agent_stubs.py

# agent_stubs.py
"""
Agent implementations for the orchestrator

Core agents are fully implemented in src/agents/
Task-specific execution agents are implemented here
"""

import logging
from typing import Dict, Any, Optional
import asyncio

logger = logging.getLogger(__name__)

# Import the fully implemented core agents
from src.agents.intent_agent import IntentRecognitionAgent, create_intent_agent
from src.agents.synthesis_agent import EnhancedSynthesisAgent, create_synthesis_agent
from src.agents.safety_agent import SafetyCheckAgent, create_safety_agent
from src.agents.skills_identification_agent import SkillsIdentificationAgent, create_skills_identification_agent

# Compatibility wrappers for core agents
class IntentRecognitionAgentStub(IntentRecognitionAgent):
    """
    Wrapper for the fully implemented Intent Recognition Agent
    Maintains compatibility with orchestrator expectations
    """
    pass

class ResponseSynthesisAgentStub(EnhancedSynthesisAgent):
    """
    Wrapper for the fully implemented Enhanced Synthesis Agent
    Maintains compatibility with orchestrator expectations
    """
    pass

class SafetyCheckAgentStub(SafetyCheckAgent):
    """
    Wrapper for the fully implemented Safety Check Agent
    Maintains compatibility with orchestrator expectations
    """
    pass

class SkillsIdentificationAgentStub(SkillsIdentificationAgent):
    """
    Wrapper for the fully implemented Skills Identification Agent
    Maintains compatibility with orchestrator expectations
    """
    pass


# ============================================================================
# Task-Specific Execution Agents
# These agents handle specialized tasks in the execution plan
# ============================================================================

class TaskExecutionAgent:
    """
    Base class for task-specific execution agents
    Provides common functionality for all task agents
    """
    
    def __init__(self, llm_router, agent_id: str, task_name: str, specialization: str = ""):
        """
        Initialize task execution agent
        
        Args:
            llm_router: LLMRouter instance for making inference calls
            agent_id: Unique identifier for this agent
            task_name: Name of the task this agent handles
            specialization: Description of what this agent specializes in
        """
        self.llm_router = llm_router
        self.agent_id = agent_id
        self.task_name = task_name
        self.specialization = specialization or f"Specialized in {task_name} tasks"
        logger.info(f"Initialized {self.agent_id}: {self.specialization}")
    
    async def execute(self, user_input: str, context: Dict[str, Any] = None, 
                     previous_results: Dict[str, Any] = None, **kwargs) -> Dict[str, Any]:
        """
        Execute the agent's task
        
        Args:
            user_input: Original user query
            context: Conversation context
            previous_results: Results from previous sequential tasks
            **kwargs: Additional parameters
            
        Returns:
            Dict with task execution results
        """
        try:
            logger.info(f"{self.agent_id} executing task: {self.task_name}")
            
            # Build task-specific prompt
            prompt = self._build_execution_prompt(user_input, context, previous_results, **kwargs)
            
            # Execute via LLM router
            logger.debug(f"{self.agent_id} calling LLM router for {self.task_name}")
            result = await self.llm_router.route_inference(
                task_type="general_reasoning",
                prompt=prompt,
                max_tokens=kwargs.get('max_tokens', 2000),
                temperature=kwargs.get('temperature', 0.7)
            )
            
            if result:
                return {
                    "agent_id": self.agent_id,
                    "task": self.task_name,
                    "status": "completed",
                    "content": result,
                    "content_length": len(str(result)),
                    "method": "llm_enhanced"
                }
            else:
                logger.warning(f"{self.agent_id} returned empty result")
                return {
                    "agent_id": self.agent_id,
                    "task": self.task_name,
                    "status": "empty",
                    "content": "",
                    "content_length": 0,
                    "method": "llm_enhanced"
                }
                
        except Exception as e:
            logger.error(f"{self.agent_id} execution failed: {e}", exc_info=True)
            return {
                "agent_id": self.agent_id,
                "task": self.task_name,
                "status": "error",
                "error": str(e),
                "content": "",
                "method": "llm_enhanced"
            }
    
    def _build_execution_prompt(self, user_input: str, context: Dict[str, Any] = None,
                               previous_results: Dict[str, Any] = None, **kwargs) -> str:
        """
        Build task-specific execution prompt
        Override in subclasses for custom prompt building
        """
        # Build context summary
        context_summary = self._build_context_summary(context)
        
        # Base prompt structure
        prompt = f"""User Query: {user_input}

Context: {context_summary}
"""
        
        # Add previous results if sequential execution
        if previous_results:
            prompt += f"\nPrevious Task Results:\n{self._format_previous_results(previous_results)}\n"
        
        # Add task-specific instructions
        prompt += f"\n{self._get_task_instructions()}"
        
        return prompt
    
    def _build_context_summary(self, context: Dict[str, Any] = None) -> str:
        """Build concise context summary"""
        if not context:
            return "No prior context"
        
        summary_parts = []
        
        # Extract interaction contexts
        interaction_contexts = context.get('interaction_contexts', [])
        if interaction_contexts:
            recent_summaries = [ic.get('summary', '') for ic in interaction_contexts[-3:]]
            if recent_summaries:
                summary_parts.append(f"Recent topics: {', '.join(recent_summaries)}")
        
        # Extract user context
        user_context = context.get('user_context', '')
        if user_context:
            summary_parts.append(f"User background: {user_context[:200]}")
        
        return " | ".join(summary_parts) if summary_parts else "No prior context"
    
    def _format_previous_results(self, previous_results: Dict[str, Any]) -> str:
        """Format previous task results for inclusion in prompt"""
        formatted = []
        for task_name, result in previous_results.items():
            if isinstance(result, dict):
                content = result.get('content', result.get('result', ''))
                if content:
                    formatted.append(f"- {task_name}: {str(content)[:500]}")
        return "\n".join(formatted) if formatted else "No previous results"
    
    def _get_task_instructions(self) -> str:
        """
        Get task-specific instructions
        Override in subclasses
        """
        return f"Task: Execute {self.task_name} based on the user query and context."


# ============================================================================
# Specific Task Execution Agents
# ============================================================================

class InformationGatheringAgent(TaskExecutionAgent):
    """Agent specialized in gathering comprehensive information"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="INFO_GATH_001",
            task_name="information_gathering",
            specialization="Comprehensive information gathering and fact verification"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Gather comprehensive, accurate information relevant to the user's query.
- Focus on facts, definitions, explanations, and verified information
- Structure the information clearly with key points
- Cite important details and provide context
- Ensure accuracy and completeness"""


class ContentResearchAgent(TaskExecutionAgent):
    """Agent specialized in researching and compiling content"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="CONTENT_RESEARCH_001",
            task_name="content_research",
            specialization="Detailed content research and compilation"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Research and compile detailed content about the topic.
- Include multiple perspectives and viewpoints
- Gather current information and relevant examples
- Organize findings logically with clear sections
- Provide comprehensive coverage of the topic"""


class TaskPlanningAgent(TaskExecutionAgent):
    """Agent specialized in creating detailed execution plans"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="TASK_PLAN_001",
            task_name="task_planning",
            specialization="Detailed task planning and execution strategy"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Create a detailed execution plan for the requested task.
- Break down into clear, actionable steps
- Identify requirements and dependencies
- Outline expected outcomes and success criteria
- Consider potential challenges and solutions
- Provide timeline and resource estimates"""


class ExecutionStrategyAgent(TaskExecutionAgent):
    """Agent specialized in developing strategic approaches"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="EXEC_STRAT_001",
            task_name="execution_strategy",
            specialization="Strategic execution methodology development"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Develop a strategic approach for task execution.
- Define methodology and best practices
- Identify implementation considerations
- Provide actionable guidance with clear priorities
- Consider efficiency and effectiveness
- Address risk mitigation strategies"""


class CreativeBrainstormingAgent(TaskExecutionAgent):
    """Agent specialized in creative ideation"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="CREATIVE_BS_001",
            task_name="creative_brainstorming",
            specialization="Creative ideas generation and brainstorming"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Generate creative ideas and approaches for content creation.
- Explore different angles, styles, and formats
- Provide diverse creative options
- Include implementation suggestions
- Encourage innovative thinking
- Balance creativity with practicality"""


class ContentIdeationAgent(TaskExecutionAgent):
    """Agent specialized in content concept development"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="CONTENT_IDEATION_001",
            task_name="content_ideation",
            specialization="Content concepts and ideation development"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Develop content concepts and detailed ideation.
- Create outlines and structural frameworks
- Define themes and key messaging
- Suggest variations and refinement paths
- Provide detailed development paths
- Consider audience and purpose"""


class ResearchAnalysisAgent(TaskExecutionAgent):
    """Agent specialized in research analysis"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="RESEARCH_ANALYSIS_001",
            task_name="research_analysis",
            specialization="Thorough research analysis and insights"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Conduct thorough research analysis on the topic.
- Identify key findings, trends, and patterns
- Analyze different perspectives and methodologies
- Provide comprehensive insights
- Evaluate evidence and sources
- Synthesize complex information"""


class DataCollectionAgent(TaskExecutionAgent):
    """Agent specialized in data collection and organization"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="DATA_COLLECT_001",
            task_name="data_collection",
            specialization="Data point collection and evidence gathering"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Collect and organize relevant data points and evidence.
- Gather statistics, examples, and case studies
- Compile supporting information
- Structure data for easy analysis and reference
- Verify data quality and relevance
- Organize systematically"""


class PatternIdentificationAgent(TaskExecutionAgent):
    """Agent specialized in pattern recognition and analysis"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="PATTERN_ID_001",
            task_name="pattern_identification",
            specialization="Pattern recognition and correlation analysis"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Identify patterns, correlations, and significant relationships.
- Analyze trends and cause-effect relationships
- Discover underlying structures
- Provide insights based on pattern recognition
- Identify anomalies and exceptions
- Connect disparate information"""


class ProblemAnalysisAgent(TaskExecutionAgent):
    """Agent specialized in problem analysis"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="PROBLEM_ANALYSIS_001",
            task_name="problem_analysis",
            specialization="Detailed problem analysis and root cause identification"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Analyze the problem in detail.
- Identify root causes and contributing factors
- Understand constraints and limitations
- Break down the problem into components
- Map problem relationships
- Prioritize issues for systematic resolution"""


class SolutionResearchAgent(TaskExecutionAgent):
    """Agent specialized in solution research and evaluation"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="SOLUTION_RESEARCH_001",
            task_name="solution_research",
            specialization="Solution research and evaluation"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Research and evaluate potential solutions.
- Compare different approaches and methodologies
- Assess pros and cons of each option
- Recommend best practices
- Consider implementation feasibility
- Evaluate effectiveness and efficiency"""


class CurriculumPlanningAgent(TaskExecutionAgent):
    """Agent specialized in educational curriculum design"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="CURRICULUM_PLAN_001",
            task_name="curriculum_planning",
            specialization="Educational curriculum and learning path design"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Design educational curriculum and learning path.
- Structure content progressively
- Define clear learning objectives
- Suggest appropriate resources
- Create comprehensive learning framework
- Ensure pedagogical effectiveness"""


class EducationalContentAgent(TaskExecutionAgent):
    """Agent specialized in educational content generation"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="EDUC_CONTENT_001",
            task_name="educational_content",
            specialization="Educational content with clear explanations"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Generate educational content with clear explanations.
- Use effective teaching methods
- Provide examples and analogies
- Manage progressive complexity
- Make content accessible and engaging
- Support learning objectives"""


class TechnicalResearchAgent(TaskExecutionAgent):
    """Agent specialized in technical research"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="TECH_RESEARCH_001",
            task_name="technical_research",
            specialization="Technical aspects and solutions research"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Research technical aspects and solutions.
- Gather technical documentation
- Identify best practices and standards
- Compile implementation details
- Structure technical information clearly
- Provide practical guidance"""


class GuidanceGenerationAgent(TaskExecutionAgent):
    """Agent specialized in step-by-step guidance"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="GUIDANCE_GEN_001",
            task_name="guidance_generation",
            specialization="Step-by-step guidance and instructions"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Generate step-by-step guidance and instructions.
- Create clear, actionable steps
- Provide detailed explanations
- Include troubleshooting tips
- Ensure comprehensiveness
- Make guidance easy to follow"""


class ContextEnrichmentAgent(TaskExecutionAgent):
    """Agent specialized in context enrichment"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="CONTEXT_ENRICH_001",
            task_name="context_enrichment",
            specialization="Conversation context enrichment"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Enrich the conversation with relevant context and insights.
- Add helpful background information
- Connect to previous topics
- Include engaging details
- Enhance understanding
- Maintain conversation flow"""


class GeneralResearchAgent(TaskExecutionAgent):
    """Agent for general research tasks"""
    
    def __init__(self, llm_router):
        super().__init__(
            llm_router,
            agent_id="GENERAL_RESEARCH_001",
            task_name="general_research",
            specialization="General research and information gathering"
        )
    
    def _get_task_instructions(self) -> str:
        return """Task: Conduct general research and information gathering.
- Compile relevant information
- Gather insights and useful details
- Organize findings clearly
- Provide comprehensive coverage
- Structure for easy reference"""


# ============================================================================
# Factory Functions for Task Execution Agents
# ============================================================================

def create_task_execution_agent(task_name: str, llm_router) -> TaskExecutionAgent:
    """
    Factory function to create task-specific execution agents
    
    Args:
        task_name: Name of the task to create an agent for
        llm_router: LLMRouter instance
        
    Returns:
        Appropriate TaskExecutionAgent instance
    """
    agent_map = {
        "information_gathering": InformationGatheringAgent,
        "content_research": ContentResearchAgent,
        "task_planning": TaskPlanningAgent,
        "execution_strategy": ExecutionStrategyAgent,
        "creative_brainstorming": CreativeBrainstormingAgent,
        "content_ideation": ContentIdeationAgent,
        "research_analysis": ResearchAnalysisAgent,
        "data_collection": DataCollectionAgent,
        "pattern_identification": PatternIdentificationAgent,
        "problem_analysis": ProblemAnalysisAgent,
        "solution_research": SolutionResearchAgent,
        "curriculum_planning": CurriculumPlanningAgent,
        "educational_content": EducationalContentAgent,
        "technical_research": TechnicalResearchAgent,
        "guidance_generation": GuidanceGenerationAgent,
        "context_enrichment": ContextEnrichmentAgent,
        "general_research": GeneralResearchAgent,
    }
    
    agent_class = agent_map.get(task_name, GeneralResearchAgent)
    return agent_class(llm_router)


def create_task_execution_agents(task_names: list, llm_router) -> Dict[str, TaskExecutionAgent]:
    """
    Factory function to create multiple task execution agents
    
    Args:
        task_names: List of task names to create agents for
        llm_router: LLMRouter instance
        
    Returns:
        Dictionary mapping task names to agent instances
    """
    agents = {}
    for task_name in task_names:
        agents[task_name] = create_task_execution_agent(task_name, llm_router)
    return agents