A2A Protocol Analysis & Implementation Recommendations

# New: A2AAgentCard integration
from a2a_sdk import AgentCard, AgentSkill, AgentCapabilities
 
class MTDAgentCardService:
    def generate_agent_card(self, agent: AgentModel) -> AgentCard:
        """Convert MTD AgentModel to A2A-compatible AgentCard"""
        skills = []
        
        # Convert existing tools to AgentSkills
        for tool_name in agent.tools:
            skill = AgentSkill(
                id=tool_name,
                name=self._get_tool_display_name(tool_name),
                description=self._get_tool_description(tool_name),
                tags=self._get_tool_tags(tool_name),
                examples=self._get_tool_examples(tool_name)
            )
            skills.append(skill)
        
        return AgentCard(
            name=agent.name,
            description=agent.description,
            url=f"{self.base_url}/agents/{agent.gid}",
            version="1.0.0",
            defaultInputModes=['text'],
            defaultOutputModes=['text'],
            capabilities=AgentCapabilities(
                streaming=True,
                multiTurn=True,
                authentication=True
            ),
            skills=skills
        )

# New: A2A protocol endpoints
@router.get("/agents/{agent_id}/.well-known/agent.json")
async def get_agent_card(agent_id: str, user: LoggedInUser = Depends(get_current_user)):
    """Expose A2A-compatible agent card"""
    agent = await agent_service.get_agent(agent_id, user.uid)
    agent_card = a2a_card_service.generate_agent_card(agent)
    return agent_card.dict()
 
@router.post("/agents/{agent_id}/a2a/message")
async def a2a_message_handler(
    agent_id: str,
    request: A2AMessageRequest,
    user: LoggedInUser = Depends(get_current_user)
):
    """Handle A2A protocol messages"""
    # Convert A2A request to internal format
    internal_request = a2a_adapter.convert_to_internal(request)
    
    # Process using existing agent service
    response = await agent_service.process_conversation(
        agent_id, internal_request, user.uid
    )
    
    # Convert response to A2A format
    return a2a_adapter.convert_to_a2a(response)

class A2AProtocolAdapter:
    """Adapter to bridge A2A protocol with existing MTD implementation"""
    
    def convert_to_internal(self, a2a_request: A2AMessageRequest) -> ConversationRequest:
        """Convert A2A request to MTD internal format"""
        return ConversationRequest(
            query=self._extract_text_from_parts(a2a_request.message.parts),
            conversation_id=a2a_request.contextId,
            message_id=a2a_request.message.messageId
        )
    
    def convert_to_a2a(self, mtd_response: ConversationResponse) -> A2AMessageResponse:
        """Convert MTD response to A2A format"""
        return A2AMessageResponse(
            taskId=mtd_response.conversation_id,
            contextId=mtd_response.conversation_id,
            message={
                "role": "assistant",
                "parts": [{"kind": "text", "text": mtd_response.results}],
                "messageId": self._generate_message_id()
            }
        )

class DynamicToolRegistry:
    """A2A-inspired dynamic tool discovery system"""
    
    def __init__(self):
        self._tools = {}
        self._tool_metadata = {}
    
    def register_tool(self, tool_func: callable, metadata: ToolMetadata):
        """Register tool with rich metadata"""
        tool_name = tool_func.__name__
        self._tools[tool_name] = tool_func
        self._tool_metadata[tool_name] = metadata
    
    def discover_tools(self, agent_type: str = None, tags: List[str] = None) -> List[AgentSkill]:
        """Dynamically discover available tools as AgentSkills"""
        skills = []
        for tool_name, metadata in self._tool_metadata.items():
            if self._matches_criteria(metadata, agent_type, tags):
                skill = AgentSkill(
                    id=tool_name,
                    name=metadata.display_name,
                    description=metadata.description,
                    tags=metadata.tags,
                    examples=metadata.examples
                )
                skills.append(skill)
        return skills

class AgentDiscoveryService:
    """Service for discovering available agents and their capabilities"""
    
    async def discover_agents(
        self, 
        organization_id: str,
        capability_filter: str = None
    ) -> List[AgentCard]:
        """Discover available agents with A2A cards"""
        agents = await self.agent_service.get_agents_by_organization(organization_id)
        agent_cards = []
        
        for agent in agents:
            if agent.status == "published":
                card = self.a2a_card_service.generate_agent_card(agent)
                if self._matches_capability_filter(card, capability_filter):
                    agent_cards.append(card)
        
        return agent_cards

class InterAgentCommunicationService:
    """Enable direct agent-to-agent communication using A2A protocol"""
    
    async def send_agent_message(
        self,
        source_agent_id: str,
        target_agent_id: str,
        message: str,
        context_id: str = None
    ) -> A2AMessageResponse:
        """Send message from one agent to another"""
        
        # Get target agent card
        target_card = await self.discovery_service.get_agent_card(target_agent_id)
        
        # Create A2A message request
        request = A2AMessageRequest(
            message={
                "role": "assistant",  # Source agent acts as assistant to target
                "parts": [{"kind": "text", "text": message}],
                "messageId": self._generate_message_id()
            },
            contextId=context_id or self._generate_context_id()
        )
        
        # Send to target agent
        response = await self.a2a_client.send_message(target_card.url, request)
        return response

class A2AAgentOrchestrator:
    """Orchestrate complex multi-agent workflows using A2A protocol"""
    
    async def execute_multi_agent_workflow(
        self,
        workflow_definition: WorkflowDefinition,
        initial_input: str,
        user_id: str
    ) -> WorkflowResult:
        """Execute complex workflow across multiple agents"""
        
        context_id = self._generate_workflow_context_id()
        workflow_state = WorkflowState(
            context_id=context_id,
            current_step=0,
            accumulated_results=[]
        )
        
        for step in workflow_definition.steps:
            # Discover appropriate agent for this step
            suitable_agents = await self.discovery_service.discover_agents(
                organization_id=self._get_user_org(user_id),
                capability_filter=step.required_capability
            )
            
            if not suitable_agents:
                raise NoSuitableAgentError(f"No agent found for capability: {step.required_capability}")
            
            # Select best agent (could be ML-based selection)
            selected_agent = self._select_best_agent(suitable_agents, step)
            
            # Execute step
            step_input = self._prepare_step_input(workflow_state, step, initial_input)
            step_result = await self.inter_agent_service.send_agent_message(
                source_agent_id="orchestrator",
                target_agent_id=selected_agent.id,
                message=step_input,
                context_id=context_id
            )
            
            # Update workflow state
            workflow_state.accumulated_results.append(step_result)
            workflow_state.current_step += 1
        
        return WorkflowResult(
            context_id=context_id,
            final_result=self._combine_results(workflow_state.accumulated_results),
            execution_trace=workflow_state.execution_trace
        )