Specifies tooling boundaries, execution in CI, failure triage, reporting, maintenance, and quality gates for AI output testing. It fixes adoption boundaries and observable failure evidence while keeping environment-specific limits configurable.
Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.
Decision
For AI output testing, adopt a versioned risk-based test plan with reproducible fixtures and owned gates. Operate the capability with named signals, bounded capacity, owned alerts, a diagnostic runbook, and tested recovery evidence. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.
Scope
- The AI output testing actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this operations block.
- Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
- Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.
Outside this block
- Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
- Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.
Contract
- Trace each critical test to a behavior, boundary, incident, or risk and assert externally visible outcomes or durable evidence.
- Control clock, randomness, identity, network, and data dependencies so reruns distinguish product defects from fixture instability.
- Keep production-sensitive data and credentials out of fixtures; generate representative synthetic boundary and abuse cases.
- Define retry, quarantine, ownership, expiry, and removal criteria for flaky tests; retries cannot turn a failure into silent success.
- Define normal, degraded, exhausted, and unavailable states with decision metrics and explicit operator actions.
- Preserve accepted-work identity during failover, replay, rollback, and repair so recovery cannot duplicate or silently lose effects.
- Use versioned prompts, models, tools, datasets, rubrics, graders, and sampling configuration and inspect disagreement and uncertainty.
- No single model grader is sole authority for high-impact safety or correctness gates without calibrated human or deterministic evidence.
Implementation guidance
- Practice the runbook in a representative environment and record recovery time, data loss boundary, and unresolved assumptions.
- Model AI output testing inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
- Store the applicable AI output testing contract or policy version with material state so migrations, replay, and support decisions remain attributable.
- Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.
Failure handling and safeguards
- A broken test environment reports an infrastructure outcome distinct from product failure and cannot satisfy a required release gate.
- Invalid or contradictory AI output testing state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
- Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.
Verification and operations
- Inject saturation, dependency outage, stale configuration, and telemetry loss and confirm the declared degraded behavior.
- Monitor AI output testing success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
- Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.
Adoption assumptions
- Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
- The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.
The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.