How to read these artifacts

Artifacts are evidence of decision quality, not decoration.

Each artifact states its purpose, intended user, supported decision, release stage, status, and relationship to offers, methods, and cases. The examples are representative unless labeled otherwise.

Representative artifacts

Explore representative artifacts used to support release decisions.

Choose workflow and establish owners.

Release blockage map

Representative example

Purpose: Identify the unresolved decision, dependency, evidence gap, or ownership problem preventing release.

Intended user: Executive sponsor, product leader, technical leader.

Decision supported: Where progress is stopping and who owns the unresolved decisions.

Readable preview

FieldExample
WorkflowAI-assisted intake summary for an operations team.
Observed stallDemos are accepted, but release review keeps reopening evaluation and ownership questions.
Primary blockageNo accountable business owner can approve the release criteria.
Evidence gapFailure cases for sensitive intake notes are missing.
Next decisionName the business owner and define escalation cases before expanding the build.
View the AI Release Audit

Define what good looks like.

Release-readiness scorecard

Representative example

Purpose: Determine whether workflow definition, ownership, evaluation, routing, controls, and operational readiness are sufficient for a controlled release.

Intended user: Sponsor and delivery team.

Decision supported: Whether release risk is decreasing.

Readable preview

FieldExample
WorkflowDefined: internal claims triage summary for operations users.
OwnershipBusiness owner named; technical owner named; escalation owner missing.
EvaluationTwenty routine cases pass; sensitive and adversarial cases incomplete.
Risk routeLow-risk summaries can proceed; uncertain or sensitive cases route to review.
Release decisionNot ready for broad release. Ready for controlled internal trial after escalation owner and failure cases are complete.
View the AI Release Audit

Route actions by risk, confidence, and consequence.

Risk-routing matrix

Representative example

Purpose: Define which actions may proceed automatically and which require review, approval, escalation, containment, or termination.

Intended user: Product, engineering, operations, risk reviewers.

Decision supported: Which AI actions move, pause, route to review, or stop.

Readable preview

FieldExample
AutomaticLow risk, high confidence, reversible.
ApprovalMaterial consequence or sensitive action.
StopUnauthorized data/tool/action or unsafe behavior.
Explore Risk-Based Routing

Test realistic success and failure cases.

AI evaluation scorecard

Representative example

Purpose: Test whether the workflow performs the intended work, handles meaningful failures, follows policy, and escalates appropriately.

Intended user: Technical owner, evaluator, sponsor.

Decision supported: Whether the workflow has enough evidence to release.

Readable preview

FieldExample
Task successPass/fail with severity.
GroundednessEvidence source and unsupported assertions.
EscalationCorrect refusal or human handoff behavior.
Explore AI Agent Evaluation

Put human judgment where it matters.

Human approval design

Representative example

Purpose: Define where human authority is required, what evidence the reviewer sees, and how approval, rejection, timeout, and escalation work.

Intended user: Reviewer, approver, operations leader.

Decision supported: Where human judgment is necessary and what evidence the person sees.

Readable preview

FieldExample
Approval authorityNamed role can approve, reject, or escalate.
Evidence shownInput, output, route class, confidence, risk notes.
Audit trailDecision, rationale, approver, time.
Explore Human Approval Gates

Release with evidence and clear ownership.

Ownership and operating handoff

Representative example

Purpose: Establish who owns the released workflow, how it is monitored, and how incidents, rollback, support, and improvement are handled.

Intended user: Operational owner, technical owner, support lead.

Decision supported: Who owns the released workflow and how failures are handled.

Readable preview

FieldExample
OwnerNamed business and technical owners.
MonitoringSignals, thresholds, and review cadence.
RollbackStop, revert, communicate, and learn.
View the AI Delivery Pilot

Measure, learn, and update.

Release-metrics dashboard

Representative example

Purpose: Track whether delivery is becoming faster and more dependable through measures such as readiness, evaluation pass rate, approval latency, intervention rate, incident rate, release time, and operating outcomes.

Intended user: Executive sponsor and delivery leader.

Decision supported: Whether AI delivery is becoming faster and more dependable.

Readable preview

FieldExample
ReadinessWorkflows meeting criteria.
Escape rateFailures that reach production users/operators.
Learning closureProduction lessons converted into updates.
Explore AI Release Metrics