What is shared context for AI agents?
Shared context is an access-controlled operating record that several authenticated actors can inspect and update. It makes state, evidence, revisions, and feedback visible outside one model session.
When should a team use shared context instead of memory?
Use shared context when work crosses identity, model, tool, or organizational boundaries, or when a human must review the evidence. Use runtime memory for preferences and local conversational continuity that do not need a shared audit trail.
- The task may move from Claude, ChatGPT, Gemini, Cursor, or Codex to another tool.
- A teammate needs to verify or annotate the work before it continues.
- Files, SQL, HTML, images, or reports must travel with the summary.
- The company needs authorship, access control, revisions, or retention policy.
Can MCP provide shared context?
MCP provides a standard client-server protocol for context exchange, tools, resources, and notifications. A context platform can expose shared records through MCP, but the protocol itself does not decide how records are versioned, governed, or reviewed.
How do memory and shared context work together?
Keep transient reasoning and preferences in the runtime, then publish durable outcomes and evidence to shared context at meaningful checkpoints. The next agent can retrieve the canonical record through MCP or HTTP and seed its own local working memory from verified state.
