Engineering notes on AI governance
Research, analysis, and implementation notes on architectural drift prevention and 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 seven topic hubs collect the cornerstone and supporting pieces for each area. Architectural drift prevention for the AI SDLC is the lead topic.
Featured
The Emerging AI Agent Infrastructure Stack
Agent systems are separating into layers: models, tools, orchestration, memory, observability, governance, provenance, verification. Each answers a different reliability question — and the defining question is no longer model capability.
The Software Factory Is Finally Here. Its Decision Layer Is Missing.
Bob Bemer proposed the software factory in 1968. Coding agents finally supply the production capacity. Architectural control is the part still missing.
AI Engineering ROI: How to Measure the Real Return From AI Coding
AI spend is up 28x. Feature delivery is flat. Where the value leaks between generation and delivered software, and how to measure it in three layers instead of one.
Latest analysis
When Kubernetes Works but the Application Still Fails
Kubernetes autoscaling can add pods past what PostgreSQL accepts. Treat a connection budget as an architecture decision, and see which tool enforces each part.
Policy as Code Enforces Rules. Who Governs the Decisions Behind Them?
Kyverno exceptions, Sentinel overrides and OPA plan checks all work. The harder question: is the decision behind a policy still current, and who owns it?
NVIDIA OpenShell Reference Architecture: Agent Access Policy Is Not Architecture Governance
NVIDIA's OpenShell Reference Architecture can verify agent access policy in five modeled areas. An allowed write can still break an architecture decision.
From Decision Records to Decision Governance
Follow one ADR from record to governed decision, and see what a coding agent gets wrong if your decision records stop at each stage.
Can AI Reverse-Engineer the Decisions Behind a Software System? What the Research Shows
Architecture recovery is mature. A small 2026 study found that model-extracted decisions describe what changed and often miss why. Inferred decisions need human confirmation.
Pre-Action Enforcement: Where Agent Governance Takes Effect
Review and CI see agent actions afterwards. Pre-action enforcement checks a proposed write first, but only on action paths the gate can reconstruct.
Browse by topic
Architectural drift prevention
The lead topic: what architectural drift prevention is, where drift comes from across the AI SDLC, and which deterministic enforcement mechanisms hold.
Architectural governance
What architectural governance is, why intent decays as agents generate, and what deterministic enforcement looks like before a change lands.
Maintaining architectural intent
How to keep system-level decisions, constraints, and engineering standards intact as AI coding agents plan and generate code in agent-first workflows.
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.
Software migrations & modernization
Keeping AI coding agents on the target architecture while legacy and migrated code share one repository.
Start here
Architectural Drift Prevention
The discipline, defined: keeping AI-generated changes aligned with existing architectural decisions across the AI SDLC. Start here before the essays.
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.