How to Measure Generative AI and Agent Quality

Define quality as an operational contract

Translate vague goals such as helpful or accurate into observable criteria. For an agent, include whether the task was completed, tools were used correctly, constraints were respected and escalation happened when needed.

Set severity-weighted failure thresholds. A minor formatting error and an unauthorized transaction must not count equally.

Build a representative evaluation set

Collect normal, difficult, ambiguous, adversarial and unanswerable cases from the real workflow. Record expected evidence, required actions and forbidden outcomes rather than relying only on ideal reference prose.

• Common high-volume tasks
• High-value and high-risk edge cases
• Missing or conflicting information
• Prompt injection and unsafe requests
• Cases requiring refusal or human escalation

Measure the system in layers

Evaluate retrieval, generation, tool selection, tool arguments and final task outcome separately. Layered metrics reveal where the system failed and which component should change.

Combine deterministic checks, model-based graders and calibrated human review. Validate automated graders against expert judgments before trusting them.

Create a regression gate

Run the evaluation suite after prompt, model, retrieval, tool or policy changes. Compare quality, safety, latency and cost against the accepted baseline and block releases that breach thresholds.

Monitor production reality

Track task success, overrides, escalations, complaints, incidents, drift, latency and cost. Sample traces for expert review and convert real failures into new regression cases.