Research Report 7.3: Observability & Debugging
Traditional monitoring tells you a server is down - LLM observability must tell you that your agent is confidently generating wrong answers and nobody noticed
Research into monitoring, debugging, and troubleshooting LLM orchestration systems - covering structured logging, distributed tracing, metrics collection, anomaly detection, and the unique challenges of observing probabilistic systems where failures look like normal outputs.
Also connected to
Documentation on claude 23 research report 7.3! observability & debugging
If each step in your AI pipeline is 90% accurate, a ten-step chain drops to 35% reliability - and most teams don't realize this until production
The unified framework that production-grade agent platforms use to make context work at scale
Documentation on claude 20 research report 6.4! error propagation & resilience
Documentation on claude 22 research report 7.2! performance & optimization