Agentic AI Governance
AI Governance Built for Agents
That Take Action
Review what an agent actually does in testing and production, then set the boundaries it must follow when it acts.
Governance Stops at Approval.
Lumenova AI Carries It Into Production.
An agent passes review on a Tuesday. By Friday it has a new tool, a new model, and access no one signed off on, and the approval still says “approved”. Governance built on policies, committees, and questionnaires usually ends at that sign-off. After it, nothing holds the agent to what was reviewed, and nothing records what it does.
The Lumenova AI platform ties approval to access, so it stays with the agent. Approval status on each use case sets what its agents can reach at the gateway, and the reviewed scope becomes the enforced scope.
Policy runs before each action, every decision is recorded, and reviewers pull the evidence themselves, without waiting on engineering or starting over from the questionnaire.
For Approvers and Reviewers
Evidence Your Governance Team Can Act On
Give approvers the records, rule history, and reliability measures they need, in the formats and tools they already work with.
Approval-Based Access
Store approval status on each use case in the AI Registry, and let the AI Gateway use it to decide what each team’s agents can reach.
Decision Records
Keep each decision’s policy, outcome, reason, and input, linked to its trace, so reviewers can see exactly why an action was allowed or blocked.
Rule History
Record each policy version’s author, timestamp, and change note, with plain-English diffs reviewers can read.
Validated Scores
See how closely automated evaluation scores match your reviewers, so validators know how far to rely on them.
Reviewable Roles
Export custom roles as JSON and move them through GitOps, so role definitions get the same review as code.
Configurable Audit Log
Keep an audit log that’s append-only at the application level, with a retention period you set to match your regulatory and internal requirements.
GRC Export
Export decisions, findings, and detections as CSV or JSON, filtered by application and time range, for your GRC tool.
ITSM Alerts
Route alerts into ITSM and event management with resolution syncing both ways, and reach other tools through webhooks.
From Approval to Evidence
- Owning team
Register
The use case is registered with its owner, risk tier, and the resources it needs.
- Approver
Approve
The approver sets the approval status, and the approver is recorded on the use case.
- Platform
Enforce
The gateway scopes what the team's agents can reach, and policy runs before actions.
- Platform
Record
Policy decisions, guardrail decisions, and findings are recorded as the agents run.
- Reviewers
Review
Reviewers filter and export the records themselves as CSV or JSON.
Frequently Asked Questions
Approval status lives on the use case in the AI Registry. The AI Gateway uses it to decide which models, tools, agents, prompts, and skills that use case’s agents can reach, so the scope you reviewed is the scope that’s enforced.
Access stays limited to what the approved use case allows, so a new tool or resource outside that scope isn’t reachable until the use case is updated.
Yes. Reviewers filter and export decision records themselves. Each record includes the policy evaluated, the outcome, the reason, and the full input, linked to its trace.
Each policy version records its author, timestamp, and change note, with diffs your reviewers can read in plain English.
Agents seen in gateway traffic without a registry entry are held until someone registers them or turns them away. Agents that never cross the gateway need to be registered by their owners.