The Reliability Problem That Forced Us to Rethink AI Agents

Short summary

Production AI agents fail differently than demos—tools drift, timeouts cause duplicates, retries mask logic errors. The authors solved this by breaking reliability into distinct engineering concerns: narrower tool definitions, schema validation, idempotent operations, state persistence, approval gates, and regression testing. Logging and tracing every step made failures visible and recoverable.

• •Production AI agents have reliability problems demos don't reveal—timeouts, partial failures, and retry loops cause silent corruption
• •Solutions: narrow tool definitions with strict schemas, idempotent operations with circuit breakers, and explicit approval gates for risky actions
• •Permanent regression testing and comprehensive logging turned mysterious failures into debuggable, recoverable events

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