All Toolsโ€บPolicy Enforcer
๐Ÿ”ง AI agent governanceJune 13, 2026โœ… Tests passing

Policy Enforcer

A lightweight Python library for defining and enforcing governance policies for AI agent actions. Developers can define rulesets (e.g., allowable actions, thresholds, or resource usage limits) and dynamically apply them to monitor and restrict agent behavior in production.

What It Does

  • Define custom policy rules for agent behavior.
  • Cross-check agent actions against defined policies.
  • Event-based triggers for violations, such as alerts or action rollbacks.

Installation

Install the library using pip:

pip install -r requirements.txt

Usage

Example

from policy_enforcer import PolicyEnforcer

rules = [
    {
        "name": "Limit action type",
        "condition": {"type": {"allowed": ["read", "write"]}},
        "message": "Action type not allowed"
    }
]

action = {"type": "delete"}

enforcer = PolicyEnforcer(rules)
result = enforcer.check(action)

print(result)

CLI Usage

python policy_enforcer.py <rules.json> <action.json>

Source Code

import json
from typing import Dict, Any, List
from pydantic import BaseModel, ValidationError
from cerberus import Validator

class PolicyRule(BaseModel):
    name: str
    condition: Dict[str, Any]
    message: str

class PolicyEnforcer:
    def __init__(self, rules: List[Dict[str, Any]]):
        """
        Initialize the PolicyEnforcer with a list of rules.

        :param rules: List of dictionaries defining policy rules.
        """
        self.rules = []
        for rule in rules:
            try:
                validated_rule = PolicyRule(**rule)
                self.rules.append(validated_rule)
            except ValidationError as e:
                raise ValueError(f"Invalid rule definition: {e}")

    def check(self, action: Dict[str, Any]) -> Dict[str, Any]:
        """
        Check an agent action against the defined rules.

        :param action: Dictionary defining the agent action.
        :return: Compliance report with violations if any.
        """
        validator = Validator()
        violations = []

        for rule in self.rules:
            validator.schema = rule.condition
            if not validator.validate(action):
                violations.append({
                    "rule": rule.name,
                    "message": rule.message,
                    "errors": validator.errors
                })

        return {
            "compliant": len(violations) == 0,
            "violations": violations
        }

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Policy Enforcer CLI")
    parser.add_argument("rules", type=str, help="Path to JSON file containing policy rules")
    parser.add_argument("action", type=str, help="Path to JSON file containing agent action")

    args = parser.parse_args()

    try:
        with open(args.rules, "r") as rules_file:
            rules = json.load(rules_file)

        with open(args.action, "r") as action_file:
            action = json.load(action_file)

        enforcer = PolicyEnforcer(rules)
        result = enforcer.check(action)

        print(json.dumps(result, indent=4))
    except FileNotFoundError as e:
        print(f"Error: {e}")
    except json.JSONDecodeError as e:
        print(f"Error: Invalid JSON format - {e}")
    except ValueError as e:
        print(f"Error: {e}")

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Details

Tool Name
policy_enforcer
Category
AI agent governance
Generated
June 13, 2026
Tests
Passing โœ…

Quick Install

Clone just this tool:

git clone --depth 1 --filter=blob:none --sparse \
  https://github.com/ptulin/autoaiforge.git
cd autoaiforge
git sparse-checkout set generated_tools/2026-06-13/policy_enforcer
cd generated_tools/2026-06-13/policy_enforcer
pip install -r requirements.txt 2>/dev/null || true
python policy_enforcer.py
Policy Enforcer โ€” AI Tools by AutoAIForge