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APRF-15

Change Management & Release

Repeatable promotion of models, prompts, tools, and agents—with tested, fast rollback.

Purpose

Promote AI artifacts (models, prompts, tools, agents, indexes) through controlled environments, and enable immediate rollback when production behavior regresses.

Engineering philosophy

If you cannot roll back, you cannot safely roll forward. AI change management treats prompts and model pins as release units with the same discipline as application code.

Why it matters

Uncontrolled hot-edits and untested rollbacks create drift, unreproducible incidents, and long mean-time-to-recover when quality or safety collapses after a change.

Common failures

  • Hot-editing production prompts
  • Indexes rebuilt manually with no version tag
  • Tools registered in prod without staging soak
  • Environment config drift for model pins
  • No previous prompt version retained
  • Model pin changed with no path back
  • Schema migrations that break old tool clients irreversibly
  • Rollback untested until an incident

Mandatory checks

Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.

  • DEP-M1L3 · DefinedTier 2 · Productionhybrid

    A promotion path shall exist from non-prod to prod for prompts, models, and tools

    Artifact
    Pipeline/runbook documenting non-prod→prod promotion for prompts, models, and tools
    Pass condition
    100% of production prompt/model/tool releases in the last 30 days flowed through the documented promotion path; 0 production hot-edits without a linked change record
  • DEP-M2L3 · DefinedTier 2 · Productionhybrid

    Production changes shall be recorded (who/what/when) and linked to reviews

    Artifact
    Change log or ticket export for AI artifact releases
    Pass condition
    100% of production AI artifact changes in the last 30 days have who/what/when fields and a review link (PR, ticket, or approval ID)
  • DEP-M3L3 · DefinedTier 2 · Productionautomated

    Infrastructure and AI config shall be defined as code or equivalent declarative config

    Artifact
    IaC/config repo paths for AI gateway, model pins, tool catalogs, and prompts
    Pass condition
    Drift check shows 0 unmanaged production AI config resources outside declarative sources; sample of live pins matches declared versions at 100%
  • CHG-M1L3 · DefinedTier 2 · Productionhybrid

    Prior production versions of prompts and model pins shall be retained and restorable

    Artifact
    Version retention policy + registry listing of prior production versions
    Pass condition
    ≥N prior production versions retained per policy (minimum N=2); restore dry-run successfully loads the immediate prior version in staging or prod-adjacent env
  • CHG-M2L3 · DefinedTier 2 · Productionmanual

    Rollback procedure shall be documented and operable by on-call

    Artifact
    Rollback runbook + on-call acknowledgment or drill checklist
    Pass condition
    Runbook lists exact commands/UI steps and owners; ≥1 on-call engineer completed a walkthrough or drill in the last 90 days with recorded time-to-execute
  • CHG-M3L3 · DefinedTier 2 · Productionhybrid

    Rollback shall be tested in a drill or real event on a defined cadence

    Artifact
    Drill or incident record with timestamps and outcome
    Pass condition
    ≥1 successful rollback (drill or real) in the last 90 days with measured time-to-restore ≤ documented RTO
  • CHG-M4L5 · OptimizingTier 3 · Mission Criticalhybrid

    Automated rollback shall trigger on quality SLO burn for AI releases

    Artifact
    Rollback automation config + sample trigger/test evidence tied to quality SLOs
    Pass condition
    PASS if quality SLO burn is wired to automated rollback (or page+runbook with measured MTTA) and a test/drill occurred ≤90 days

Evidence required

  • Deployment pipeline documentation
  • Sample change records
  • Environment inventory
  • Rollback runbook
  • Evidence of tested rollback
  • Version retention policy

Severity & risk

Severity
critical
Impact if violated
Risk level
high
Typical residual risk (impact × likelihood)

Engineering best practices

  • Bundle related prompt/model/tool versions as a release unit when they interact
  • Keep staging data representative without copying unnecessary production secrets
  • Automate smoke tests post-deploy including one eval canary
  • Document freeze windows for peak business periods
  • Keep rollback independent of full application redeploy when possible
  • Version tool schemas with compatibility windows
  • Avoid one-way data migrations tied to prompt experiments
  • Measure time-to-rollback as an operational KPI

Automatic validations

  • Pipeline gates requiring eval and security checks
  • Drift detection between declared and live config
  • Post-deploy smoke test automation
  • CI verifying previous artifacts remain fetchable
  • Synthetic rollback dry-runs in staging
  • Alerts recommending rollback on quality burn

Manual validations

  • Release readiness review for major agent launches
  • Spot audits of console-only changes
  • On-call drills executing rollback
  • Post-incident verification that rollback path was used or improved

Examples

  • A release train ships prompt v12 + model pin + tool schema together after staging soak
  • Index rebuilds produce a new version ID switched via config flip
  • Quality drop after a prompt release is reversed in under five minutes via registry rollback
  • A new tool schema is dual-published so clients can fall back

References

Crosswalks

  • MANAGE Manage

    NIST AI Risk Management Framework · supports

  • Accountable Accountable and Transparent

    NIST AI Risk Management Framework · supports

  • §8 Operation

    ISO/IEC 42001 · supports

  • §10 Improvement

    ISO/IEC 42001 · aligns-with

  • CC8 Change Management

    SOC 2 Trust Services Criteria · evidence-for

  • PI1 Processing Integrity

    SOC 2 Trust Services Criteria · evidence-for

  • Operational Excellence Operational Excellence

    AWS Well-Architected Framework · aligns-with

  • Level 1 Build process documented

    SLSA (Supply-chain Levels for Software Artifacts) · partial

  • Level 2 Hosted build + signed provenance

    SLSA (Supply-chain Levels for Software Artifacts) · aligns-with

  • Verify-on-deploy Verification before use

    SLSA (Supply-chain Levels for Software Artifacts) · supports

Future evolution

Unified release manifests covering prompts, models, tools, eval attestations, and automatic quality-triggered rollbacks.