Observability for AI agents shares the three-pillar model from distributed systems (logs, metrics, traces) but adds agent-specific dimensions: the prompt that initiated the agent, the tools it called and in what order, the reasoning it produced (if visible), the tokens consumed, and the outcome quality relative to the task.
Without observability, debugging agent failures is guesswork. When an agent produces incorrect code, you need to know: what prompt did it receive, what context did it retrieve, which tool calls did it make, where did its reasoning diverge from correct? Structured logs that capture this data make post-hoc debugging possible.
Agent observability also enables performance tracking over time. An agent's accuracy and efficiency can degrade when the codebase changes, when the prompt is modified, or when the underlying model is updated. Continuous monitoring catches regressions before they accumulate into significant problems.