To give an AI coding agent real context about your codebase in 2026, the proven options are: an AGENTS.md brief, editor rules (Cursor/Copilot), MCP servers for tool access, RAG over your docs, inline comments and READMEs, and a code-anchored context layer. Most serious teams combine a few — a global brief plus per-file context is the strongest setup.
AI agents like Claude Code, Cursor, Copilot, and Codex can read your code. What they cannot read is the reasoning behind it — the decisions, the gotchas, the invariants that never made it into the source. Below are the six ways teams close that gap, from lightest to most durable, with honest trade-offs for each.
1. AGENTS.md — the repo-wide brief
Best for: always-true, global rules. AGENTS.md is a root-level file that tells any agent how your project is structured, what to run before committing, and which conventions to follow. It is portable, plain-text, and the de facto standard entry point.
Limit: it is one global file. It cannot carry per-area knowledge without becoming an unreadable wall, and it goes stale silently as the code moves on (more in why AGENTS.md isn’t enough).
2. Editor rules — Cursor Rules, .cursorrules, Copilot instructions
Best for: steering an agent inside one editor. Most AI editors support a rules file that injects standing instructions into every prompt — style, framework choices, "always do X." Quick to set up and effective for consistency.
Limit: they are editor-specific and prompt-level, so they tune behavior more than they deliver knowledge, and they do not travel across tools or to teammates on a different setup.
3. MCP servers — connect agents to tools and data
Best for: giving agents live access to systems. The Model Context Protocol lets an agent reach filesystems, databases, and APIs through a uniform interface. It is the right layer for access.
Limit: MCP transports access, not judgment. It can let an agent read everything and still miss the one constraint that matters (see MCP vs. a context layer).
4. RAG over your docs
Best for: question-answering over large documentation. Retrieval-augmented generation embeds your docs in a vector database and fetches relevant passages at query time, grounding answers in real sources.
Limit: RAG is reactive — it answers what the agent asks. It will not proactively warn about a constraint no one queried, and its quality depends entirely on the docs being current.
5. Inline comments and READMEs
Best for: local, in-the-moment explanation. Comments and READMEs sit right next to the code and are read by humans and agents alike. Cheap and universal.
Limit: they capture the what, rarely the why, and they drift as readily as any other doc — there is no mechanism to notice when a comment no longer matches the code above it.
6. A code-anchored context layer
Best for: durable, per-file knowledge that stays honest. A code-anchored context layer attaches notes — decisions, gotchas, invariants — to the exact globs they govern, delivers them for the files being changed, and flags them for review when that code changes. This is the approach Driftless takes.
Limit: it requires the team to write the knowledge down once; the payoff is that every future human and agent starts with it instead of re-deriving it. It complements, rather than replaces, the options above.
How to choose
- Just starting? Add an AGENTS.md and your editor’s rules file today.
- Agents need to reach systems? Add MCP for access.
- Answering questions over big docs? Add RAG.
- Knowledge keeps getting lost between sessions and people? Add a code-anchored context layer — that is the durable fix, measured by context coverage rather than a single file.
The strongest setups are not either/or: a global brief (AGENTS.md) for the always-true rules, plus per-file context for the specifics, plus MCP for access.
FAQ
What’s the single highest-leverage thing to do first?
Write an AGENTS.md and a rules file — minutes of work, immediate effect. Then, as soon as knowledge starts getting lost between sessions, anchor it to the files it concerns.
Do these replace each other?
No. They sit at different layers — rules steer behavior, MCP gives access, RAG answers questions, and a context layer delivers the knowledge a change requires. Most teams combine them.
How is this different from just writing more documentation?
Static docs drift. The durable approaches here either live next to the code or are anchored to it and re-checked when it changes, which is what keeps them from rotting. See context drift.
Driftless is the code-anchored context layer for human–AI engineering teams. See how it works.