Engineering notes on AI governance
Research, analysis, and implementation notes on architectural governance for AI-assisted software development.
Start with the latest analysis, jump to a topic, or browse the full archive. New essays land most weeks; the four topic hubs collect the cornerstone and supporting pieces for each area.
Stanford AI Index 2026: AI Coding Is Becoming Solved. Engineering Governance Has Not.
The Stanford AI Index 2026 shows coding benchmarks near saturation and adoption mainstream. If generation is solved, the unsolved layer is keeping agent output aligned with architectural decisions.
Agentic Resource Discovery: Why Agent Discovery Still Needs Governance
Google’s Agentic Resource Discovery lets agents find and verify tools across the web. Discovery and identity are necessary but not sufficient: a trusted capability can still be used in an untrusted way.
The Strongest AI Engineering Teams Won’t Be Built From Bigger Agents
A DevOps study found collaborative team structures beat both silos and full integration. Applied to AI agents, the missing variable is a governance layer — with the metrics to prove it.
How to Enforce Engineering Standards Across AI-Assisted Teams
AI multiplies code output but not architectural oversight. A four-layer model — decisions, standards, team context, verification — for enforcing engineering standards across human-agent teams.
You Can’t Govern What You Can’t See: The Visibility Gap in Agentic Engineering
Claude Code pricing and Copilot billing reveal a deeper problem: leaders have little visibility into an emerging agentic workforce. Costs are a symptom; governance starts with visibility.
Builderbot Proves the Next AI Engineering Layer Is Coordination, Not Coding
Block’s Builderbot operates across the company’s repos and workflows at scale. It shows the next AI engineering challenge is organizational coordination, and why coordination still needs governance.
Architectural governance
What architectural governance is, why intent decays as agents generate, and what deterministic enforcement looks like before a change lands.
AI coding agents
How governance applies across Claude Code, Cursor, Copilot, Devin, code review, and the agentic SDLC.
Agent infrastructure
Memory, orchestration, harnesses, registries, runtimes, and protocols — and why each layer still needs governance.
Engineering performance
DORA, SPACE, METR, rework, and verification cost: how AI-assisted engineering is measured and what to track once agents do the work.
Review Is Not Governance
CodeRabbit helps review AI-generated code. Mneme helps govern what the AI generates in the first place. Two different layers of the same problem.
Prompt Engineering Is Not Governance
Prompt templates can nudge an LLM toward better output. They cannot enforce architectural invariants, resolve decision conflicts, or prevent drift across a multi-engineer codebase.
Models Are Temporary. Architectural Intent Is Not.
Models change. Agents change. IDEs change. Architectural intent should not. The case for keeping AI governance outside the model — and the second kind of lock-in (governance lock-in) that most teams discover too late.