Autonomous ReAct Loops: Failure Modes and Deterministic Guardrails
The ReAct (Reason + Act) pattern is the default blueprint for multi-step AI agents: think, call a tool, inspect the response, repeat. Giving a language model complete control over its own execution loop works fine for open-ended demos, but it’s notoriously fragile in production. Left unchecked, autonomous loops drift off-course, get trapped in repetitive retry spirals, and exhaust context windows long before solving the actual problem.
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