AI Observability: Monitoring Agent Failures in Production

Teams that ship AI-powered features often discover, six months in, that their observability stack was designed for a different kind of software. Traditional monitoring tells you when a service returns a five-hundred, when latency spikes, when a queue backs up. These signals are still necessary for AI workloads. They are no longer sufficient. The distinctive failure modes of production AI systems — silent regressions, confident wrong answers, cost blowouts from a single looping agent, drift intro