeper

    1. 0x001Model an agent loopTrace a task through observation, decision, action, and feedback. 12 min 12 min
    2. 0x102Track state and progressKeep a compact record of goals, completed work, and the next useful step. 12 min 12 min
  2. Unit 2 · 0/3 lessons

    Loop control and quality

    Design tool contracts, recovery, and end-to-end evaluation. Badge: Reliable operator

    1. 0x203Design tools and action contractsGive each action a narrow purpose, validated inputs, and a clear result. 12 min 12 min
    2. 0x304Control stopping, retries, and recoveryPrevent runaway loops and handle timeouts, errors, and repeated failures. 12 min 12 min
    3. 0x405Evaluate the whole loopMeasure task outcomes and diagnose failures across model, tools, and control logic. 12 min 12 min
  3. Unit 3 · 0/1 lessons

    Loop safety and oversight

    Apply least privilege and keep people in control. Badge: Safety steward

    1. 0x506Build safety and oversight into the loopUse least privilege, explicit approval, and untrusted-data boundaries. 12 min 12 min
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