Multi-agent AI systems fail silently. A 200 OK response doesn’t mean the AI made good decisions. That was the biggest thing I realized while building a multi-agent system. My architecture looked like this: User Query → Multi Agent Call → Final Response Everything looked normal from an infrastructure perspective. APIs were healthy Latency looked fine Users were getting responses But I still couldn’t answer important questions: Did the Agent route the query to the right specialist? Did the agent hallucinate information? Did it ignore specialist outputs? Did it attribute responses incorrectly? Traditional monitoring couldn’t help because the system technically wasn’t failing. The failures were happening at the decision layer . Full Trace Visibility I used Langfuse to trace every agent execution. That includes: Tool calls Input/output payloads Token usage Latency per step If an agent touched something, I wanted visibility into it. No black boxes. Deterministic Checks Some validations didn’t need another LLM.…