잡동사니

Lessons from building reliable AI agent workflows with Hermes and local LLMsTL;DRMulti-agent systems become unstable when responsibilities overlapStronger models do not automatically improve workflow convergenceShared context without ownership boundaries creates execution driftSeparating Planner, Implementer, and Validator responsibilities significantly improved workflow stabilityThe Implementer..

From Unpredictable AI to Reliable SystemsLessons from building real-world AI agent systems📌 TL;DRPrompt engineering alone was never enough for stable executionSingle-prompt systems created over-exploration, scope drift, and unreliable outputsI redesigned the system into Planner → Implementer → ValidatorReliability came from contracts, validation, and retry loops—not better promptsProduction rel..