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Why Your AI Agent Fails at Observability: A Debugging Framework for Memory, Tool Calls, and RAG

Originally published on tamiz.pro . You built the agent. The prompt looks solid. The RAG pipeline is technically "working" because your vector DB returns results. But when you watch the agent in production, it stalls. It hallucinates. It loops on tool calls it shouldn't be making. It forgets context from ten turns ago.

  • Agent failures often stem from opaque black box nature, not the LLM itself
  • Need semantic tracing to understand causal chain of tool calls and memory ops
  • Three pillars of observability: Memory State, Tool Execution, Retrieval Validity

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