Agent observability: The complete guide for

Agent observability is a practice that tracks every action an AI agent takes during execution, such as tool selection, model responses, and memory operations, providing a structured trace to understand the agent's behavior. Unlike traditional Application Performance Management (APM) systems, which measure request rate, latency, and error rate, agent observability captures semantic behavior, revealing issues like tool misselection or plan drift that APM cannot detect. This approach uses a schema with four span types—tool calls, reasoning steps, state transitions, and memory operations—to make failure modes visible and connects these traces to evaluation processes, ensuring continuous quality improvement. Braintrust offers a platform that integrates tracing, evaluation, and release enforcement into one workflow, enabling teams to monitor real agent runs, score live production traces, and convert failures into evaluation cases, thereby preventing regressions in continuous integration (CI) pipelines. The platform supports multiple frameworks and provides native adapters and OpenTelemetry instrumentation to ensure compatibility across various agent frameworks, with a free tier offering ample resources for initial implementation.