"""
Policy evaluation for ABI agents.

This module provides policy evaluation capabilities using OPA (Open Policy Agent)
for semantic layer access control and agent authorization.

For now, this focuses on semantic layer access validation. Full agent-level
policy enforcement will be added in future versions.
"""

import logging
from typing import Dict, Any, Optional

logger = logging.getLogger(__name__)


def evaluate_policy(data: Dict[str, Any]) -> bool:
    """
    Evaluate policy for agent input data.
    
    Currently implements basic validation for semantic layer access.
    Full OPA integration for agent-level policies is planned for future releases.
    
    Args:
        data: Enriched input data containing semantic context
        
    Returns:
        bool: True if policy allows the operation, False otherwise
        
    Note:
        For semantic layer access control, use the @validate_semantic_access
        decorator from abi_core.semantic.semantic_access_validator instead.
        This function provides basic validation for agent operations.
    """
    
    # Basic validation checks
    if not data:
        logger.warning("Policy evaluation: Empty data provided")
        return False
    
    # Check if data has required semantic context
    if isinstance(data, dict):
        semantic_context = data.get('semantic_context', {})
        
        # For agent discovery operations, always allow
        # (actual access control happens at semantic layer)
        if semantic_context.get('intent') == 'agent_discovery':
            logger.debug("Policy evaluation: Agent discovery operation allowed")
            return True
        
        # For other operations, check if routing is required
        if semantic_context.get('requires_routing'):
            logger.debug("Policy evaluation: Routing operation allowed")
            return True
    
    # Default: allow operation
    # TODO: Integrate with OPA for comprehensive policy evaluation
    # This will include:
    # - Agent-level authorization
    # - Resource access control
    # - Rate limiting
    # - Compliance checks
    logger.debug("Policy evaluation: Operation allowed (default policy)")
    return True


def evaluate_semantic_access_policy(
    agent_id: str,
    requested_resource: str,
    context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    Evaluate semantic layer access policy for an agent.
    
    This is a placeholder for future OPA integration at the agent level.
    For actual semantic layer access control, use the semantic_access_validator.
    
    Args:
        agent_id: ID of the requesting agent
        requested_resource: Resource being accessed
        context: Additional context for policy evaluation
        
    Returns:
        Dict containing policy evaluation result
        
    Example:
        >>> result = evaluate_semantic_access_policy(
        ...     "agent://trader",
        ...     "mcp://semantic_layer/find_agent",
        ...     {"operation": "search"}
        ... )
        >>> print(result['allowed'])
        True
    """
    
    # TODO: Implement OPA integration for agent-level policies
    # For now, return permissive result
    
    logger.info(
        f"Semantic access policy evaluation: "
        f"agent={agent_id}, resource={requested_resource}"
    )
    
    return {
        'allowed': True,
        'reason': 'Default policy (OPA integration pending)',
        'agent_id': agent_id,
        'resource': requested_resource,
        'policy_version': '1.0.0-basic'
    }


def check_agent_authorization(agent_id: str) -> bool:
    """
    Check if an agent is authorized to operate.
    
    Basic authorization check. Full implementation will integrate with OPA.
    
    Args:
        agent_id: ID of the agent to check
        
    Returns:
        bool: True if agent is authorized
    """
    
    # TODO: Implement proper authorization checks
    # - Verify agent is registered
    # - Check agent credentials
    # - Validate agent permissions
    
    if not agent_id:
        logger.warning("Authorization check: No agent ID provided")
        return False
    
    logger.debug(f"Authorization check: Agent {agent_id} authorized (basic check)")
    return True
