AI Policy Engine: Decide What Each Agent May Do Before It Acts
Define rules for tool calls, budgets, and handoffs into policy engineers can test and risk teams can review, enforced before each action runs.
In most teams, the rule that stops an agent is a threshold buried in application code or a score from another model. Risk teams can’t read either one. The reviewer who samples outputs sees them after the action has already run. Every new agent means another team rebuilding the same controls its own way.
The result is a human review process that can’t keep pace as agents scale, and an audit trail that has to be pieced together after something goes wrong.
Your identity systems already establish who an agent is. The Policy Engine decides what that agent may do on each call, and records why.
Capabilities
Everything You Need to Govern AI Agent Authority
Write, test, and enforce policy in one workflow, from the first draft to every decision it makes in production.
Live Validation
Catch errors as you type. The editor checks policy against the enforcement engine, so invalid policy can’t be saved or run.
Pre-Production Testing
Run policy against sample input and see the decision and each evaluation step before it touches live traffic.
Version History
Move policies from draft to active to archived, with who, when, and why recorded and diffs in code and plain English.
Pinned Policy Stacks
Combine policies into reusable stacks and pin each version, so changing one rule doesn’t unexpectedly affect another use case.
Call-Order Rules
Require actions to happen in sequence, such as de-identifying data before export. Available for LangChain and LangGraph agents.
Policy Assistant
Describe a rule in plain language and get validated policy as a diff that a person reviews, then accepts or rejects.
Embedded Guardrail Checks
Run checks like personal-data detection inside a policy and use the result to allow or block the action.
Decision Records
Keep the policies evaluated, outcome, reason, and input for each decision, linked to its trace. Export as CSV or JSON for validators and auditors.
Overline will be here
How the AI Policy Engine Makes a Decision
Your agent
Requests a tool call, a model call, or a handoff to another agent.
Send context
The SDK sends the request and its context to the policy engine.
Evaluate
The policy engine evaluates the policy stack bound to the agent and returns allow, block, or flag.
The action runs
The tool call, model call, or handoff goes ahead when the policy allows it.
Blocked before it runs
The action doesn't run, and your application can route the case to a person. If the engine can't be reached, the SDK fails open unless you set it to fail closed.
Decision record
Each recorded decision keeps the policy stacks evaluated, the outcome, the reason, and the full input, linked to the trace.
Frequently Asked Questions
Through the Lumenova AI SDK, which checks policy before and after each tool and model call and at agent handoffs. Traffic routed through the AI Gateway is checked there too. Call-order rules, which look across a sequence of calls, run centrally on the Lumenova AI platform.
Policy evaluation on the platform fails closed. The SDK fails open by default, so if your application can’t reach the engine the action runs, unless you set the SDK to fail closed. Detection models called inside a decision fail open.
No. Describe a rule in plain language, and the policy assistant proposes validated policy as a diff for a person to review and accept. You can also combine existing policies into stacks and reuse them across use cases.
Run it against sample input and see the decision, the message, and a trace of each evaluation step. You can also load a past decision with its exact input to see how a proposed rule would have decided it.
No. The assistant only proposes changes as a diff. A person accepts or rejects each one, and every version records who changed it, when, and why.
The policy stacks evaluated, the outcome, the reason, and the full input, linked to the trace. Your team can filter the records and export them as CSV or JSON for validators and auditors.