ARIA Policy Implementation¶

Overview¶

ARIA's policy system provides a flexible and robust framework for managing AI participation in software projects. This document details the technical implementation of the policy system, which supports two distinct policy formats: capability-based (for testing) and model-based (for production).

Policy Formats¶

Capability-Based Format¶

Designed primarily for testing and human readability: - Focus on specific AI capabilities - Clear conditions and restrictions - Simplified structure for non-technical users - Easier to understand and validate

Model-Based Format¶

Designed for production environments: - Integration with code repositories - Path-specific rules - Predefined models with default settings - More granular control over permissions

Core Components¶

AIAction Enum¶

Defines possible AI interactions with the codebase: - GENERATE: Create new code - MODIFY: Change existing code - SUGGEST: Propose changes - REVIEW: Analyze code - EXECUTE: Run code or commands

AIPermission Class¶

Represents permission settings for specific actions: - Action type - Requirements (e.g., human review) - Constraints (e.g., path patterns)

PolicyModel Enum¶

Predefined participation models: - GUARDIAN: Complete restriction - OBSERVER: Analysis and review only - ASSISTANT: Suggestions with human review - COLLABORATOR: Area-specific permissions - PARTNER: Maximum participation with guardrails

PathPolicy Class¶

Manages path-specific rules: - Path patterns - Allowed actions - Required validations - Inheritance rules

AIPolicy Class¶

Overall policy management: - Policy loading/saving - Permission validation - Model enforcement - Path matching - Support for multiple policy formats

PolicyManager Class¶

Central policy coordination: - Policy configuration - Permission checking - Rule inheritance - Validation pipeline

Implementation Details¶

Capability-Based Configuration¶

version: 1.0.0
name: test_policy
description: A comprehensive test policy
capabilities:
  - name: code_generation
    description: Generate code based on user requirements
    allowed: true
    conditions:
      - Must include appropriate comments.
      - Must follow project coding standards.
restrictions:
  - Must not retain user data beyond the session.
  - Must inform users about limitations.

Model-Based Configuration¶

version: 1.0.0
name: production_policy
model: ASSISTANT
defaults:
  allow:
    - review
    - suggest
  require:
    - human_review
paths:
  'src/**/*.py':
    allow:
      - generate
      - modify
    require:
      - unit_tests
  'docs/**':
    allow:
      - generate
      - format

Permission Checking¶

1. Detect policy format (capability or model-based)
2. For model-based:
3. Path matching using glob patterns
4. Model-based permission inheritance
5. Explicit permission validation
6. Requirement verification
7. For capability-based:
8. Capability validation
9. Condition checking
10. Restriction enforcement

Validation Pipeline¶

1. Common validation:
2. Required fields check
3. Type validation
4. Format-specific validation:
5. Capability-based validation
6. Model-based validation
7. Strict validation (optional):
8. Enhanced quality checks
9. Detailed warnings
10. Result compilation

Integration Points¶

• CI/CD hooks
• IDE plugins
• Git pre-commit hooks
• Policy documentation generation

Best Practices¶

Policy Format Selection¶

1. Use capability-based format for:
2. Testing and development
3. Non-technical stakeholders
4. Simple use cases
5. Use model-based format for:
6. Production environments
7. Complex codebases
8. Integration with CI/CD

Policy Definition¶

1. Start with a restrictive model
2. Use explicit permissions
3. Define clear path patterns
4. Document requirements

Policy Management¶

1. Regular policy reviews
2. Version control integration
3. Automated validation
4. Clear documentation

Future Enhancements¶

1. Enhanced policy analytics
2. Machine learning-based policy recommendations
3. Automated policy testing
4. Integration with more development tools
5. Policy visualization tools