Your AI Agent Deserves Its Own Repo
Every developer using AI coding agents has felt this: you start a new session, and the agent knows nothing. You re-explain your Stripe setup. You describe your deploy pipeline again. You remind it about the database schema, the webhook endpoints, the environment variables. Every. Single. Time.
Auto-memory helps a little. It saves fragments — "user prefers TypeScript," "project uses PostgreSQL" — but it's a flat list the AI controls. You can't organize it. You can't version it. You can't decide what gets loaded and when. It's a scratchpad, not a knowledge base.
There's a better way to think about this.
Context is a new kind of artifact
As developers, we already maintain separate artifacts for different consumers:
- Code is for your compiler and runtime. It's the artifact your machine executes.
- Documentation is for humans. READMEs, wikis, API docs — they help people understand and use your systems.
- Context is for your AI agent. It's the artifact that lets your agent operate with full understanding of your stack, your processes, and your decisions.
This isn't a config file you drop into .cursorrules or a system prompt you paste at the start of each chat. It's a separate, version-controlled body of knowledge that exists so your agent can work with the same understanding a senior teammate would have after six months on the team.
A new kind of project.
You're not just writing code anymore
Here's the shift most developers haven't made yet: the quality of your AI's output is directly proportional to the quality of context you give it.
An LLM with no context is a generalist — it knows a lot about everything and not enough about your specific setup. An LLM with good context is a specialist. It knows your Stripe webhook URL, your deploy checklist, your naming conventions, the bug you've been chasing since Tuesday.
This means your job has expanded. You're still a developer, but you're also a curator of what your AI knows. Call it "developer of context" if you want a title for it.
What does that look like in practice?
An integration module that describes your Stripe setup means your agent can query invoices without you explaining webhooks every session. It knows the endpoint, the events you care about, the payload shape.
A workflow module for your deploy pipeline means the agent follows YOUR process — not its best guess at a generic deploy flow. Your staging environment, your smoke tests, your rollback procedure.
A task module for an active bug means the agent picks up where you left off, with all the context from yesterday's debugging session. The stack trace, the hypotheses you've ruled out, the fix you're trying next.
Each of these is a markdown file in a repo. Nothing fancy. Just structured knowledge your agent can load.
Why markdown. Why files. Why a repo.
The obvious question: why not a database? A vector store? Some purpose-built tool with embeddings and retrieval?
Because simplicity wins.
Markdown is universal. Every AI model reads it natively. No parsing layer, no serialization format, no schema migration. You write it, the agent reads it.
Files are composable. Load what you need, skip what you don't. Working on billing? Load the Stripe module. Debugging a deploy? Load the infrastructure module. Your agent gets focused context instead of everything-at-once noise.
A repo is version-controlled. Your context evolves with your stack. When you migrate from Stripe to Paddle, you update the module. When you change your deploy pipeline, the workflow module changes too. Git history tells you what your agent knew and when.
No vendor lock-in. Switch from Claude to GPT to Gemini tomorrow — your context still works. It's just files. Every agent can read them.
The before and after
Here's what it looks like without structured context:
"Hey Claude, we use Stripe for payments. The API key is in STRIPE_SECRET_KEY. Webhooks hit /api/webhook. We process checkout.session.completed events. The relevant code is in src/billing/. The webhook secret is STRIPE_WEBHOOK_SECRET. Oh, and we use the meter-based billing API for usage tracking, here's how that works..."
You type that. Or some version of it. Every time.
Here's what it looks like with a context module:
"Pull last month's invoices."
The agent reads your Stripe module. Knows the API, the endpoints, the event types, the code paths. Doesn't need the walkthrough. Just does the work.
That's not a marginal improvement. That's a fundamentally different way of working with AI.
Build yours
Everything is Context is the tool I built to make this practical. It gives you a structured way to create, organize, and load context modules for your AI agent.
Three commands to get started:
curl -LsSf https://everythingiscontext.com/eic/install.sh | sh
eic add stripe # loads a pre-built Stripe integration module
eic add cloudflare # loads Cloudflare DNS/Workers context
Or build your own modules from scratch. Write markdown, organize it into modules, load what you need per session.
Your code has a repo. Your docs have a repo. Your AI context should too.