The original title is "Using Finite State Machines for deterministic AI agent workflows"

Short summary

The article argues that production AI agents need Finite State Machines to bridge the gap between probabilistic LLM behavior and deterministic business requirements. It contrasts linear LLM chains (which lack error recovery, global context, and loop prevention) with FSM-driven architectures where the LLM decides state transitions while the engine handles execution. Includes an orchestrator pattern diagram and references XState and LangGraph for implementation.

• •FSMs decouple LLM decision logic from execution for predictable, auditable agents
• •Linear chains fail at scale: no error recovery, no global context, infinite loops
• •Orchestrator pattern uses FSM for flow control with sub-agents per state

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