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 primary category.
Latest analysis
Agentic Change Management Needs a Decision Layer
CodeRabbit's Triage turns the PR queue into a scored decision system. A PR still isn't an architectural decision — here's the layer underneath it.
Forward-Deployed Architects Need a Decision Layer
A forward-deployed architect discovers how a business unit actually operates. Without a decision layer that survives them, an agent working in that unit inherits none of it.
AI Throughput Is Not Engineering Throughput
NBER research tracking 500,000+ GitHub developers found autonomous agents produce a 240% increase in coding activity, attenuating to 30% at the release stage. Coding activity was never the right unit.
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.
What Changes When Forward-Deployed Engineers Use Coding Agents
Agents do not widen the context deficit. They raise the rate at which it converts into committed code, which breaks the review arrangement that used to contain it.
The Hidden Architecture Problem in Forward-Deployed Engineering
A repository records what exists, not what was deliberate. Four structural failure modes explain why more documentation has never closed that gap.
Browse by topic
Architectural drift prevention
The primary category: 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.