Explore TeamStation AI
Open the capacity planner | Book strategy call

TeamStation AI / Executive decisions

AI delivery governance makes releases accountable

AI delivery governance turns policy into checks on the work being shipped. It binds evaluations, approval, deployment, and monitoring to the exact version of a system, rather than a result from an earlier build.

Review the decision guide
At a glance
  1. Freeze the candidate

    Identify the code, prompts, model, tools and data configuration.

  2. Evaluate and review

    Test expected behavior, regressions and denied actions.

  3. Release and verify

    Check the deployed version and monitor the agreed boundaries.

How it works

Evidence must follow the release candidate

Scroll sideways to see the whole diagram. The same steps are written out below.

Evidence must follow the release candidate
  1. Freeze the candidate

    Identify the code, prompts, model, tools and data configuration.

  2. Evaluation

    Test expected behavior, regressions and denied actions.

    Evaluate and review: Candidate version, test set, environment, and observed failures.
  3. Review

    Reviewer and exact reviewed change.

  4. Release

    Require an authorized release decision on that exact candidate and a tested recovery path.

    Release owner
  5. Production readback

    Check the deployed version and monitor the agreed boundaries.

    Release and verify: Observed deployed version and key operating checks.
Return for review

Hold the release if required tests are missing or review no longer matches the candidate.

Evaluation results
Resolve failures or record an authorized scope reduction.
Review
Re-review when relevant content changes.
Recovery check
Do not release without a usable recovery path.

Known recovery target and conditions for using it.

Production readback
Investigate mismatch before calling the release complete.

The problem

A passing demo or an old review can't validate changed code, prompts, tools, or data. A messy PR history makes that harder: release evidence must match the actual candidate.

Decide what must pass before release

Evaluation

Test intended behavior, denied actions, and important failure cases.

Owner: Evaluation and domain owners

Release

Match review and test evidence to the exact candidate.

Owner: Release owner

Operation

Define monitoring, rollback conditions, and escalation ownership.

Owner: Service owner

Release checks need time and people, so include evaluation and recovery work in the capacity plan. Reserve review and operating capacity.

Illustrative scenario / not a client result

The prompt changed after the review passed

An AI service passed evaluation, then a prompt and retrieval setting changed before deployment. The previous report describes a different candidate. The change might be small in code, yet it can alter which documents are retrieved, how an answer is formed or when a tool is called.

Re-run affected checks
Trace the changed behavior and repeat its evaluation plus the relevant regressions.
Reduce the release scope
Leave the unverified behavior out of the candidate with a clear, authorized scope decision.
Hold the release
Stop when critical failures, stale review or an untested recovery path remain unresolved.

The decision: Record the candidate identity with each test and review. After deployment, read back the actual version and exercise the critical flow. A successful build or upload doesn't establish that users received the reviewed behavior.

Put the plan into practice

  1. Step 1Freeze the intended behavior and failure boundaries for the candidate.
  2. Step 2Run representative evaluations and regressions, recording the version and limitations.
  3. Step 3Require an authorized release decision on that exact candidate and a tested recovery path.
  4. Step 4Verify the deployed version and monitor business exceptions as well as technical failures.

The architecture determines which dependencies and failure paths a release must exercise. Trace the production request path.

Release evidence checklist

NIST calls for documented test sets, metrics, and evaluation tools. NIST AI RMF 1.0 (2023), MEASURE 2.1.

TeamStation planning worksheet informed by the cited reference, not a customer result, certification, or completed assessment.

  1. Release artifactEvaluation results
    Required bindingCandidate version, test set, environment, and observed failures.
    Failure actionResolve failures or record an authorized scope reduction.
  2. Release artifactReview
    Required bindingReviewer and exact reviewed change.
    Failure actionRe-review when relevant content changes.
  3. Release artifactRecovery check
    Required bindingKnown recovery target and conditions for using it.
    Failure actionDo not release without a usable recovery path.
  4. Release artifactProduction readback
    Required bindingObserved deployed version and key operating checks.
    Failure actionInvestigate mismatch before calling the release complete.

What this does not establish

Passing a finite test set does not prove that all future behavior is safe. Preserve known limits and monitor conditions not represented in the evaluation.

Questions before you commit

When does an AI release need a new review?

When a change affects the reviewed behavior, data, permissions, model, prompt, tools, or operating environment. Evidence must match the candidate being released.

Are model evaluations enough to approve a release?

No. Check integrations, access controls, user workflows, operating behavior, recovery, and the exact deployed version as well as model outputs.

From planning to delivery

What to scope with TeamStation

We can scope the engineering work behind repeatable release checks and operational handover. Start with your current delivery pipeline and its known failure modes.

  • Version-bound evaluation and regression checks.
  • Explicit review, release and recovery ownership.
  • Production verification and monitoring tasks in the delivery backlog.

No finite test suite proves unrestricted safety. Keep known gaps visible, and do not describe a planning checklist as an independently completed audit.

Discuss your delivery scope

Your next decision

Bind AI evaluations, review, release approval and recovery to the exact version shipped, with clear owners and production readback.

Confirm who may accept remaining risk