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 six topic hubs collect the cornerstone and supporting pieces for each area. Architectural drift prevention for the AI SDLC is the primary category.
Latest analysis
The Architecture Layer the AI-Native SDLC Needs
Anthropic's playbook puts advisory and deterministic controls in the agent loop. Architectural decisions need a policy layer that resolves which rule applies before each action.
State of Startups 2026: 61% of Startup Codebases Are Majority AI-Written
Supabase's State of Startups 2026 finds 61% of startups have more than half their codebase written by AI, and 40% sit at 76-100%. Review budgets do not scale with that.
The Definitive Guide to AI for DevOps: Guardrails, Not Gates
KodeKloud's 2026 guide names guardrails, not gates as one of six takeaways, drawn from 29 practitioners. My contribution argues intent must become machine-readable.
Architecture Cannot Be a Prompt: What Context Compaction Silently Drops
New COMPINT research finds context compactors retain only 17% of standing session constraints. An agent can keep the task and lose the rule that governed it.
Architectural Technical Debt From AI Coding Agents
Gartner expects architectural technical debt to reach 80% of all technical debt by 2027. Code that passes review can still move a system off its architecture.
What Happens to an Architectural Decision After Ten Agent Turns
Coding agents lose fidelity to earlier decisions as trajectories lengthen. 2026 benchmarks now measure the decay, and repository state alone does not survive a handoff.
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.
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.