Agent state machines

Represent agent progress as explicit states and transitions.

Lesson overview

Model-driven loops become easier to reason about when states are explicit.

Typical states include received, planning, awaiting_tool, executing, awaiting_approval, evaluating, completed, and failed.

Persist state for long-running work so a process restart does not lose the workflow.

Learning path

Theory → Example → Code → Practice → Quiz → Challenge → Completion

0/6 done

Step 1

Model-driven loops become easier to reason about when states are explicit.

Typical states include received, planning, awaiting_tool, executing, awaiting_approval, evaluating, completed, and failed.

Persist state for long-running work so a process restart does not lose the workflow.

Step 2

Apply Agent state machines to a realistic production scenario and trace the decision step by step.

Step 3

Add implementation notes or a runnable example for this concept.

Step 4

Write down the inputs, expected output, constraints, and one failure case for Agent state machines.

Step 5

Quiz coming soon.

Step 6

Design a production-ready solution for Agent state machines and explain one important trade-off.

Work through every step in order, then the lesson will be marked complete.

Each chapter and subtopic has its own public URL under /ai-agent.