Evaluate Business Outcomes, Not Fluent Answers

An AI system can produce a convincing answer and still leave the business in the wrong state.

A hotel assistant may confirm a change that never reached the reservation system. A support agent may diagnose an incident correctly but propose a deployment outside its authority. Production evaluation must cover the complete workflow.

Test behavior and outcome

Include expected, incomplete, ambiguous, conflicting, adversarial, unauthorized, and dependency-failure cases. Measure:

• correctly completed workflows;
• correct tool and action selection;
• policy violations and false denials;
• escalation and human-review rates;
• failure, retry, and rollback rates;
• latency; and
• cost per successful workflow.

Independently confirm the final state. For a reservation, inspect the reservation record. For a payment, validate the vendor, amount, evidence, and approval. For generated code, run deterministic tests and independent security checks.

The component proposing an action should not be the only component verifying it.

Prove model flexibility

Calling multiple model APIs does not prove that the workflow can switch safely. Maintain a tested portfolio: a primary model, an economical option for suitable lower-risk work, and a fallback for continuity.

Each approved model must pass the same minimum acceptance criteria for structured output, tool selection, policy compliance, latency, and safe failure. Degraded operation should be explicit: switch models, reduce capability, queue the request, or route it to a person. Never improvise silently.

Gate every material change

Rerun the suite when the model, prompt, retrieval configuration, tool, agent skill, or policy changes. Compare results with the approved baseline and preserve rollback.

Cayru builds workflow evaluations, model-fallback tests, and release gates around the platforms a company already uses.

Baseline one production workflow for correctness, authority, fallback, latency, and cost.