AI Governance

Enterprise adoption of autonomous development agents introduces systemic risks around code provenance, supply-chain integrity, and automated approvals. This track outlines policy layers, risk boundaries, and engineering gates required to govern AI-assisted software delivery without throttling engineering velocity.

Start here

Reading order

  1. AI Adoption in Engineering: Breaking the 50% Plateau

    • Updated

    Purpose-built AI tooling cuts per-task cost 21-68%. Three-cohort model and four-phase operating framework for engineering leaders past the 50% adoption plateau.

  2. AI Approval Gates: Engineering Oversight at Machine Speed

    • Updated

    AI approval gates: reversibility-tiered human-in-the-loop design with four health metrics that prevent reviewer atrophy at machine speed.

  3. Why Your AI Agent Failed in Production

    • Updated

    Why your AI agent failed: missing decision provenance, not metrics. The 3 observability gaps traditional monitoring won't catch.

  4. SRE for AI Agents: Error Budgets, Trust, and 90 Trials

    • Updated

    Can an AI agent predict scope without hallucinating? We ran 90 trials. It added 1.7 phantom files per change. Error budgets and trust ladders are the gate.

  5. AI Delivery Decision Frameworks: Type 1, Type 2, DACI

    • Updated

    Misclassifying reversible decisions costs more than the decision itself. Four frameworks unblock AI delivery: Type 1/Type 2, Eisenhower, DACI, and PMBOK.

  6. AI-Governed DNS Migration Without Maintenance Windows

    • Updated

    Programmatic pre-validation eliminates the weekend window from DNS migrations. Full platform migration: 2 hours, business hours, zero downtime.

Browse all tracks on the Topics index or filter by tag on the Tags page.