Implementation Patterns¶

Common Architectural Patterns¶

Pattern 1: Hierarchical Agent Network¶

         ┌────────────┐
         │ Supervisor │
         │   Agent    │
         └─────┬──────┘
               │ A2A
     ┌─────────┼─────────┐
     ↓         ↓         ↓
┌─────────┐ ┌─────────┐ ┌─────────┐
│Research │ │Analysis │ │ Report  │
│  Agent  │ │  Agent  │ │  Agent  │
└────┬────┘ └────┬────┘ └────┬────┘
     │ MCP       │ MCP       │ MCP
     ↓           ↓           ↓
  [Search]   [Compute]   [Document]
  [Tools]     [Tools]     [Tools]

When to Use¶

• Clear task decomposition possible
• Need for specialized sub-agents
• Centralized coordination required

Example Implementation¶

# Supervisor agent coordinates via A2A
async def handle_complex_request(request):
    # Discover available agents
    agents = await a2a_client.discover_agents(
        capabilities=["research", "analysis", "reporting"]
    )

    # Delegate subtasks via A2A
    research_result = await a2a_client.delegate(
        agent_id=agents["research"],
        task="gather market data"
    )

    analysis_result = await a2a_client.delegate(
        agent_id=agents["analysis"],
        task="analyze trends",
        context=research_result
    )

    # Each agent uses MCP for tool access
    # (handled internally by each agent)

Pattern 2: Peer-to-Peer Collaboration¶

┌─────────┐ A2A  ┌─────────┐ A2A  ┌─────────┐
│ Agent A │ ←──→ │ Agent B │ ←──→ │ Agent C │
└────┬────┘      └────┬────┘      └────┬────┘
     │ MCP            │ MCP            │ MCP
     ↓                ↓                ↓
  [Tools A]       [Tools B]       [Tools C]

When to Use¶

• Agents need to negotiate and collaborate
• No natural hierarchy exists
• Dynamic team formation required

Example Implementation¶

# Agent negotiates with peers
async def collaborative_solve(problem):
    # Broadcast capability request via A2A
    responses = await a2a_client.broadcast({
        "type": "capability_query",
        "required": problem.requirements
    })

    # Form collaborative team
    team = await a2a_client.form_team(
        agents=responses.capable_agents,
        consensus_model="majority"
    )

    # Collaborate on solution
    solution = await team.collaborate(problem)
    return solution

Pattern 3: Service Mesh Architecture¶

┌───────────────────────────────┐
│     A2A Service Registry      │
├───────────────────────────────┤
│  - Agent Discovery            │
│  - Load Balancing             │
│  - Health Monitoring          │
└──────────┬────────────────────┘
           │
    ┌──────┼──────┐
    ↓      ↓      ↓
[Agent] [Agent] [Agent]
    │      │      │
   MCP    MCP    MCP
    │      │      │
[Shared Tool Infrastructure]

When to Use¶

• Large-scale deployments
• Need for resilience and redundancy
• Shared resource management

Example Implementation¶

class ServiceMeshAgent:
    def __init__(self):
        # Register with A2A mesh
        self.a2a_mesh = A2AMesh.register(
            agent_id=self.id,
            capabilities=self.capabilities
        )

        # Setup MCP connections
        self.mcp_client = MCP.connect(
            tools=self.required_tools
        )

    async def handle_request(self, request):
        # Check if can handle locally
        if self.can_handle(request):
            # Use MCP tools directly
            return await self.mcp_client.execute(request)

        # Otherwise delegate via A2A mesh
        return await self.a2a_mesh.route(request)

Pattern 4: Gateway Pattern¶

┌─────────────────┐
│   API Gateway   │
│  (A2A Router)   │
└────────┬────────┘
         │ A2A
    ┌────┼────┐
    ↓    ↓    ↓
[Agents with MCP tools]

When to Use¶

• External API exposure needed
• Centralized authentication/authorization
• Rate limiting and monitoring

Best Practices¶

For A2A Implementation¶

1. Agent Discovery
2. Implement capability-based discovery
3. Cache agent registry for performance

4. Handle agent unavailability gracefully

5. Message Design

6. Keep messages lightweight
7. Include correlation IDs for tracking

8. Version your message schemas

9. Error Handling

10. Implement retry mechanisms
11. Provide fallback agents
12. Log all inter-agent communications

For MCP Integration¶

1. Tool Management
2. Lazy-load MCP connections
3. Pool and reuse connections

4. Monitor tool availability

5. Resource Access

6. Cache frequently accessed resources
7. Implement access control at MCP level

8. Batch operations when possible

9. Performance

10. Minimize MCP round-trips
11. Use streaming for large datasets
12. Implement timeouts appropriately

For Combined Usage¶

1. Clear Boundaries
2. A2A for orchestration decisions
3. MCP for tool execution

4. Never bypass protocols

5. State Management

6. A2A maintains conversation state
7. MCP maintains tool session state

8. Synchronize when necessary

9. Security Layers

10. Authenticate at A2A level (agent identity)
11. Authorize at MCP level (tool permissions)
12. Audit at both levels

Next: References & Resources →¶