Debugging Multi Agent Memory Loss in Long Running Pipelines

Long-running AI agents often experience "Agentic Amnesia," losing their train of thought over extended execution windows. This failure occurs because standard frameworks rely on naive context truncation and lossy LLM-driven summaries that delete critical historical details. We address this bottleneck by decoupling memory from the active model context window and implementing a Tri-Tier Memory Architecture. By isolating ephemeral working scratchpads from immutable event ledgers and structured stat...