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Core•8–12 hours

AI Serving Release Envelope Lab

Build a governed release contract for an AI runtime: exact model/config fingerprint, readiness checks, bounded retries, canary, rollback, and runtime verification instead of assuming that green CI already means production truth.

AI servingrelease envelopesruntime verificationcanary rolloutrollbackreliability engineering

Scenario

Task

The team changes a model, prompt, retrieval index, or tool configuration and gets green CI. Production may still run another model revision, a stale prompt, an incomplete index, or old tool scopes. Build a release envelope that binds the verified candidate to the actual runtime and proves that canary and production execute the tested configuration.

Step-by-step execution

1. Define a release unit broader than a git commit

Outcome: The team can unambiguously identify the behavioral configuration that was actually verified.

Tasks

  • →Collect the code/model/prompt/retrieval/tool/policy/dependency fingerprint
  • →Record immutable or resolvable version identifiers
  • →Assign an owner to every mutable component
  • →Reject ambiguous aliases in production evidence unless resolved to an exact version

Checks

  • ✓One approved envelope maps to one reproducible configuration
  • ✓Changing a model alias or retrieval index changes the fingerprint
  • ✓Secrets never enter the evidence artifact

2. Build the readiness and failure contract

Outcome: The runtime does not accept traffic while a critical dependency or policy state is not ready.

Tasks

  • →Separate liveness from readiness
  • →Set timeout, cancellation, and bounded retry rules
  • →Add provider/retrieval/tool dependency health
  • →Define degraded modes and stop conditions

Checks

  • ✓A readiness failure is not masked by liveness success
  • ✓The retry budget cannot create a retry storm
  • ✓Degraded mode never expands authority or data scope

3. Run a staged rollout

Outcome: The candidate receives bounded production exposure before full promotion.

Tasks

  • →Run shadow or replay where possible
  • →Allocate a canary cohort
  • →Compare runtime quality, reliability, and cost signals with baseline
  • →Record an explicit promote/hold/rollback verdict

Checks

  • ✓A critical safety or authority regression blocks promotion regardless of aggregate score
  • ✓The canary observation window matches the declared risk class
  • ✓The rollback target is verified before rollout

4. Prove runtime truth

Outcome: Production status is confirmed by runtime evidence rather than pipeline completion.

Tasks

  • →Read the actual runtime fingerprint
  • →Compare it with the approved envelope
  • →Simulate a stale model/prompt/index mismatch
  • →Store a deployment verification artifact

Checks

  • ✓A mismatch ends in FAIL or UNKNOWN, never PASS
  • ✓CI PASS does not replace deploy verification
  • ✓Rollback is also confirmed by the actual runtime fingerprint

Acceptance criteria

  • The release envelope versions code, model, prompt, retrieval, tools, policies, dependencies, and the eval dataset
  • Readiness checks critical AI dependencies and has bounded timeout/retry/cancellation policy
  • The canary has explicit promote/hold/rollback criteria and a known-good rollback target
  • Production verification machine-compares the actual runtime fingerprint with the approved envelope
  • A stale or mismatched runtime configuration cannot receive PASS