Agent Observability

Purpose

This skill provides the complete knowledge required to build production-grade observability systems for AI agent deployments. Unlike traditional software where execution paths are deterministic and debuggable with standard tools, agent systems exhibit non-deterministic reasoning loops, branching decision trees, variable-length tool call chains, and stochastic output variations that demand specialized observability infrastructure.

The skill covers every dimension of agent observability: capturing and visualizing reasoning traces that show why an agent made each decision, integrating OpenTelemetry to instrument every LLM call and tool invocation, propagating distributed trace context across multi-agent chains, maintaining tamper-evident decision audit logs, profiling performance bottlenecks in agent pipelines, detecting anomalous behavior patterns that indicate drift or failure, tracking costs across model providers and optimizing token usage, and analyzing latency to identify and eliminate bottlenecks in the agent execution critical path.