AI Coding Agent Context: How to Give Claude Code, Cursor and Codex Shared Project Context
AI coding agent context is the project-specific state an agent needs before it edits code: current decisions, active work, code structure, and what still needs verification. It usually lives in one of three places — a single session, a static instruction file, or a shared context layer. Only the third one survives sessions, tools, and teammates changing.
What "context" means for a coding agent
Coding agents like Claude Code, Cursor, and Codex reason well over the text they can see. The problem is not reasoning; it is visibility. A repository accumulates decisions, conventions, in-flight branches, and prior mistakes that no single file fully captures. "Context" is shorthand for all of that project reality — not just the code, but why it looks the way it does and what is currently in motion.
There are at least three layers worth separating: the model's general training knowledge (fixed, not project-specific), the current session's conversation history (temporary, tied to one chat), and the project's own state (decisions, active work, code impact — which should outlive any one session or tool).
Why context gets lost
A new chat or a new terminal session has no memory of the last one. The agent re-reads whatever files it can find and infers the rest.
Long sessions get summarized or trimmed to fit the context window. Early decisions and constraints are the first things to go.
Claude Code, Cursor, and Codex do not share state by default. Moving from one to another means starting from files and prompts again.
When more than one agent or developer works in the same repository, nothing tells a second agent what a first agent already decided or is mid-way through.
The limits of static instruction files
AGENTS.md and CLAUDE.md solved a real problem: giving an agent stable, repo-specific rules instead of re-explaining them every session. They are still worth having. But they are static text, which means three things are always true about them.
- A human (or a script) has to remember to update them; they do not update themselves when a decision changes or a task finishes.
- They describe stable conventions well, but they cannot represent what changed this week, what a teammate's agent is currently doing, or which files a change actually touched.
- Every client that wants the same guidance needs its own copy in the right place, which drifts over time unless something keeps the copies in sync.
None of this makes instruction files wrong — it makes them one input, not the whole answer to project context.
Static context vs. dynamic project state
| Approach | Persists across sessions | Shared across tools | Updates itself | Good for |
|---|---|---|---|---|
| Native session context | No — lost when the session or chat ends | No | N/A | A single task inside a single session |
| Static instruction file (AGENTS.md, CLAUDE.md) | Yes, but only stable rules the file's author wrote down | Partially — each client needs its own copy or reads the same file | No — a human or a script has to edit it | Conventions that rarely change: style rules, architecture constraints |
| Dynamic context layer (hosted MCP) | Yes — decisions, active work, and evidence are stored and retrieved | Yes — any MCP-compatible client can query the same layer | Partially — updates come from reviewed captures, not automatic scraping | What changed, why, what it impacts, and what to verify right now |
How a shared context layer works
The common fix is a layer that sits outside any single editor or CLI, reachable by any client that speaks the same protocol. The Model Context Protocol (MCP) is the connector most coding agents already support: Claude Code, Cursor, Codex, Gemini CLI, and other MCP clients can all query the same server instead of keeping private notes.
The client still does the reasoning and the editing. The context layer's job is narrower: return what changed, why it changed, what it is likely to impact, and what should be verified before the agent proceeds — sourced from the project itself rather than the model's guesses.
How Snipara approaches this
Snipara is a context and memory layer for AI coding agents. It gives each repository a Project Brain powered by Project Intelligence, connecting decisions, active work, code impact, verification evidence and handoffs across Claude Code, Cursor, Codex, Gemini CLI and other MCP clients.
In practice that means: a client connects through hosted MCP, Snipara returns reviewed context and evidence rather than a raw file dump, and existing AGENTS.md or CLAUDE.md files are not overwritten by setup — generated copies are kept under .snipara/templates for a human or agent to merge deliberately.
Limitations
- Snipara does not replace the coding model — Claude, Codex, or Cursor still reason and write the code.
- It does not guarantee correct code; it improves the starting context the agent reasons from.
- It does not remove the need to run tests or review diffs before merging.
- Project Intelligence briefs are reviewable evidence views, not canonical project truth or autonomous approval.
- Snipara is not an LLM provider — you bring your own model and coding client.
FAQ
What is AI coding agent context?
It is the project-specific information an agent needs before it edits code: current decisions, active work, code structure, and what still needs verification. It is different from the model's training data and different from a single chat's history.
Why does context disappear between sessions?
Most coding agents keep context inside a session or chat transcript. When that session ends, gets compacted, or the tool changes, nothing carries the working state forward unless it was written somewhere persistent outside the session.
Are AGENTS.md and CLAUDE.md enough?
They are useful for stable, rarely-changing conventions, but they are static text a human has to maintain. They cannot represent what changed this week, what a teammate's agent is currently doing, or what a change actually impacted in the code graph.
How do Claude Code, Cursor, and Codex share context?
Through a common protocol, typically MCP (Model Context Protocol). All three can connect to the same hosted MCP endpoint and query the same project state, instead of each tool keeping its own private notes.
Does a shared context layer replace the coding agent?
No. The coding agent still reasons, writes, and edits code. A context layer answers what the agent should know before it starts, not what code to write.