A practical engineering protocol for AI coding agents.
AI coding agents are good at writing code. The harder problem is getting them to work like disciplined engineers.
Agent Protocol provides repository-level rules for making coding agents:
- plan complex work before changing code,
- stay inside the requested scope,
- prefer simple solutions,
- fix root causes instead of symptoms,
- respect existing architecture,
- verify changes before claiming completion,
- and learn from meaningful corrections.
Understand → Inspect → Plan → Implement → Verify → Review → Report
When something goes wrong:
Stop → Understand → Re-plan → Fix → Verify
The goal is not to make an AI agent write more code.
The goal is to make the smallest correct change — and prove that it works.
AGENTS.md
README.md
LICENSE
templates/
PROJECT_RULES.md
todo.md
lessons.md
examples/
nextjs/PROJECT_RULES.md
swift/PROJECT_RULES.md
docs/
workflow.md
tasks/
todo.md
lessons.md
AGENTS.md defines how the agent works.
PROJECT_RULES.md templates define constraints specific to a repository.
tasks/todo.md tracks substantial work when explicit planning is useful.
tasks/lessons.md stores durable lessons from meaningful mistakes and corrections.
This separation keeps engineering behavior, project architecture, temporary work, and accumulated knowledge from becoming one giant prompt.
- Copy
AGENTS.mdinto your repository root. - Copy
templates/PROJECT_RULES.mdand customize it for the project. - Create
tasks/todo.mdandtasks/lessons.mdfrom the templates when useful. - Add repository-specific build, test, architecture, and scope constraints.
- Let your coding agent inspect these files before substantial work.
The examples/ directory contains small project-specific rule sets for Next.js and Swift repositories.
The first release intentionally contains no CLI, installer, package, or enforcement layer. It focuses on a small, portable engineering protocol that can be adopted without tooling.
Agent Protocol is experimental. The protocol will evolve based on real repository use and recurring failure patterns.
MIT.