Guide
Make Your Codebase AI-Agent-Friendly
AI coding agents (Claude Code, Cursor, GitHub Copilot) are now how a large share of developers build — but they're only as good as the codebase they're given. A few small files turn "an agent that fumbles" into "an agent that starts productive."
This is the exact set of context files we ship in every VerdantStack starter kit. Here's what they are, why they matter, and how to add them to your own project.
Why this matters in 2026
The platforms have made AI-agent discoverability a first-class concern: Cloudflare launched an "Agent Readiness" score, Google added llms.txt to Lighthouse, and Vercel ships an agent-readiness auditor. For a developer using a coding agent daily, a kit or repo that ships purpose-built context files is measurably easier to build on than one that doesn't.
The payoff is concrete: fewer wasted agent turns, cleaner PRs, less "the agent rewrote the architecture" churn, and — for a paid starter — a real reason to choose it.
The five steps
1. Add an AGENTS.md at the repo root
This is the single highest-leverage file for AI coding agents. It tells an agent the architecture, the commands, and the absolutely-do-not-break rules before it touches anything. Claude Code, Cursor, and others read it automatically. Keep it short and struct: architecture diagram, quick commands, file map, and three or four critical rules.
2. Add a CLAUDE.md for Claude-specific context
CLAUDE.md carries the mental model and the gotchas that matter most to Claude. Where AGENTS.md is the general-purpose brief, CLAUDE.md is the "things that will bite you" file — the invariant the agent must not "fix". It reinforces AGENTS.md and adds tool-specific guidance.
3. Ship an llms.txt so agents can discover your docs
llms.txt is a simple, standard index of your project — one file at the site root that lists products, guides, and docs with URLs. Google added llms.txt to Lighthouse in 2026: it is now an official machine-readable documentation convention, not a niche hack. Point agents at the high-value pages, not every page.
4. Document your public API surface with type-level references
A generated API reference (TypeDoc / JSDoc) gives an agent an exact, never-rot spec of every export. Keep comments on signatures, not verbose essays: agents retrieve the comment adjacent to the identifier when asked "how do I use X". A CI gate that fails on an undocumented export keeps it honest.
5. Wire the files into build and release, so they cannot rot
Docs drift is the silent killer. Generate the API reference from the compiler, and add a build gate that fails when an exported symbol is undocumented or a reference is out of sync. The context files should be owned like code, because agents now treat them like code.
The toolset at a glance
AGENTS.md
Root context file AI coding agents read automatically
Pitfall: Omitting it — agents start fumbling instead of productive
CLAUDE.md
Claude-specific mental model and invariants
Pitfall: Letting it drift from the actual architecture
llms.txt / llms-full.txt
Machine-readable site index for agents (llmstxt.org)
Pitfall: Listing everything instead of the high-value pages
Generated API reference
Compiler-derived spec that cannot silently rot
Pitfall: Hand-writing it so it drifts from the code
The one thing to avoid
Don't make these files wallpaper. An AGENTS.md that hasn't been updated since the schema changed, or an llms.txt that points to dead pages, is worse than none — it teaches an agent confidently wrong things. If you add the files, wire them into CI so they stay truthful.
See it in a real kit
Every VerdantStack starter ships this exact setup: AGENTS.md +CLAUDE.md at the repo root, a generated TypeDoc API reference, and anllms.txt on the site — maintained by a build gate so they cannot drift.
Related reading
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