agent-policy-guardrails-engine
Build, run, and extend the Agent Policy & Guardrails Engine. Use when implementing policy formats, enforcement logic, decision conflict resolution, policy APIs, and audit/compliance workflows.
Why use this skill?
Implement robust policy enforcement and guardrails for your AI agents. Manage complex decision logic, compliance, and safety workflows with our modular, extensible engine.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/adad44/guardrailsfor-agentWhat This Skill Does
The Agent Policy & Guardrails Engine is a robust framework designed to manage and enforce security, operational, and compliance constraints for AI agent workflows. It serves as the central control plane for decision-making, ensuring that every tool invocation or action taken by an agent adheres to predefined policies. The engine processes inputs against structured policies, resolves conflicts based on severity and priority, and returns a decisive action: ALLOW, DENY, MODIFY, or REQUIRE_APPROVAL. By decoupling policy logic from agent execution, this skill allows for granular governance, enabling developers to scale agent autonomy without compromising safety or regulatory requirements. Whether you are managing financial transaction limits, restricting data access, or requiring human intervention for sensitive tasks, this engine provides a standardized, testable, and deterministic architecture to handle these complex requirements seamlessly.
Installation
You can install this skill directly via the OpenClaw hub CLI:
clawhub install openclaw/skills/skills/adad44/guardrailsfor-agent
Ensure you have the required dependencies listed in the requirements.txt file and run your local environment with python3 -m uvicorn app.main:app --reload.
Use Cases
- Financial Governance: Restrict agents from performing transactions above a certain dollar amount without explicit human approval.
- Data Privacy Enforcement: Automatically strip PII from payloads before they reach external APIs, utilizing the
MODIFYaction. - Operational Compliance: Ensure that agents only communicate through authorized channels and during specific business hours.
- Risk Mitigation: Block any action identified as "high-risk" based on the context of the current session or the specific tool being requested.
- Decision Conflict Resolution: Manage complex environments where multiple policies might apply to a single action, ensuring the most restrictive or highest-priority rule takes precedence.
Example Prompts
- "Check the current policy set and add a rule that denies any file deletion actions initiated by the agent unless the user provides an override code."
- "Update the guardrails engine to require manual approval for all outbound API requests that involve our production database endpoint."
- "Evaluate if the pending action to post an update on Twitter violates our brand communication guidelines and apply modifications if necessary."
Tips & Limitations
- Deterministic Logic: Always aim for deterministic policy definitions. Avoid reliance on fluctuating external state unless it is explicitly handled by the service layer.
- Schema First: Always define your schemas in
app/schemas.pybefore modifying the engine. This ensures the parser and the enforcement engine stay in sync. - Testing: The reliability of your guardrails depends on your test coverage. Maintain rigorous tests in
tests/test_engine.pyspecifically for complex conflict resolution scenarios where policies overlap. - Limitations: Note that complex natural language policies are compiled into structured formats; ensure that the conversion process is validated periodically to avoid misinterpretation of guardrail intent.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-adad44-guardrailsfor-agent": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: code-execution