Overview
How might we design agent versioning so people can trust and act on AI output?
When this pattern fits
- Ideal for agent development, production systems, and workflows where safely testing and comparing agent improvements is critical.
When to skip or lighten it
- One-off personal assistants with no shared ownership.
- Changes so tiny that version noise outweighs benefit (batch them).
- Environments where legal holds forbid rollback of a published agent.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Editing production prompts in place with no history.
Versions labeled only as timestamps with no changelog.
A/B tests without a clear winner criterion.
Rolling forward with no one-click restore.
How products use it
| Product | Implementation |
|---|---|
| LangSmith | Prompt and chain versions with comparison. |
| Weights & Biases | Experiment tracking across agent configs. |
| MLflow | Registered model and config versions. |
| Custom agent platforms | Draft/publish agent versions with rollback. |
Implementation
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Agent Versioning" AI interface design pattern.
Pattern Definition:Frequently asked questions
What should an agent version include?
Prompt or policy text, tool allowlist, model id, and eval notes. Show a human-readable changelog.
How do users know which version they are on?
Show version on the agent identity card and in audit receipts for consequential runs.
How does versioning relate to checkpoints?
Checkpoints restore task state mid-run. Versioning restores agent configuration across runs.
When should you A/B agents?
When metrics disagree or stakes are high enough to justify split traffic. Always define the success metric first.