Observability for AI Agents

Distinguish Observability from Monitoring: Explain why traditional monitoring cannot catch the silent, non-deterministic failures unique to agentic AI systems, and identify the four failure categories — agent reasoning, tool, context, and infrastructure — that observability is designed to surface.

Apply the Three Pillars of Observability: Use logs, metrics, and traces together to detect anomalies, investigate incidents, and reconstruct the full causal chain of a multi-step agent workflow — including silent errors that compound across steps without throwing exceptions.

Evaluate Observability Architecture and Trade-offs: Describe the four-layer observability control plane, assess sampling strategies and alert calibration challenges specific to agents, and identify what a production-ready platform must provide to close the gaps left by today's fragmented tooling.