Observability

What is observability?

Observability is the ability to understand and diagnose an AI agent's behavior from its execution signals. These signals can include traces, logs, metrics, events, model usage, tool outcomes, costs, and task results.

The goal is to answer questions about internal behavior without reproducing every failure manually.

What agent observability covers

A useful observability system can show:

• which agents, models, and tools ran;
• how requests moved through a workflow;
• where latency and cost accumulated;
• which retries or handoffs occurred;
• what errors and guardrails triggered;
• whether state transitions completed;
• how success and failure rates change over time.

For example, a rise in incomplete support tasks may be traced to a changed retrieval filter rather than the language model itself.

Observability versus evaluation

Observability provides evidence about what happened. Evaluation judges whether that behavior met defined quality criteria.

A dashboard may show that tool latency increased; an evaluation determines whether the delay caused unacceptable task performance. Evaluation can use observability data, but the two serve different purposes.

Observability versus monitoring

Monitoring tracks known signals and alerts on expected failure conditions, such as error rate or latency thresholds. Observability supports deeper investigation of unexpected behavior by connecting multiple signals and execution context.

Monitoring is part of an observability practice, not a complete substitute.

Design and privacy

Agent inputs and outputs may contain personal data, confidential documents, or credentials. Observability should redact sensitive content, control access, and define retention. Teams need enough detail to diagnose problems without copying every secret into telemetry.

Stable task IDs and trace relationships are essential. Without them, model calls, tool results, and final outcomes cannot be connected.

Why it matters

Agent behavior is probabilistic and distributed across models, tools, memory, and workflows. Observability makes that behavior inspectable in development and production.

It does not guarantee quality by itself. The practical value comes from connecting signals to evaluation, incident response, and design changes so teams can improve reliability based on evidence rather than guesswork.