2025-11-07 · Authensor

What Is a Policy Engine for AI Agents?

A policy engine is a deterministic decision-making system that evaluates every AI agent action against a set of declarative rules and returns an authorization verdict -- allow, deny, or escalate. Unlike probabilistic model-based safety (where the LLM is asked to self-censor), a policy engine operates independently of the AI model, applying consistent, auditable logic that cannot be influenced by prompt injection or model hallucination. SafeClaw by Authensor includes a policy engine that reads YAML-defined rules and enforces them across all tool calls for agents built with Claude, OpenAI, or any MCP-compatible framework.

Why AI Agents Need a Policy Engine

AI models are inherently non-deterministic. The same prompt can produce different outputs, and models can be manipulated through adversarial inputs. Relying on the model itself to enforce safety constraints creates a single point of failure where the safety mechanism and the threat vector are the same system.

A policy engine decouples safety enforcement from the AI model:

This separation of concerns is the foundation of trustworthy AI agent systems.

How a Policy Engine Works

SafeClaw's policy engine follows a first-match-wins evaluation strategy:

  1. Receive action request -- The engine receives a structured tool call with action type, parameters, and context
  2. Iterate through rules -- Rules are evaluated in order from top to bottom
  3. Match conditions -- Each rule specifies conditions (action type, path patterns, command patterns, domains)
  4. Return first match -- The first rule whose conditions match determines the verdict
  5. Fall through to default -- If no rule matches, the defaultAction (typically deny) applies

First-Match-Wins Example

# safeclaw.yaml
version: 1
defaultAction: deny


rules:
  # Rule 1: Allow reading test files
  - action: file_read
    path: "./tests/**"
    decision: allow



# Rule 2: Deny reading fixture data with secrets
  - action: file_read
    path: "./tests/fixtures/secrets/**"
    decision: deny



# Rule 3: Escalate all other file reads
  - action: file_read
    decision: escalate

In this configuration, Rule 1 matches first for any file read in ./tests/, including files in ./tests/fixtures/secrets/. Because the engine uses first-match-wins, Rule 2 never applies. To fix this, place the more specific deny rule before the broader allow rule. Rule ordering is a critical aspect of policy engine configuration.

Installing and Configuring SafeClaw's Policy Engine

npx @authensor/safeclaw

A well-structured policy places rules in order from most specific to least specific:

# safeclaw.yaml
version: 1
defaultAction: deny


rules:
  # Most specific: block sensitive paths first
  - action: file_read
    path: "./.env*"
    decision: deny
    reason: "Environment files contain secrets"



- action: file_read
    path: "./*/.pem"
    decision: deny
    reason: "Certificate files are sensitive"



# Broader allows after specific denies
  - action: file_read
    path: "./src/**"
    decision: allow



- action: file_read
    path: "./docs/**"
    decision: allow



# Catch-all escalation for unmatched reads
  - action: file_read
    decision: escalate
    reason: "Unrecognized file read location"

This ordering ensures sensitive files are protected even if they exist within otherwise-allowed directories.

Policy Engine Properties

A robust policy engine for AI agents must exhibit several properties:

Determinism

Given the same action request and the same policy, the engine must always return the same verdict. SafeClaw's 446-test suite validates deterministic behavior across all rule combinations.

Independence from the Model

The policy engine must not call the AI model to make decisions. This prevents prompt injection from influencing authorization.

Fail-Closed Behavior

If the engine encounters an error during evaluation -- a malformed rule, a missing field, or an unexpected action type -- it must default to deny rather than allow. SafeClaw implements fail-closed semantics throughout.

Auditability

Every evaluation must be logged with the action request, the matched rule (or lack thereof), and the resulting verdict. This creates a complete record for compliance and incident response.

Low Latency

Policy evaluation happens in the critical path of every tool call. SafeClaw's engine evaluates policies in microseconds, adding negligible overhead to agent operations.

Policy as Code

Defining policies in YAML files enables treating safety configuration as code:

This approach aligns AI agent safety with modern DevOps and infrastructure-as-code practices.

Cross-References

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$ npx @authensor/safeclaw