AI Agent Evaluation Metrics That Explain Quality

Behavioral and grounding metrics

Behavioral quality asks whether the agent did what the scenario required. Grounding asks whether the response stayed within the facts, policies, and context available to it.

Examples include:

• Scenario success: did the agent achieve the customer's intended outcome?
• Grounded response: did it avoid unsupported claims or invented actions?
• Completeness: did it include the required next step or limitation?
• Tone and policy fit: did it communicate in the approved style without hiding important constraints?

These are usually semantic checks. They need a precise scenario and criteria that a reviewer can understand. “Be helpful” is difficult to interpret consistently; “explain the cancellation steps and do not claim the account is already cancelled” is testable.

Tool and routing metrics

An agent may produce a plausible answer after taking the wrong path. Track the execution decisions that make the answer trustworthy:

• Route or specialist selected.
• Required tool called or deliberately not called.
• Tool arguments match the account, product, or request.
• Tool result is reflected accurately in the response.
• Required span attributes are present.

These are procedural checks. They depend on instrumentation, so a missing field should be treated as a visibility problem rather than silently counted as a pass.

Response and protocol metrics

Response checks cover facts that can be compared directly:

• HTTP status or application status.
• Required response fields or schema shape.
• Empty or malformed output.
• Safety or policy markers that must be present.

Protocol success is necessary but not sufficient. A 200 response can still contain an unsafe tool decision or a false claim, which is why response checks should sit beside behavioral checks.

Latency and token metrics

Latency and token usage help explain user experience and operating cost. Measure them at the stage where they matter: total interaction time, model duration, tool duration, input tokens, and output tokens when those values are available in the trace.

Use these metrics to find a trade-off, not to declare quality by themselves. A faster answer that chooses the wrong tool is not a successful optimization. A longer answer may be acceptable when it prevents an unsupported action, but a repeated latency regression still deserves investigation.

Build a metric set around decisions

Start with the decisions your team needs to make:

1. Can we ship this agent change?
2. Did a production interaction regress after release?
3. Which stage of the agent path needs a fix?
4. Did the fix improve quality without creating a latency or cost problem?

Map each decision to one or more checks. Use a semantic metric for the outcome, a procedural metric for the path, and a response or performance metric for the operational constraint. Keep the scenario and trace attached so a score remains explainable.

Interpret scores with their limits

Metrics are evidence, not a substitute for judgment. A score can hide which scenarios ran, whether the trace had complete instrumentation, or whether the test was relevant to the interaction. Compare like with like, review the underlying evidence, and keep an explicit note when a metric depends on a product policy or a data field your system may not always emit.

Mibo can evaluate the same definitions actively before release and passively against real traces after release. Read AI agent evaluation for the product workflow, or run a live reliability check to see passive testing in action.

For trace attributes and assertion behavior, use the Mibo assertion reference and the OpenTelemetry ingestion guide.