AI agents need lightweight event-driven harnesses rather than heavyweight frameworks. A harness (like Utah/Inngest) decomposes agent tasks into independently retryable and observable steps, handling retries, state persistence, and job queues automatically. This contrasts with monolithic agent frameworks that treat the entire agent execution as a single unit. The step-level granularity enables better debugging (observe each step independently), resilience (retry failed steps without rerunning the entire chain), and composability (sub-agents as steps within larger workflows). ## Source - **Author**: Dan Farrelly (Inngest.com) - **Source**: [[Your Agent Needs a Harness, Not a Framework]] - **Date**: 2026-03-02 ## Cross-References - [[Systems Engineering Discipline as Autonomous Agent Reliability Foundation]] - [[Dynamic Trust Calibration as Agent Reliability Mechanism]] - [[Autonomous Agent Reliability Infrastructure]]