Agent-to-Agent (A2A) Interaction in Generative AI: The Future of Multi-Agent LLM Systems

class HierarchicalAgentSystem:
    def __init__(self):
        self.coordinator = CoordinatorAgent()
        self.specialist_agents = {
            'data_analysis': DataAnalysisAgent(),
            'content_generation': ContentGenerationAgent(), 
            'quality_assurance': QualityAssuranceAgent()
        }
    
    def execute_task(self, task_description):
        # Coordinator analyzes task and creates execution plan
        plan = self.coordinator.create_execution_plan(task_description)
        
        # Delegate subtasks to specialist agents
        results = {}
        for subtask in plan.subtasks:
            agent = self.specialist_agents[subtask.agent_type]
            results[subtask.id] = agent.execute(subtask)
        
        # Coordinator synthesizes final result
        return self.coordinator.synthesize_results(results, plan)

class PeerToPeerAgent:
    def __init__(self, agent_id, capabilities):
        self.agent_id = agent_id
        self.capabilities = capabilities
        self.peer_registry = {}
    
    async def discover_peers(self):
        # Agent discovery using A2A protocol
        available_agents = await self.query_agent_registry()
        for agent_info in available_agents:
            if self.can_collaborate_with(agent_info):
                self.peer_registry[agent_info.id] = agent_info
    
    async def delegate_task(self, task, required_capability):
        suitable_peers = [agent for agent in self.peer_registry.values() 
                         if required_capability in agent.capabilities]
        
        if suitable_peers:
            chosen_peer = self.select_best_peer(suitable_peers, task)
            return await self.send_task_request(chosen_peer, task)
        
        return self.handle_task_locally(task)

// Agent metadata configuration
{
  "@context": "https://a2a-protocol.org/context",
  "@type": "AgentCard",
  "id": "data-analysis-agent-v1.0",
  "name": "Advanced Data Analysis Agent",
  "capabilities": [
    {
      "type": "data_processing",
      "input_formats": ["csv", "json", "parquet"],
      "output_formats": ["json", "visualization", "report"]
    }
  ],
  "communication": {
    "protocols": ["http", "websocket"],
    "message_format": "json",
    "streaming": true
  }
}
// End of AgentCard configuration

class CapabilityBasedCoordinator:
    def __init__(self):
        self.agent_capabilities = {}

    def register_agent(self, agent_id, capabilities):
        self.agent_capabilities[agent_id] = capabilities

    def find_suitable_agents(self, task_requirements):
        suitable_agents = []
        for agent_id, capabilities in self.agent_capabilities.items():
            compatibility_score = self.calculate_compatibility(
                capabilities, task_requirements
            )
            if compatibility_score > 0.7:  # Threshold for suitability
                suitable_agents.append((agent_id, compatibility_score))
        
        return sorted(suitable_agents, key=lambda x: x[1], reverse=True)

class MajorityVotingConsensus:
    def __init__(self, agents):
        self.agents = agents
    
    async def reach_consensus(self, problem):
        solutions = []
        for agent in self.agents:
            solution = await agent.solve(problem)
            solutions.append(solution)
        
        # Count votes for each unique solution
        vote_counts = {}
        for solution in solutions:
            solution_key = self.normalize_solution(solution)
            vote_counts[solution_key] = vote_counts.get(solution_key, 0) + 1
        
        # Return solution with most votes
        winner = max(vote_counts.items(), key=lambda x: x[1])
        return winner[0]

class SoftwareDevelopmentCrew:
    def __init__(self):
        self.architect = SoftwareArchitectAgent()
        self.developer = CodeDeveloperAgent() 
        self.reviewer = CodeReviewAgent()
        self.tester = TestEngineerAgent()
        self.documenter = DocumentationAgent()
    
    async def develop_feature(self, requirements):
        # Architecture design
        architecture = await self.architect.design_system(requirements)
        
        # Code implementation
        code = await self.developer.implement_feature(
            requirements, architecture
        )
        
        # Code review and improvement
        review_feedback = await self.reviewer.review_code(code)
        if review_feedback.needs_changes:
            code = await self.developer.refactor_code(
                code, review_feedback
            )
        
        # Test generation and execution
        tests = await self.tester.generate_tests(code, requirements)
        test_results = await self.tester.run_tests(tests)
        
        # Documentation generation
        docs = await self.documenter.create_documentation(
            code, architecture, requirements
        )
        
        return {
            'code': code,
            'tests': tests,
            'documentation': docs,
            'test_results': test_results
        }

class DataAnalysisCrew:
    def __init__(self):
        self.data_collector = DataCollectionAgent()
        self.statistician = StatisticalAnalysisAgent()
        self.domain_expert = DomainExpertiseAgent()
        self.visualizer = DataVisualizationAgent()
        self.interpreter = InsightInterpretationAgent()
    
    async def analyze_dataset(self, analysis_request):
        # Data collection and preparation
        dataset = await self.data_collector.collect_and_clean_data(
            analysis_request.data_sources
        )
        
        # Statistical analysis
        statistical_results = await self.statistician.perform_analysis(
            dataset, analysis_request.analysis_type
        )
        
        # Domain-specific interpretation
        domain_insights = await self.domain_expert.interpret_findings(
            statistical_results, analysis_request.domain
        )
        
        # Visualization creation
        visualizations = await self.visualizer.create_visualizations(
            dataset, statistical_results, analysis_request.visualization_preferences
        )
        
        # Comprehensive insight generation
        final_insights = await self.interpreter.synthesize_insights(
            statistical_results, domain_insights, visualizations
        )
        
        return {
            'dataset': dataset,
            'statistical_analysis': statistical_results,
            'domain_insights': domain_insights,
            'visualizations': visualizations,
            'final_report': final_insights
        }

class EconomicMarketSimulation:
    def __init__(self):
        self.market_maker = MarketMakerAgent()
        self.institutional_traders = [InstitutionalTraderAgent() 
                                    for _ in range(5)]
        self.retail_traders = [RetailTraderAgent() 
                             for _ in range(100)]
        self.regulatory_agent = RegulatoryComplianceAgent()
        self.news_agent = MarketNewsAgent()
    
    async def simulate_trading_day(self, market_conditions):
        # Initialize market conditions
        await self.market_maker.set_initial_conditions(market_conditions)
        
        # Generate market events
        events = await self.news_agent.generate_market_events()
        
        # Execute trading simulation with agent interactions
        for time_step in range(market_conditions.trading_hours):
            # Institutional trading strategies
            institutional_actions = await asyncio.gather(*[
                trader.make_trading_decision(market_conditions, events)
                for trader in self.institutional_traders
            ])
            
            # Retail trading responses  
            retail_actions = await asyncio.gather(*[
                trader.react_to_market(market_conditions, events)
                for trader in self.retail_traders
            ])
            
            # Market maker adjustments
            market_adjustments = await self.market_maker.adjust_market(
                institutional_actions + retail_actions
            )
            
            # Regulatory compliance monitoring
            compliance_issues = await self.regulatory_agent.monitor_compliance(
                institutional_actions + retail_actions
            )
            
            # Update market conditions for next iteration
            market_conditions = self.update_market_state(
                market_conditions, market_adjustments, compliance_issues
            )
        
        return self.generate_simulation_report(market_conditions)

class EcosystemSimulation:
    def __init__(self):
        self.predator_agents = [PredatorAgent(species) for species in ['wolf', 'bear']]
        self.prey_agents = [PreyAgent(species) for species in ['deer', 'rabbit']]  
        self.vegetation_agent = VegetationGrowthAgent()
        self.climate_agent = ClimateConditionsAgent()
        self.human_impact_agent = HumanActivityAgent()
    
    async def simulate_ecosystem_dynamics(self, simulation_parameters):
        for year in range(simulation_parameters.duration_years):
            # Update environmental conditions
            climate_conditions = await self.climate_agent.generate_yearly_conditions()
            vegetation_state = await self.vegetation_agent.update_vegetation(
                climate_conditions
            )
            
            # Predator-prey interactions
            for season in ['spring', 'summer', 'fall', 'winter']:
                season_conditions = {
                    'climate': climate_conditions[season],
                    'vegetation': vegetation_state[season]
                }
                
                # Prey behavior and population dynamics
                prey_actions = await asyncio.gather(*[
                    agent.seasonal_behavior(season_conditions)
                    for agent in self.prey_agents
                ])
                
                # Predator hunting and territory management
                predator_actions = await asyncio.gather(*[
                    agent.hunting_behavior(season_conditions, prey_actions)
                    for agent in self.predator_agents
                ])
                
                # Human impact assessment
                human_impact = await self.human_impact_agent.assess_impact(
                    season_conditions, prey_actions, predator_actions
                )
                
                # Update population dynamics
                self.update_population_dynamics(
                    prey_actions, predator_actions, human_impact
                )
        
        return self.generate_ecosystem_report()

class CustomerServiceCrew:
    def __init__(self):
        self.intake_agent = CustomerIntakeAgent()
        self.technical_support = TechnicalSupportAgent()
        self.billing_specialist = BillingSpecialistAgent()
        self.escalation_manager = EscalationManagerAgent()
        self.satisfaction_monitor = CustomerSatisfactionAgent()
    
    async def handle_customer_inquiry(self, customer_inquiry):
        # Initial inquiry processing
        inquiry_analysis = await self.intake_agent.analyze_inquiry(customer_inquiry)
        
        # Route to appropriate specialist
        if inquiry_analysis.category == 'technical':
            resolution = await self.technical_support.resolve_issue(
                customer_inquiry, inquiry_analysis
            )
        elif inquiry_analysis.category == 'billing':
            resolution = await self.billing_specialist.handle_billing_issue(
                customer_inquiry, inquiry_analysis
            )
        else:
            resolution = await self.escalation_manager.handle_complex_case(
                customer_inquiry, inquiry_analysis
            )
        
        # Monitor customer satisfaction
        satisfaction_score = await self.satisfaction_monitor.evaluate_resolution(
            customer_inquiry, resolution
        )
        
        # Escalate if satisfaction is low
        if satisfaction_score < 0.7:
            resolution = await self.escalation_manager.improve_resolution(
                customer_inquiry, resolution, satisfaction_score
            )
        
        return {
            'resolution': resolution,
            'satisfaction_score': satisfaction_score,
            'handling_time': resolution.duration,
            'agent_involved': resolution.primary_agent
        }

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver

class MultiAgentState(TypedDict):
    messages: List[BaseMessage]
    current_agent: str
    task_status: str
    intermediate_results: Dict[str, Any]

# Create specialized agent nodes
def research_agent_node(state: MultiAgentState):
    # Research agent implementation
    research_results = research_agent.invoke(state["messages"])
    return {
        "messages": state["messages"] + [research_results],
        "current_agent": "research",
        "intermediate_results": {
            **state["intermediate_results"],
            "research_data": research_results.content
        }
    }

def analysis_agent_node(state: MultiAgentState):
    # Analysis agent implementation
    analysis_results = analysis_agent.invoke(state["messages"])
    return {
        "messages": state["messages"] + [analysis_results],
        "current_agent": "analysis",
        "intermediate_results": {
            **state["intermediate_results"],
            "analysis": analysis_results.content
        }
    }

def reporting_agent_node(state: MultiAgentState):
    # Reporting agent implementation
    final_report = reporting_agent.invoke(state["messages"])
    return {
        "messages": state["messages"] + [final_report],
        "current_agent": "reporting",
        "task_status": "completed"
    }

# Build the workflow graph
workflow = StateGraph(MultiAgentState)
workflow.add_node("research", research_agent_node)
workflow.add_node("analysis", analysis_agent_node)
workflow.add_node("reporting", reporting_agent_node)

# Define workflow transitions
workflow.add_edge("research", "analysis")
workflow.add_edge("analysis", "reporting")
workflow.add_edge("reporting", END)

# Set entry point
workflow.set_entry_point("research")

# Compile with persistence
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

# Configure LLM settings
import autogen
# Configure LLM settings
config_list = [{
    "model": "gpt-4o",
    "api_key": os.environ["OPENAI_API_KEY"]
}]

llm_config = {
    "timeout": 600,
    "cache_seed": 42,
    "config_list": config_list,
    "temperature": 0.3,
}

# Create specialized agents
project_manager = autogen.AssistantAgent(
    name="ProjectManager",
    system_message="""You are a project manager responsible for coordinating 
    software development tasks. Break down complex requirements into manageable 
    tasks and delegate to appropriate team members.""",
    llm_config=llm_config,
)

developer = autogen.AssistantAgent(
    name="Developer", 
    system_message="""You are a senior software developer. Implement features 
    based on requirements and write clean, efficient code.""",
    llm_config=llm_config,
)

quality_assurance = autogen.AssistantAgent(
    name="QualityAssurance",
    system_message="""You are a QA engineer responsible for testing and 
    validation. Review code for bugs, edge cases, and adherence to requirements.""",
    llm_config=llm_config,
)

user_proxy = autogen.UserProxyAgent(
    name="UserProxy",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={
        "work_dir": "development",
        "use_docker": True,
    },
    llm_config=llm_config,
)

# Create group chat for collaboration
groupchat = autogen.GroupChat(
    agents=[project_manager, developer, quality_assurance, user_proxy],
    messages=[],
    max_round=20,
    speaker_selection_method="round_robin"
)

manager = autogen.GroupChatManager(
    groupchat=groupchat,
    llm_config=llm_config
)

# Execute collaborative development task
user_proxy.initiate_chat(
    manager,
    message="""Develop a REST API for user authentication with the following requirements:
    - JWT token-based authentication
    - User registration and login endpoints
    - Password hashing and validation
    - Proper error handling and status codes"""
)

# Import CrewAI and tools
from crewai import Agent, Task, Crew
from crewai_tools import WebSearchTool, FileWriterTool

# Create specialized agents
market_researcher = Agent(
    role='Market Research Analyst',
    goal='Conduct comprehensive market research and competitive analysis',
    backstory="""You are an expert market research analyst with 10 years of 
    experience in technology markets. You excel at identifying market trends, 
    competitor strategies, and growth opportunities.""",
    tools=[WebSearchTool()],
    verbose=True
)

content_strategist = Agent(
    role='Content Strategy Specialist', 
    goal='Develop compelling content strategies based on market research',
    backstory="""You are a senior content strategist who translates market 
    insights into actionable content plans. You understand audience psychology 
    and engagement optimization.""",
    tools=[FileWriterTool()],
    verbose=True
)

social_media_manager = Agent(
    role='Social Media Manager',
    goal='Create engaging social media campaigns aligned with content strategy',
    backstory="""You are a creative social media expert who knows how to 
    create viral content and build community engagement across platforms.""",
    tools=[FileWriterTool()],
    verbose=True
)

# Define collaborative tasks
market_research_task = Task(
    description="""Conduct comprehensive market research for AI-powered 
    productivity tools. Analyze competitor strategies, target audience preferences, 
    and emerging trends in the productivity software market.""",
    expected_output="""A detailed market research report including:
    - Competitive landscape analysis
    - Target audience personas
    - Market size and growth projections
    - Key trends and opportunities""",
    agent=market_researcher
)

content_strategy_task = Task(
    description="""Based on the market research, develop a comprehensive 
    content strategy that addresses our target audience's needs and 
    differentiates us from competitors.""",
    expected_output="""A content strategy document including:
    - Content pillars and themes
    - Content calendar framework
    - Distribution channel strategy
    - Success metrics and KPIs""",
    agent=content_strategist,
    dependencies=[market_research_task]
)

social_campaign_task = Task(
    description="""Create a social media campaign that implements the 
    content strategy across multiple platforms.""",
    expected_output="""A social media campaign plan including:
    - Platform-specific content recommendations
    - Posting schedule and frequency
    - Engagement tactics and community building strategies
    - Performance tracking mechanisms""",
    agent=social_media_manager,
    dependencies=[content_strategy_task]
)

# Create and execute the crew
marketing_crew = Crew(
    agents=[market_researcher, content_strategist, social_media_manager],
    tasks=[market_research_task, content_strategy_task, social_campaign_task],
    verbose=2,
    process='sequential'
)

result = marketing_crew.kickoff()

# Initialize Semantic Kernel
from semantic_kernel import Kernel
from semantic_kernel.agents import Agent, AgentGroupChat
from semantic_kernel.agents.orchestration import SequentialOrchestration

# Initialize Semantic Kernel
kernel = Kernel()

# Create specialized agents
data_analyst = Agent(
    name="DataAnalyst",
    description="Analyzes datasets and identifies patterns and insights",
    instructions="""You are a data analyst specialized in statistical analysis 
    and pattern recognition. Process datasets and provide meaningful insights.""",
    kernel=kernel
)

report_writer = Agent(
    name="ReportWriter", 
    description="Creates comprehensive reports from analysis results",
    instructions="""You are a technical writer who creates clear, comprehensive 
    reports from data analysis results. Focus on actionable insights.""",
    kernel=kernel
)

quality_reviewer = Agent(
    name="QualityReviewer",
    description="Reviews and validates analysis and reports for accuracy",
    instructions="""You are a quality assurance specialist who reviews 
    analytical work for accuracy, completeness, and clarity.""",
    kernel=kernel
)

# Configure sequential orchestration
orchestration = SequentialOrchestration([
    data_analyst,
    report_writer, 
    quality_reviewer
])

# Execute coordinated workflow
async def execute_analysis_pipeline(dataset_path):
    initial_message = f"Please analyze the dataset at {dataset_path} and provide insights."
    
    result = await orchestration.invoke_async(initial_message)
    return result

# Python asyncio multi-agent framework
import asyncio
import json
from typing import Dict, List, Any
from dataclasses import dataclass
from enum import Enum

class MessageType(Enum):
    TASK_REQUEST = "task_request"
    TASK_RESPONSE = "task_response"
    CAPABILITY_INQUIRY = "capability_inquiry"
    CAPABILITY_RESPONSE = "capability_response"
    COLLABORATION_REQUEST = "collaboration_request"

@dataclass
class AgentMessage:
    sender_id: str
    receiver_id: str
    message_type: MessageType
    content: Dict[str, Any]
    timestamp: float
    correlation_id: str = None

class BaseAgent:
    def __init__(self, agent_id: str, capabilities: List[str]):
        self.agent_id = agent_id
        self.capabilities = capabilities
        self.message_queue = asyncio.Queue()
        self.active_tasks = {}
        self.peer_agents = {}
        
    async def send_message(self, target_agent, message: AgentMessage):
        """Send a message to another agent"""
        message.sender_id = self.agent_id
        message.receiver_id = target_agent.agent_id
        await target_agent.receive_message(message)
    
    async def receive_message(self, message: AgentMessage):
        """Receive and queue a message for processing"""
        await self.message_queue.put(message)
    
    async def process_messages(self):
        """Main message processing loop"""
        while True:
            try:
                message = await asyncio.wait_for(
                    self.message_queue.get(), timeout=1.0
                )
                await self.handle_message(message)
            except asyncio.TimeoutError:
                continue
    
    async def handle_message(self, message: AgentMessage):
        """Handle different types of incoming messages"""
        if message.message_type == MessageType.CAPABILITY_INQUIRY:
            await self.handle_capability_inquiry(message)
        elif message.message_type == MessageType.TASK_REQUEST:
            await self.handle_task_request(message)
        elif message.message_type == MessageType.COLLABORATION_REQUEST:
            await self.handle_collaboration_request(message)
            async def handle_capability_inquiry(self, message: AgentMessage):
    """Respond to capability inquiry from another agent"""
    response = AgentMessage(
        sender_id=self.agent_id,
        receiver_id=message.sender_id,
        message_type=MessageType.CAPABILITY_RESPONSE,
        content={
            "capabilities": self.capabilities,
            "current_load": len(self.active_tasks)
        },
        timestamp=asyncio.get_event_loop().time(),
        correlation_id=message.correlation_id
    )
    
    # Send response back to inquiring agent
    sender_agent = self.peer_agents.get(message.sender_id)
    if sender_agent:
        await self.send_message(sender_agent, response)

async def handle_task_request(self, message: AgentMessage):
    """Process incoming task requests"""
    task_id = message.content.get("task_id")
    task_description = message.content.get("description")
    
    # Check if we can handle this task
    if self.can_handle_task(task_description):
        # Accept and process the task
        result = await self.execute_task(task_description)
        
        response = AgentMessage(
            sender_id=self.agent_id,
            receiver_id=message.sender_id,
            message_type=MessageType.TASK_RESPONSE,
            content={
                "task_id": task_id,
                "status": "completed",
                "result": result
            },
            timestamp=asyncio.get_event_loop().time(),
            correlation_id=message.correlation_id
        )
    else:
        # Decline the task
        response = AgentMessage(
            sender_id=self.agent_id,
            receiver_id=message.sender_id,
            message_type=MessageType.TASK_RESPONSE,
            content={
                "task_id": task_id,
                "status": "declined",
                "reason": "Task outside capabilities"
            },
            timestamp=asyncio.get_event_loop().time(),
            correlation_id=message.correlation_id
        )
    
    sender_agent = self.peer_agents.get(message.sender_id)
    if sender_agent:
        await self.send_message(sender_agent, response)

def can_handle_task(self, task_description: str) -> bool:
    """Determine if agent can handle a given task"""
    # Simple capability matching - in practice this would be more sophisticated
    task_keywords = task_description.lower().split()
    return any(capability.lower() in task_keywords for capability in self.capabilities)

async def execute_task(self, task_description: str) -> Dict[str, Any]:
    """Execute a task - to be overridden by specific agent implementations"""
    # Simulate task execution
    await asyncio.sleep(0.5)  # Simulate processing time
    return {"status": "completed", "output": f"Processed: {task_description}"}

def register_peer(self, agent):
    """Register another agent as a peer for communication"""
    self.peer_agents[agent.agent_id] = agent
        

class DataAnalysisAgent(BaseAgent):
    def __init__(self):
        super().__init__("data_analyst", ["data_analysis", "statistics", "visualization"])
        self.analysis_tools = ["pandas", "numpy", "matplotlib", "seaborn"]
    
    async def execute_task(self, task_description: str) -> Dict[str, Any]:
        """Execute data analysis tasks"""
        await asyncio.sleep(1.0)  # Simulate analysis time
        
        # Simulate different types of analysis based on task description
        if "trend" in task_description.lower():
            return {
                "analysis_type": "trend_analysis",
                "trends_identified": ["upward trend in Q3", "seasonal patterns detected"],
                "statistical_significance": 0.95,
                "visualization_created": True
            }
        elif "correlation" in task_description.lower():
            return {
                "analysis_type": "correlation_analysis", 
                "correlations_found": [{"var1": "feature_a", "var2": "feature_b", "correlation": 0.73}],
                "p_value": 0.02,
                "visualization_created": True
            }
        else:
            return {
                "analysis_type": "general_analysis",
                "summary_statistics": {"mean": 42.5, "std": 12.3, "count": 1000},
                "outliers_detected": 15,
                "data_quality_score": 0.87
            }

class ContentGenerationAgent(BaseAgent):
    def __init__(self):
        super().__init__("content_generator", ["writing", "content_creation", "editing"])
        self.writing_styles = ["technical", "conversational", "formal", "creative"]
    
    async def execute_task(self, task_description: str) -> Dict[str, Any]:
        """Execute content generation tasks"""
        await asyncio.sleep(0.8)  # Simulate writing time
        
        if "report" in task_description.lower():
            return {
                "content_type": "analytical_report",
                "word_count": 1500,
                "sections": ["Executive Summary", "Analysis", "Conclusions", "Recommendations"],
                "readability_score": 8.2,
                "content": "# Analytical Report\n\n## Executive Summary\nThis report analyzes..."
            }
        elif "blog" in task_description.lower():
            return {
                "content_type": "blog_post",
                "word_count": 800,
                "tone": "conversational",
                "seo_optimized": True,
                "content": "# Blog Post Title\n\nEngaging introduction that hooks the reader..."
            }
        else:
            return {
                "content_type": "general_content",
                "word_count": 500,
                "style": "professional",
                "content": "Professional content tailored to requirements..."
            }

class QualityAssuranceAgent(BaseAgent):
    def __init__(self):
        super().__init__("quality_assurance", ["validation", "testing", "review"])
        self.quality_criteria = ["accuracy", "completeness", "clarity", "consistency"]
    
    async def execute_task(self, task_description: str) -> Dict[str, Any]:
        """Execute quality assurance tasks"""
        await asyncio.sleep(0.6)  # Simulate review time
        
        return {
            "review_type": "comprehensive_review",
            "quality_score": 8.5,
            "issues_found": [
                {"type": "minor", "description": "Inconsistent formatting in section 3"},
                {"type": "suggestion", "description": "Consider adding more examples"}
            ],
            "approval_status": "approved_with_minor_changes",
            "reviewer_confidence": 0.92
        }

from abc import ABC, abstractmethod

class Tool(ABC):
    @abstractmethod
    async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]):
        pass

class WebSearchTool(Tool):
    async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]):
        query = parameters.get("query")
        max_results = parameters.get("max_results", 5)
        
        # Simulate web search
        await asyncio.sleep(0.3)
        
        return {
            "tool": "web_search",
            "query": query,
            "results": [
                {"title": f"Result {i}", "url": f"https://example.com/result{i}", 
                 "snippet": f"Relevant information about {query}"}
                for i in range(max_results)
            ],
            "total_results": max_results * 10
        }

class DataVisualizationTool(Tool):
    async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]):
        chart_type = parameters.get("chart_type", "bar")
        data = parameters.get("data", [])

        # Simulate chart generation
        await asyncio.sleep(0.5)

        return {
            "tool": "data_visualization",
            "chart_type": chart_type,
            "chart_generated": True,
            "chart_path": f"/tmp/chart_{chart_type}.png",
            "data_points": len(data),
            "visualization_quality": "high"
        }
        class ToolCapableAgent(BaseAgent):
    def __init__(self, agent_id: str, capabilities: List[str]):
        super().__init__(agent_id, capabilities)
        self.available_tools = {}
    
    def register_tool(self, tool_name: str, tool: Tool):
        """Register a tool for use by this agent"""
        self.available_tools[tool_name] = tool
    
    async def use_tool(self, tool_name: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """Use a registered tool"""
        if tool_name not in self.available_tools:
            return {"error": f"Tool {tool_name} not available"}
        
        tool = self.available_tools[tool_name]
        return await tool.execute(parameters)

class ResearchAgent(ToolCapableAgent):
    def __init__(self):
        super().__init__("research_agent", ["research", "fact_checking", "information_gathering"])
        
        # Register available tools
        self.register_tool("web_search", WebSearchTool())
        self.register_tool("data_viz", DataVisualizationTool())
    
    async def execute_task(self, task_description: str) -> Dict[str, Any]:
        """Execute research tasks using available tools"""
        research_results = {}
        
        # Extract search queries from task description
        if "search" in task_description.lower():
            search_query = self.extract_search_query(task_description)
            search_results = await self.use_tool("web_search", {
                "query": search_query,
                "max_results": 10
            })
            research_results["web_search"] = search_results
        
        # Generate visualizations if needed
        if "chart" in task_description.lower() or "graph" in task_description.lower():
            viz_results = await self.use_tool("data_viz", {
                "chart_type": "bar",
                "data": [1, 2, 3, 4, 5] # Sample data
            })
            research_results["visualization"] = viz_results
        
        return {
            "research_completed": True,
            "tools_used": list(research_results.keys()),
            "results": research_results,
            "confidence_score": 0.85
        }
    
    def extract_search_query(self, task_description: str) -> str:
        """Extract search query from task description - simplified implementation"""
        # In practice, this would use more sophisticated NLP
        words = task_description.split()
        return " ".join([word for word in words if word.lower() not in ["search", "for", "about", "find"]])

import uuid
from typing import Optional
from enum import Enum
from dataclasses import dataclass
from typing import List, Dict, Any

class TaskStatus(Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress" 
    COMPLETED = "completed"
    FAILED = "failed"

@dataclass
class WorkflowTask:
    task_id: str
    description: str
    required_capabilities: List[str]
    assigned_agent: Optional[str] = None
    status: TaskStatus = TaskStatus.PENDING
    result: Optional[Dict[str, Any]] = None
    dependencies: List[str] = None
    
class CoordinatorAgent(BaseAgent):
    def __init__(self):
        super().__init__("coordinator", ["orchestration", "task_management", "workflow_coordination"])
        self.active_workflows = {}
        self.agent_capabilities = {}
        
    async def execute_workflow(self, workflow_description: str, participating_agents: List[BaseAgent]):
        """Execute a complex multi-agent workflow"""
        workflow_id = str(uuid.uuid4())
        
        # Register participating agents and their capabilities
        for agent in participating_agents:
            self.agent_capabilities[agent.agent_id] = agent.capabilities
            agent.register_peer(self)
            self.register_peer(agent)
        
        # Decompose workflow into tasks
        tasks = self.decompose_workflow(workflow_description)
        
        # Create workflow tracking
        self.active_workflows[workflow_id] = {
            "description": workflow_description,
            "tasks": {task.task_id: task for task in tasks},
            "status": "in_progress",
            "start_time": asyncio.get_event_loop().time()
        }
        
        # Execute tasks with dependency management
        return await self.execute_tasks_with_dependencies(workflow_id, tasks, participating_agents)
    
    def decompose_workflow(self, workflow_description: str) -> List[WorkflowTask]:
        """Decompose complex workflow into manageable tasks"""
        tasks = []
        
        # Simplified task decomposition - in practice, this would be more sophisticated
        if "data analysis report" in workflow_description.lower():
            tasks = [
                WorkflowTask(
                    task_id="task_1",
                    description="Collect and analyze relevant data",
                    required_capabilities=["data_analysis", "statistics"]
                ),
                WorkflowTask(
                    task_id="task_2", 
                    description="Generate comprehensive report from analysis",
                    required_capabilities=["content_creation", "writing"],
                    dependencies=["task_1"]
                ),
                WorkflowTask(
                    task_id="task_3",
                    description="Review report for quality and accuracy", 
                    required_capabilities=["validation", "review"],
                    dependencies=["task_2"]
                )
            ]
        async def execute_tasks_with_dependencies(self, workflow_id: str, tasks: List[WorkflowTask], agents: List[BaseAgent]):
    """Execute tasks while respecting dependencies"""
    completed_tasks = set()
    task_results = {}

    # Loop until all tasks are completed
    while len(completed_tasks) < len(tasks):
        # Find tasks ready for execution (dependencies satisfied)
        ready_tasks = [
            task for task in tasks 
            if task.task_id not in completed_tasks and 
            self.dependencies_satisfied(task, completed_tasks)
        ]

        # Execute ready tasks in parallel
        if ready_tasks:
            execution_tasks = [
                self.assign_and_execute_task(task, agents)
                for task in ready_tasks
            ]

            results = await asyncio.gather(*execution_tasks)

            # Update task status and results
            for task, result in zip(ready_tasks, results):
                task.result = result
                task.status = TaskStatus.COMPLETED if result.get("status") == "completed" else TaskStatus.FAILED
                completed_tasks.add(task.task_id)
                task_results[task.task_id] = result

        await asyncio.sleep(0.1)  # Brief pause before checking again

    # Workflow completion
    self.active_workflows[workflow_id]["status"] = "completed"
    self.active_workflows[workflow_id]["end_time"] = asyncio.get_event_loop().time()

    return {
        "workflow_id": workflow_id,
        "status": "completed", 
        "task_results": task_results,
        "execution_time": self.active_workflows[workflow_id]["end_time"] - self.active_workflows[workflow_id]["start_time"]
    }

def dependencies_satisfied(self, task: WorkflowTask, completed_tasks: set) -> bool:
    """Check if task dependencies are satisfied"""
    if not task.dependencies:
        return True
    return all(dep_id in completed_tasks for dep_id in task.dependencies)

async def assign_and_execute_task(self, task: WorkflowTask, agents: List[BaseAgent]) -> Dict[str, Any]:
    """Assign task to most suitable agent and execute"""
    # Find best agent for this task
    best_agent = self.find_best_agent(task, agents)

    if not best_agent:
        return {"status": "failed", "reason": "No suitable agent found"}

    # Send task request to selected agent
    task_message = AgentMessage(
        sender_id=self.agent_id,
        receiver_id=best_agent.agent_id,
        message_type=MessageType.TASK_REQUEST,
        content={
            "task_id": task.task_id,
            "description": task.description,
            "required_capabilities": task.required_capabilities
        },
        timestamp=asyncio.get_event_loop().time(),
        correlation_id=str(uuid.uuid4())
    )

    await self.send_message(best_agent, task_message)

    # Wait for response (simplified - in practice would have timeout handling)
    response = await self.wait_for_task_response(task_message.correlation_id)
    return response
        return tasks

        def find_best_agent(self, task: WorkflowTask, agents: List[BaseAgent]) -> Optional[BaseAgent]:
    """Find the best agent for a given task based on capabilities"""
    best_agent = None
    best_score = 0
    
    for agent in agents:
        if agent.agent_id == self.agent_id:  # Skip coordinator
            continue
            
        # Calculate capability match score
        matching_capabilities = set(agent.capabilities) & set(task.required_capabilities)
        score = len(matching_capabilities) / len(task.required_capabilities)
        
        if score > best_score:
            best_score = score
            best_agent = agent
    
    return best_agent if best_score > 0.5 else None

async def wait_for_task_response(self, correlation_id: str) -> Dict[str, Any]:
    """Wait for task response with correlation ID"""
    # Simplified implementation - in practice would have proper timeout and error handling
    timeout = 30  # seconds
    start_time = asyncio.get_event_loop().time()
    
    while asyncio.get_event_loop().time() - start_time < timeout:
        try:
            message = await asyncio.wait_for(self.message_queue.get(), timeout=1.0)
            if (message.message_type == MessageType.TASK_RESPONSE and 
                message.correlation_id == correlation_id):
                return message.content
            else:
                # Put message back if it's not what we're waiting for
                await self.message_queue.put(message)
        except asyncio.TimeoutError:
            continue
    
    return {'status': 'timeout', 'reason': 'Agent response timeout'}

async def main():
    """Main example demonstrating A2A multi-agent workflow"""
    
    # Create specialized agents
    data_agent = DataAnalysisAgent()
    content_agent = ContentGenerationAgent()
    qa_agent = QualityAssuranceAgent()
    research_agent = ResearchAgent()
    coordinator = CoordinatorAgent()
    
    # Start agent message processing loops
    agents = [data_agent, content_agent, qa_agent, research_agent, coordinator]
    processing_tasks = [asyncio.create_task(agent.process_messages()) for agent in agents]
    
    try:
        # Execute a complex workflow
        workflow_description = "Create a comprehensive data analysis report on market trends"
        
        result = await coordinator.execute_workflow(
            workflow_description,
            [data_agent, content_agent, qa_agent, research_agent]
        )
        
        print("Workflow Execution Results:")
        print(f"Workflow ID: {result['workflow_id']}")
        print(f"Status: {result['status']}")
        print(f"Execution Time: {result['execution_time']:.2f} seconds")
        
        print("\nTask Results:")
        for task_id, task_result in result['task_results'].items():
            print(f"  {task_id}: {task_result.get('status', 'unknown')}")
            
    finally:
        # Clean up processing tasks
        for task in processing_tasks:
            task.cancel()
        
        await asyncio.gather(*processing_tasks, return_exceptions=True)

# Run the example
if __name__ == "__main__":
    asyncio.run(main())