AI Agent Governance for Rules, Releases, and Drift
AI agent governance for rules, guidance, releases, drift detection, runtime policy, and audit-ready evidence.
Govern the instructions agents actually run with.
Turn agent rules, guidance, skills, plugins, settings, releases, and drift into managed objects with review history.
Stop policy drift across agent runtimes.
AI agent governance is the difference between copied prompt files and a versioned operating model that reviewers can trust.
- Company rules are copied into prompt files, repo instructions, and local runtime config until nobody knows which version is active.
- Guidance changes ship informally, so agents may keep following outdated review rules or risky tool habits.
- Runtime state drifts from the desired policy and only becomes visible when review fails or production behavior changes.
Prove the policy version used during the run.
Reviewers need to know which rule release was approved, which runtime received it, and whether the agent reported a different state.
- How do we version rules, guidance, skills, plugins, and settings as real governance objects?
- How can a reviewer prove which policy an agent saw during a run?
- How do we detect drift between the approved release and reported runtime state?
Publish rules and guidance as controlled releases.
Agents Control compiles approved governance objects into runtime artifacts and compares desired state with reported state.
- Manage rules, guidance, skills, plugins, settings, releases, and drift as first-class governance objects.
- Compile policy into runtime-specific artifacts instead of asking every agent to interpret the same markdown.
- Show desired state, reported state, and violation evidence in one review loop.
Common questions
Clear answers for teams comparing agent management, orchestration, governance, security, and MCP controls.
What does AI agent governance mean in a repo?
It means rules, guidance, permissions, releases, runtime state, and violations are managed as versioned objects instead of scattered prompts.
Why are LLM guardrails not enough?
Generic guardrails help, but coding teams also need repo-specific policy, runtime permissions, drift review, and PR evidence.