Auto-extracted specs read the code, not the decisions behind it. They quietly promote placeholders and fallbacks as if settled. Sign-off is the missing step.
AI-native teams need three layers of durable memory — behavior, decisions, execution. Execution has tooling. Most teams lack the behavior layer entirely.
Paste a prompt into Claude or ChatGPT, describe your product module briefly, and get a .pbc.md behavior spec you can view, edit, and commit to your repo.
CLAUDE.md and AGENTS.md tell agents how to work in your repo. They don't tell agents what your product promises. That's a different artifact — the PBC layer.
A step-by-step guide to writing a .pbc.md file for your product's most critical module. Start with plain Markdown; add structured blocks agents can read.
Shipping fast with AI agents feels productive. But the costliest mistake isn't bad code — it's building confidently when nobody wrote down what was decided.
PRDs capture intent. Tests verify assertions. Between them, there's no artifact tracking what the product promises — grounded in code, confirmed by humans.
AI agents have AGENTS.md, memory banks, harnesses, and monitors. They still lack the product context layer — what the product promises and what must hold.
Your coding agent ships correct-looking code that breaks product promises. The fix isn't capability — it's the product context layer agents lack.
Your repo has workflow instructions, session context, and feature specs. None of them answer what the product promises to do. That's the PBC layer.
A .pbc.md file opens in VS Code, renders on GitHub, reads like any Markdown doc. Drop it into pbc.stewie.sh and the same file becomes navigable UI.