AI-FIRST ENGINEERING LEADERSHIP CHECKLIST (2026)

Purpose: Use this checklist to adopt AI coding tools/agents while maintaining reliability, security, and clear accountability. Aim to complete Sections 1–3 in a single working session; iterate monthly.

1) Governance (decisions you must make)
- Approved tools list: Choose 1–2 AI environments (e.g., Copilot Business/Enterprise, ChatGPT Enterprise, IDE agent). Document what’s approved and why.
- Data boundaries: Define what can/can’t be included in prompts (PII, secrets, customer logs, proprietary source). Add examples.
- Identity & access: Require SSO, SCIM provisioning, and role-based access for AI tools. No shared accounts.
- Retention & audit: Ensure admin audit logs exist; set retention to match your compliance needs (e.g., 90–365 days).
- Ownership rule: Publish a single sentence policy: “Service owners are accountable for changes, regardless of whether a human or agent authored them.”

2) Verified-change workflow (what every PR must prove)
- PR template fields (minimum): intent summary, risk area tag (auth/payments/PII/etc.), test evidence, rollout plan, rollback plan.
- Diff limits: Set a default cap (e.g., 400 LOC median target). Require design notes when exceeding it.
- High-risk gates: For auth/payments/PII, require codeowner review + integration tests + staged rollout.
- Provenance: Add AI provenance metadata (tool, model/version, session ID, prompt summary) in PR description or as an artifact.

3) Tooling “paved road” (make safe the default)
- CI speed: Ensure unit + integration tests run fast enough to be used (target <15 minutes for core services).
- Security basics: Enable secret scanning, SAST, dependency alerts, and SBOM generation.
- Release safety: Use feature flags, canary deploys, and automatic rollback for critical services.
- Observability: Require dashboards for latency, error rate, and saturation; define SLOs for top services.

4) Metrics scorecard (review weekly)
- DORA: deploy frequency, lead time, change failure rate, MTTR.
- AI-era metrics: % PRs with test delta (target ≥70%), median agent diff size, AI spend per engineer/month, “incident attribution clarity” (can you trace changes to PR/prompt/reviewer?).
- Budget: Set per-team AI usage budgets and alert at 80% utilization.

5) Rollout plan (staged autonomy)
- Pilot scope: Start with low-risk work (dependency bumps, docs, internal tooling) before core production paths.
- Success criteria: Define explicit targets (e.g., -20% lead time with no increase in change failure rate; spend <$200/engineer/month).
- Expand autonomy only after hitting targets for 4–6 weeks.

6) People & incentives (avoid invisible-work failure)
- Leveling updates: Reward review quality, test improvements, operational readiness, and interface clarity.
- Training: Run “agent review drills” where engineers practice spotting edge cases and demanding verification.
- Postmortems: Keep them blameless; focus on process fixes (gates/tests/rollouts), not “the model did it.”

Outcome: If you can’t confidently answer who approved a change, how it was verified, and how to roll it back, you are not AI-first—you are risk-first.