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Agents

Agent Versioning

Treat prompts, tools, and policies as versioned configs you can compare, roll back, and A/B. Change agent behavior without mystery regressions.

Interactive demo

Agent Versioning
v2.1.0
Latest stable release with improved accuracy
stable2024-01-15
v2.2.0-beta
Beta release with new features
beta2024-01-20
v3.0.0-alpha
Alpha release - experimental features
alpha2024-01-25

Overview

How might we design agent versioning so people can trust and act on AI output?

When to use

  • Ideal for agent development, production systems, and workflows where safely testing and comparing agent improvements is critical.

When to skip

  • 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.

Rules

  • 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.

Evidence

ProductImplementation
LangSmithPrompt and chain versions with comparison.
Weights & BiasesExperiment tracking across agent configs.
MLflowRegistered model and config versions.
Custom agent platformsDraft/publish agent versions with rollback.

FAQ

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.