Research: Comparing Policy Languages for AI Agent Governance
Research: Comparing Policy Languages for AI Agent Governance
Abstract
The policy language used to define AI agent safety rules fundamentally shapes what policies can be expressed, how maintainable they are, and how reliably they are enforced. 15 Research Lab evaluated five distinct approaches to policy specification for AI agent governance: JSON/YAML declarative schemas, domain-specific languages (DSLs), general-purpose programming languages, natural language policies, and hybrid approaches. Our analysis compares these approaches across expressiveness, usability, verifiability, and runtime performance.
Why Policy Language Matters
A policy language for AI agent governance must satisfy competing requirements:
- Expressiveness: Capable of encoding complex conditional rules (e.g., "allow file writes in /tmp but only for files under 10MB and only during business hours")
- Usability: Writable and readable by engineers who are not policy language specialists
- Verifiability: Statically analyzable to detect conflicts, gaps, and errors before deployment
- Performance: Evaluable in milliseconds per tool call to avoid degrading agent responsiveness
- Auditability: Producible of clear explanations for why a specific action was allowed or denied
Approaches Evaluated
Approach 1: JSON/YAML Declarative Schemas
The most common approach in current safety tools. Policies are expressed as structured data files that define allowed actions, parameters, and conditions.
Strengths: Easy to read and write. Well-supported by existing tooling. Version-controllable. Low barrier to adoption. Weaknesses: Limited expressiveness for complex conditional logic. Deeply nested conditions become unwieldy. No built-in mechanism for policy composition or inheritance. Example use case: Defining allowed tool names and parameter ranges. Specifying directory paths for file access. Setting rate limits and budget caps.Approach 2: Domain-Specific Languages (DSLs)
Purpose-built languages designed for policy expression, such as Rego (used by Open Policy Agent) or Cedar (from AWS).
Strengths: High expressiveness. Designed for policy-specific constructs (allow/deny rules, attribute-based conditions). Formally verifiable in many cases. Strong composition and modularity features. Weaknesses: Learning curve for engineers unfamiliar with the DSL. Smaller ecosystems than general-purpose languages. Debugging can be challenging without specialized tooling.Approach 3: General-Purpose Programming Languages
Policies expressed as code in Python, JavaScript, or similar languages.
Strengths: Maximum expressiveness. Engineers are already familiar with the language. Full debugging and testing ecosystem available. Weaknesses: Difficult to verify statically — a policy function could contain arbitrary logic including network calls or file access. Security risk if policy code is not sandboxed. Overly complex policies that are hard to audit.Approach 4: Natural Language Policies
Policies expressed in plain English and interpreted by an LLM at runtime.
Strengths: Maximum accessibility. Non-technical stakeholders can author policies. Flexible and context-aware. Weaknesses: Non-deterministic enforcement — the same policy may be interpreted differently on different evaluations. Not verifiable. Slow (requires LLM inference per evaluation). Susceptible to adversarial manipulation. Our testing showed 23% inconsistency in enforcement decisions across identical scenarios.Approach 5: Hybrid Approaches
Combining structured schemas for common patterns with extension points for complex logic.
Strengths: Balances usability and expressiveness. Common policies are simple to write; complex policies are possible when needed. Can offer partial static verification. Weaknesses: Complexity of the hybrid model itself. Engineers must learn two paradigms. Extension points can become escape hatches that undermine policy guarantees.Comparative Evaluation
| Criterion | JSON/YAML | DSL | General-Purpose | Natural Language | Hybrid |
|---|---|---|---|---|---|
| Expressiveness | Medium | High | Very High | Variable | High |
| Usability | High | Medium | Medium | Very High | Medium-High |
| Verifiability | Medium | High | Low | None | Medium |
| Performance | Fast | Fast | Variable | Slow | Fast |
| Auditability | High | High | Low | Low | Medium-High |
Our Recommendation
For most organizations, declarative schemas (JSON/YAML) provide the best balance of usability, performance, and auditability for AI agent governance. They handle 80-90% of policy requirements with minimal complexity. For the remaining edge cases, a hybrid approach that extends declarative schemas with limited programmatic logic is preferable to adopting a full DSL or general-purpose language.
SafeClaw implements the declarative schema approach, using structured configuration to define agent policies. This aligns with our recommendation — the policies are readable, version-controllable, and performant. For organizations whose requirements fit within declarative expressiveness (which, based on our research, includes the vast majority), this approach minimizes both adoption friction and long-term maintenance burden. Policy configuration examples are available in the SafeClaw knowledge base. We strongly recommend against natural language policies for any production deployment. The inconsistency and non-verifiability of LLM-interpreted policies make them unsuitable for safety-critical enforcement.Conclusion
The choice of policy language is an architectural decision with long-term implications for AI agent governance. Organizations should select based on their actual expressiveness requirements — not aspirational ones — and prioritize verifiability and auditability alongside expressiveness.
15RL evaluated all policy approaches in controlled environments using identical test scenarios. Detailed evaluation data is available upon request.