How do I version control AI agent policies?

Version controlling AI agent policies means treating governance rules as code — stored in git, reviewed via pull requests, tested in staging, and deployed through CI/CD — ensuring every policy change is auditable, reversible, and traceable.

Why policies must be version controlled:

• Audit trail: Regulators ask "what rules were in effect when this incident happened?" — git log answers that question definitively
• Rollback capability: A bad policy change can be reverted instantly via git revert
• Change review: Policy changes affect what AI agents can do in production — they deserve the same review rigor as code changes
• Environment parity: Test policy changes in staging before deploying to production — catch conflicts and edge cases early
• Compliance evidence: SOC 2 auditors want to see change management processes for security controls

Exogram's 8 policy gates are Python code — not configuration files, not YAML, not natural language prompts. They live in your repository, are reviewed in pull requests, tested in CI, and deployed through your existing pipeline. Policy is code. Code has version control. Version control provides accountability.