Overview
How might we design context portability so people can trust and act on AI output?
When this pattern fits
- Essential for cross-app agents, tool-calling assistants, and multi-step workflows where users benefit from inspecting and editing the context payload that travels across each boundary.
When to skip or lighten it
- Single-app tools with no ecosystem to port into.
- Highly sensitive contexts that must not leave a compliance boundary.
- Throwaway chats where portability adds ceremony without reuse.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Opaque blobs users cannot inspect before export.
Portable packages that silently include secrets.
One-way lock-in with import but no export.
Losing citations and permissions metadata in transit.
How products use it
| Product | Implementation |
|---|---|
| Model Context Protocol | Standardized context and tool interfaces across clients. |
| ChatGPT | Connectors and shared context across GPT surfaces. |
| Zapier | Mapped fields that carry data between automations. |
| Apple Shortcuts | Variables passed across multi-app shortcut steps. |
Implementation
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Pattern Definition:Frequently asked questions
What is context portability in AI UX?
It is the ability to move conversation context, files, and tool state between apps or sessions in a form users can inspect and control.
What should a portable package include?
Messages or summaries, attachments by reference, tool allowlists, and provenance. Exclude secrets unless explicitly opted in.
How does this relate to shared session links?
Shared links hand off a live or snapshot session inside one product. Portability aims at cross-app, often file- or protocol-based handoff.
Why does legibility matter?
If users cannot read what is being transferred, they cannot consent. Portable does not mean invisible.