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Runtime Policy Evaluation

Related to AI Runtime Governance and the Runtime Policy Engine.

Runtime policy evaluation is the process by which AI governance systems analyze agent actions in real-time and determine appropriate responses. Every action an AI agent attempts is evaluated against configured policies, resulting in one of three decisions: allow, block, or require approval.

What Is Runtime Policy Evaluation?

Policy evaluation is the computational process that translates governance rules into action decisions. When an AI agent attempts to execute an action, the governance system captures details about that action: what tool is being invoked, what parameters are being passed, which agent is making the request, and what context surrounds the request.

These details are then compared against configured policy rules. Rules define conditions and consequences: if certain conditions are met, a specific decision should be made. The evaluation process matches the action context against all applicable rules and resolves any conflicts to produce a final decision.

The entire evaluation must complete quickly, typically in under 10 milliseconds, to avoid impacting AI agent performance. This requires optimized evaluation algorithms, efficient data structures, and often distributed deployment to minimize latency.

Decision Logic: Allow, Block, Require Approval

Runtime policy evaluation produces one of three possible decisions:

ALLOW

The action is permitted and proceeds immediately to execution. Allow decisions are appropriate for low-risk actions that fall within the agent's normal operating scope. The action is logged for audit purposes but requires no additional intervention.

BLOCK

The action is prohibited and does not execute. The agent receives an error response indicating the action was blocked and, optionally, why. Block decisions are appropriate for actions that violate security policies, exceed risk thresholds, or fall outside the agent's permitted scope.

REQUIRE_APPROVAL

The action is paused pending human review. A notification is sent to designated approvers who can examine the action details and decide whether to permit or deny it. This decision is appropriate for high-risk actions that might be legitimate but require human judgment.

This three-decision model provides graduated responses to different risk levels. Organizations can configure policies that automatically permit routine operations, automatically block dangerous actions, and route uncertain cases to human reviewers.

Policy Evaluation Architecture

A robust policy evaluation system includes several architectural components:

Context Extraction

The first stage extracts governance-relevant attributes from raw action requests. For a database query, this might include parsing SQL to identify tables, operations, and affected row counts. For an API call, this might include extracting the endpoint, HTTP method, and request body contents.

Rule Matching

Extracted context is matched against configured rules. Rules are typically expressed as condition-action pairs: if these conditions are true, then this decision applies. Conditions can test any attribute extracted from the context, using operators like equality, pattern matching, numeric comparisons, and set membership.

// Example rule conditions
{
  "conditions": {
    "tool": "database",
    "action": "DELETE",
    "target": { "pattern": "users.*" }
  },
  "decision": "BLOCK",
  "reason": "User table deletions are not permitted"
}

Risk Scoring

Many systems calculate a numeric risk score based on multiple factors: action type, resource sensitivity, agent trust level, time of day, and historical patterns. This score can influence decisions or serve as additional context for human reviewers evaluating approval requests.

Conflict Resolution

When multiple rules match an action, the system must resolve conflicts. Common strategies include: explicit blocks override allows, more specific rules override general rules, and approval requirements take precedence over automatic allows.

Decision Output

The final decision is returned along with metadata: which rules matched, what risk score was calculated, and why the decision was made. This information is logged for audit and can be returned to the agent for appropriate error handling.

Role in Autonomous System Governance

Policy evaluation is the operational core of AI runtime governance. While policies define intent and the control plane provides infrastructure, evaluation is where governance actually happens. Every action evaluation represents a governance decision being enforced.

The evaluation process must be deterministic and explainable. Given the same action context and policy configuration, the system must produce the same decision. And that decision must be traceable to specific rules and conditions, enabling debugging and compliance verification.

Evaluation also feeds the governance feedback loop. By analyzing evaluation outcomes, organizations can identify policy gaps, tune risk thresholds, and improve governance over time. Patterns of blocked actions might reveal agent misconfigurations. Patterns of approval requests might indicate opportunities for automation.

How Runplane Solves It

Runplane provides a high-performance policy evaluation engine designed for AI agent governance. The engine evaluates policies in under 10 milliseconds, ensuring governance adds minimal latency to agent operations.

Policies are configured through a visual editor or declarative JSON schema. The system supports complex conditions with logical operators, pattern matching, and numeric comparisons. Risk scoring incorporates configurable factors to calculate action risk automatically.

Every evaluation is logged with complete context: the action attempted, rules matched, risk score calculated, and decision made. Dashboards show evaluation patterns in real-time, helping teams understand how policies affect agent behavior and identify opportunities for improvement.