evaluation and governance
Evaluation and governance connect policy to behavior. We define the operating boundary, run repeatable cases, record failures, and give the AI owner evidence for the release, revision, or stop decision.
evaluation and governance
A policy says what should happen. Evaluation shows what the workflow actually does when the input is incomplete, hostile, unusual, or changed by a new model. The CAIO needs both.
We turn expected behavior, prohibited behavior, and human review boundaries into repeatable cases. Each run produces evidence for a release, revision, or stop decision.
$ sample eval-suite --release-gate
scope: expected · prohibited · human-review
result: failures attached to source cases
owner review: required before release
decision: release, revise, or stop
what the evidence covers
Test the missing-fact and conflicting-source cases where a plausible answer would still be wrong.
Run the same cases after a prompt, model, tool, or policy change and attach the difference to the release decision.
Check arguments, permissions, skipped calls, duplicate calls, and the conditions that require a person.
Exercise instruction conflicts, hidden requests, data-exposure attempts, and actions the workflow must refuse.
Record empty, malformed, multilingual, and exception-heavy inputs instead of treating the happy path as coverage.
Keep the same operating cases over time so the monthly scorecard shows the change and the evidence behind it.
how evaluation runs
01
The operating owner and CAIO agree on what passing means, what must be refused, and when a person takes over.
02
Cases cover the ordinary workflow, known failures, boundary conditions, and adversarial inputs relevant to the real use.
03
Every material change returns a result, failing cases, and an owner decision. The scorecard can point to evidence instead of a feeling.
No. The system can come from a vendor, another builder, or your existing stack. We need a permitted way to run it and enough approved examples to define the operating boundary.
No. A number without the case set hides what failed and why it matters. We keep the failing cases, the affected behavior, and the owner decision beside the summary.
A one-off evaluation setup can fit a $1,500 to $2,500 starter build or a $5,000 production sprint, both fixed, depending on scope. Ongoing evaluation can sit inside CAIO Core or CAIO + Delivery after the ownership decision.
Only the access needed to run the agreed cases and inspect the result. Sensitive examples stay subject to the company's approved data handling, and the decision log records the boundary.
The free AI ownership assessment starts with six questions. CAIO-qualified companies can then use an ownership-fit working session to decide where evaluation and governance belong.
It is not a free full roadmap or week-long audit.
