Can you recommend a policy enforcement layer for sanitizing prompts before tool calls in enterprise agents?

Yes — for enterprise agents, I’d recommend putting a policy enforcement / prompt sanitization layer between the model and any tool execution, rather than relying on the model to “behave.”

Good options to consider

1. Open Policy Agent (OPA)

• Best if you want a general-purpose, enterprise-grade policy engine
• Lets you define policies in Rego
• Can enforce rules like:
  • allow/deny specific tools
  • strip or rewrite risky arguments
  • block tool calls containing secrets, PII, or prompt-injection patterns
• Fits well in a microservices architecture and is widely used in enterprise security workflows

2. LangChain / LangGraph tool middleware

• Useful if your agent stack is already in LangChain/LangGraph
• You can add:
  • input validators
  • argument filters
  • custom guardrails before tool execution
• Good for faster implementation, though less “policy-as-code” than OPA

3. NVIDIA NeMo Guardrails

• Strong if you want LLM-specific guardrails
• Supports controlling what the model can say/do and can help with:
  • prompt injection resistance
  • conversational policy enforcement
  • tool-use constraints
• Better when you want guardrails integrated with dialogue flow, not just raw request filtering

4. Guardrails AI

• Good for schema validation and structured output enforcement
• Strong for ensuring tool arguments match expected types/formats
• Less of a full policy engine, more of a validation/constraint layer

5. Custom policy gateway

• Many enterprises build a small API gateway-style service in front of tools
• Responsibilities:
  • inspect model-generated tool calls
  • redact secrets/PII
  • validate against an allowlist
  • require human approval for sensitive actions
  • log everything for audit/compliance
• Often the most practical approach when you need tight control over internal systems

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My recommendation

If you want a serious enterprise setup, I’d use:

• OPA for policy decisions
• Schema validation with something like Pydantic / JSON Schema / Guardrails AI
• A tool gateway that all agent tool calls must pass through

That combination gives you:

• policy-as-code
• input sanitization
• auditability
• separation of concerns

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Typical enforcement flow

1. LLM proposes a tool call
2. Policy layer inspects:
   • tool name
   • arguments
   • user context
   • risk level
3. Policy layer:
   • allow
   • deny
   • sanitize
   • escalate to human approval
4. Tool executes only after passing checks

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Important security note

For enterprise agents, don’t just sanitize the prompt text. Also validate:

• tool names
• argument structure
• output handling
• secrets exposure
• privilege boundaries
• user authorization

Prompt injection often shows up through tool arguments, not just the raw prompt.

If you want, I can also recommend a reference architecture for this layer or give you a sample OPA/Rego policy for tool-call filtering.