How to Build Deterministic AI Agent Loops Using Finite State Machines in TypeScript

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Software engineers building production AI agents frequently encounter failures such as infinite loops, hallucinated arguments, and costly unvalidated state mutations that work in demos but break in real deployments. A technical guide published on DEV Community proposes replacing naive LLM agent loops with a Finite State Machine architecture featuring isolated verification gates and event-sourced replay. The approach introduces a four-phase execution model called Observe, Propose, Verify, and Commit, which decouples LLM planning from deterministic runtime validation before any tool is executed. Each phase is enforced through typed contracts using Zod schemas and TypeScript, ensuring that invalid state transitions are caught and logged before they cause side effects. The architecture aims to make AI agent runtimes production-ready by preventing context pollution, non-idempotent mutations, and unbounded retry loops that drain API credits.

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