Skip to content

Execution plan for major upgrades

Run this plan in sequence and ship concrete outputs in every readiness stage.

Program guardrails

  • Keep the decision backbone fixed: gate fast -> gate release -> doctor.
  • Keep tier boundaries clear: Public/stable first, advanced/supporting next, transition/legacy secondary.
  • Ship measurable deltas in each phase (artifacts, KPIs, contracts, docs updates).

Baseline readiness — Build the baseline evidence lane

Mission

Stand up a baseline evidence lane that always produces machine-readable evidence and immediately flags environment/tooling drift.

Do this

  • Lock environment contracts (Python/toolchain expectations).
  • Automate the baseline lane and capture artifacts.
  • Create a quality debt register (lint/test/contract failures) with direct owners.
  • Publish a weekly baseline report.

Execute now

  1. Run the baseline evidence lane:
  2. make operations-baseline
  3. make operations-status
  4. make operations-next-action
  5. make operations-complete
  6. or bash scripts/baseline_lane.sh
  7. Capture outputs under build/baseline/:
  8. gate-fast.json
  9. release-preflight.json
  10. doctor.json
  11. per-check logs and return codes
  12. summary JSON + markdown
  13. Convert blockers into assigned remediation tasks with due dates.
  14. Execute the top remediation task in the same cycle and rerun make operations-status.

Done when

  • CI and local runs are deterministic (same command path, same artifact contract).
  • Every baseline step emits logs and summary payloads.
  • Top blocking quality debt items are ranked, assigned, and reduced every cycle.

Next upgrades

  • Multi-Python matrix baseline evidence.
  • Profile-aware baseline (quick/standard/strict) comparisons.
  • Cross-repo baseline federation for portfolio reporting.

Close out

  • Freeze baseline as complete in weekly reporting.
  • Keep baseline artifacts as immutable audit evidence.
  • Remove stale baseline open tasks from active execution boards.

release readiness — Simplify surfaces and commands

Mission

Cut onboarding confusion while preserving advanced operational power.

Do this

  • Streamline first-contact docs and help surfaces.
  • Tighten command-family guidance by outcome.
  • Improve migration guidance for transition-era lanes.

Execute now

  1. Build an "operator essentials" command map.
  2. Collapse repetitive docs paths into canonical flows.
  3. Add explicit migration tables for legacy/transition surfaces.
  4. Introduce docs consistency checks for start-here + CI + triage routes.

Done when

  • A new operator reaches first successful run through one unambiguous path.
  • Command discoverability matches product tiers with minimal confusion.
  • Legacy routing is documented, predictable, and non-disruptive.

Next upgrades

  • Persona-specific docs overlays (maintainer/operator/executive).
  • Interactive CLI onboarding workflows.
  • Auto-generated docs nav health score.

Quality governance — Expand the quality engine

Mission

Upgrade checks into a stronger execution engine with better recommendations and richer evidence payloads.

Do this

  • Mature adaptive check planning.
  • Improve remediation outputs.
  • Improve run-report analytics and trend comparisons.

Execute now

  1. Extend adaptive planning signals (changed paths, reasons, risk areas).
  2. Enrich risk summary/fix plan payload contracts.
  3. Add trend deltas across successive baseline runs.
  4. Improve doctor handoff signals for deterministic follow-up pass actions.
  5. Validate baseline summary schema with:
  6. make quality-contract-check
  7. python scripts/check_baseline_summary_contract.py --summary build/baseline/baseline-summary.json --format json

Done when

  • Mean-time-to-triage drops through machine-readable outputs.
  • Recommendation precision improves in failure scenarios.
  • Output contracts stay stable and versioned for operator tooling.

Next upgrades

  • Intelligent check selection by repository topology.
  • Failure-family clustering and root-cause acceleration.
  • Fleet-level quality risk heatmaps.

Operational governance — Enforce enterprise governance

Mission

Turn technical capability into enterprise-grade confidence with explicit governance and audit evidence.

Do this

  • Harden contract governance.
  • Enforce policy and compatibility discipline.
  • Produce release-room evidence bundles.

Execute now

  1. Expand enterprise contract validations in CI gates.
  2. Standardize release-room summaries and evidence retention windows.
  3. Document compatibility/deprecation boundaries per tier.
  4. Track governance adherence with recurring reviews.
  5. Enforce governance docs contract with:
  6. make governance-contract-check
  7. python scripts/check_operational_readiness_governance_contract.py --format json

Done when

  • Enterprise checks are reproducible and policy-aligned.
  • Governance evidence is available and auditable.
  • Compatibility expectations are clear to integrators.

Next upgrades

  • Compliance overlay packs (domain-specific).
  • Policy-as-code templates for partner repos.
  • Automated governance drift alerts.

Ecosystem readiness — Scale integrations

Mission

Scale integrations and extension surfaces without destabilizing core release-confidence guarantees.

Do this

  • Improve plugin/runtime extension reliability.
  • Standardize integration playbooks.
  • Ship partner-ready packaging and support artifacts.

Execute now

  1. Harden plugin onboarding and failure diagnostics.
  2. Expand integration quickstarts with contract-backed checks.
  3. Publish extension certification criteria.
  4. Build portfolio scorecards from shared evidence contracts.
  5. Enforce ecosystem contract with:
  6. make ecosystem-contract-check
  7. python scripts/check_ecosystem_readiness_contract.py --format json

Done when

  • Integrations remain optional, reliable, and version-aware.
  • Extension failures are visible and non-blocking by default.
  • Partner onboarding time drops through repeatable playbooks.

Next upgrades

  • Marketplace-style extension catalog.
  • Hosted control-plane integrations.
  • Managed adoption analytics across organizations.

Metrics readiness — Operationalize metrics and commercialization

Mission

Convert technical progress into repeatable business outcomes and reporting-ready metrics for operators, buyers, and investors.

Do this

  • Generate KPI snapshots and scorecards.
  • Validate metrics contracts for recurring reporting.
  • Publish commercialization-ready evidence surfaces.

Execute now

  1. Build and publish periodic KPI snapshots.
  2. Standardize scorecard freshness and publishing cadence.
  3. Tie release-confidence outputs to adoption/operations metrics.
  4. Enforce metrics contract with:
  5. make metrics-contract-check
  6. python scripts/check_metrics_readiness_contract.py --format json

Done when

  • KPI artifacts are generated on schedule and contract-validated.
  • Metrics signals are reproducible and tied to release-confidence behavior.
  • Reporting surfaces are usable in quarterly portfolio/leadership reviews.

Next upgrades

  • Trend anomaly alerts for KPI regressions.
  • Portfolio-level cross-repo reporting federation.
  • Commercial package variants with benchmark slices.

Readiness control loop (run every stage)

Run the same control loop in every readiness stage:

  1. Plan: set targets, risks, and metrics.
  2. Execute: ship lane automation and deliverables.
  3. Validate: run contract checks and baseline verification.
  4. Publish: emit artifacts, summary, and decision memo.
  5. Expand: queue the next major upgrades.

This keeps the roadmap sequential while still open for large-scale iteration.