Building Reliable AI Agents: Lessons from Failed Bots, FSMs, and the Hidden Costs of Agentic Workflows

Short summary

A deep-dive into why naive ReAct-style AI agents fail in production due to hallucination loops, infinite recursion, and unbounded context growth. The author argues that Finite State Machines (FSMs) should govern LLMs—handling logic, state validation, and transitions—while the LLM handles reasoning. This separation of concerns prevents catastrophic failure modes and controls the hidden costs of agentic workflows.

• •Naive ReAct agents fail via hallucination loops, infinite recursion, and context overflow
• •FSMs should govern LLMs: FSM handles logic and state, LLM handles reasoning
• •Orchestrator pattern centralizes workflow control, invoking LLM only when needed

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