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State Machines for Agents

Build reliable, predictable agent behavior with finite state machines

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Real-World Examples

State machines power real-world systems across domains. From chatbots to game AI, order processing to autonomous agents, state machines provide structure and reliability. Let's explore practical applications and see them in action.

Interactive: State Machine Explorer

Select an example to see its state machine structure. Click available transitions to watch the system respond.

💬

Customer Service Chatbot

Handles customer inquiries with automatic escalation

LISTENING
UNDERSTANDING
RESPONDING
ESCALATING
RESOLVED
CURRENT STATE: INITIAL
Available transitions:
No transitions (final state)
SIMULATION LOG:

When to Use State Machines

✓
Discrete Phases
Clear, distinct stages in a process
✓
Event-Driven Logic
Behavior changes based on events
✓
Predictable Behavior
Need deterministic, testable logic
✓
Complex Workflows
Multiple paths with conditional branching

Implementation Libraries

XState (JavaScript/TS)
Industry standard with visual tools
npm install xstate
python-statemachine
Pythonic state machine library
pip install python-statemachine
Roll Your Own
Simple state machines can be custom-built

Best Practices

Design Time

  • • Start with a diagram before coding
  • • Keep states at the same level of abstraction
  • • Name states after what agent is doing, not what happened
  • • Document guard conditions clearly

Runtime

  • • Log state transitions for debugging
  • • Handle all possible events in every state
  • • Use timeouts to prevent stuck states
  • • Test all paths through the state machine

💡 Key Insight

State machines excel at modeling workflows, not algorithms. If your problem involves sequential phases, event handling, or modal behavior, a state machine is likely a great fit. If your problem is more about computation or data transformation, other patterns may be better. The best engineers know when to use state machines—and when not to.

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