Agent-to-Agent Protocol

Overview

A2A (Agent-to-Agent) Protocol is a framework-agnostic standard for coordinating multiple AI agents. It allows agents built with different frameworks (LangGraph, CrewAI, Semantic Kernel, custom systems) to work together seamlessly.

The protocol focuses on task coordination, state sharing, and orchestration of complex multi-agent workflows.

Key Characteristics

Framework Independence

• Works with LangGraph (LangChain’s agentic framework)
• Integrates CrewAI multi-agent orchestration
• Compatible with Semantic Kernel
• Supports custom agent systems
• No vendor lock-in

Task Orchestration

• Express task dependencies clearly
• Automatic parallelization when dependencies allow
• Error handling and retry logic
• Monitoring and observability

State Management

• Share state between agents safely
• Version control for agent outputs
• Rollback capabilities for failures
• Audit trail of all state changes

Workflow Structure

A2A workflows define agents, tasks, and dependencies:

{
  agents: [
    { id: "planner", role: "planning", framework: "langgraph" },
    { id: "researcher", role: "research", framework: "crewai" },
    { id: "executor", role: "execution", framework: "langgraph" }
  ],
  tasks: [
    { id: "plan", agent: "planner" },
    { id: "research", agent: "researcher", depends: ["plan"] },
    { id: "execute", agent: "executor", depends: ["research", "plan"] }
  ]
}

Execution graph:

┌─────────┐
│ Plan    │
└────┬────┘
     │
┌────▼─────────┐
│ Research     │
└────┬─────────┘
     │
┌────▼─────────┐
│ Execute      │
└──────────────┘

Ontology Integration

A2A integrates with the 6-dimension ontology for complete tracking:

Things

• workflow: Complete workflow definition
• intelligence_agent: Each participating agent
• task: Individual task in workflow
• All scoped to groupId

Connections

• delegated_to: Agent delegates work to another agent
• depends_on: Task dependency relationships
• coordinates_with: Agent-to-agent coordination
• Rich metadata about task status

Events

• workflow_started: Workflow execution began
• task_started: Individual task started
• task_completed: Task finished with output
• task_failed: Task failed with error
• agent_fallback: Fallback agent was used

Knowledge

• Workflow patterns: Store successful workflow configurations
• Performance metrics: Track execution time, success rate
• Error patterns: Learn from failures
• Agent capabilities: Index agent specializations

Multi-Agent Patterns

Sequential Pipeline

Tasks execute one after another:

Task1 → Task2 → Task3 → Task4

Parallel Execution

Independent tasks run simultaneously:

Task1 ─┐
Task2 ─┤→ Task4
Task3 ─┘

Fan-Out/Fan-In

One task spawns multiple parallel subtasks:

       ┌─→ SubTask1 ─┐
Task1 ─┤─→ SubTask2 ─┤→ Task2
       └─→ SubTask3 ─┘

Conditional Branching

Task execution depends on previous results:

       ┌─→ Task2 (if error)
Task1 ─┤
       └─→ Task3 (if success)

Communication Between Agents

A2A enables agents to share information:

1. Direct State Passing: Output of one agent becomes input to next
2. Shared Context: Agents access shared knowledge base
3. Message Exchange: Agents communicate via message queue
4. Event Streaming: Agents subscribe to events

Error Handling Strategy

A2A includes robust error handling:

• Timeouts: Set time limits for tasks
• Retries: Automatic retry with backoff
• Fallbacks: Use alternative agents
• Circuit Breaker: Stop cascading failures
• Rollback: Revert to previous state if needed

Performance & Scaling

A2A designed for large-scale workflows:

• Distributed Execution: Run agents across multiple machines
• Load Balancing: Distribute tasks fairly
• Result Caching: Cache outputs to reduce redundant work
• Monitoring: Track performance metrics
• Scaling: Automatically scale up for large workflows

Comparison with Other Protocols

Aspect	A2A	ACP	MCP
Primary Use	Task coordination	Agent messaging	Resource access
Focus	Workflow orchestration	Inter-agent chat	AI data access
State Management	Rich, versioned	Minimal	None
Task Tracking	Built-in	Via connections	Via tools

Getting Started

1. Identify your agent frameworks and systems
2. Design your workflow with tasks and dependencies
3. Define agents and their capabilities
4. Implement A2A adapters for each framework
5. Deploy workflow coordinator
6. Monitor execution and optimize

Best Practices

✅ Keep tasks focused: Each task should have one clear purpose ✅ Use meaningful names: Make workflow easy to understand ✅ Set timeouts: Prevent infinite waits ✅ Monitor metrics: Track performance and success rates ✅ Version workflows: Track changes over time ✅ Document dependencies: Make relationships explicit

Related Resources

• A2A Specification: Google A2A Spec
• Framework Adapters: LangGraph | CrewAI
• Example Workflows: Reference Implementations
• Community: A2A Discussions

                      // Define workflow using A2A protocol
const workflow = {
  id: "workflow_research_report",
  name: "Research and Write Report",
  agents: [
    {
      id: "agent_researcher",
      role: "researcher",
      task: "research_topic",
      inputs: { topic: "${topic}" }
    },
    {
      id: "agent_writer",
      role: "writer",
      task: "write_report",
      inputs: { research: "${agent_researcher.output}" },
      dependencies: ["agent_researcher"]
    },
    {
      id: "agent_editor",
      role: "editor",
      task: "review_report",
      inputs: { report: "${agent_writer.output}" },
      dependencies: ["agent_writer"]
    }
  ],
  outputs: {
    finalReport: "${agent_editor.output}"
  }
};

// Create workflow as thing
const workflowId = await ctx.db.insert("things", {
  type: "workflow",
  name: workflow.name,
  groupId: ctx.auth?.getOrganizationId?.(),
  properties: {
    workflow: workflow,
    status: "draft"
  },
  status: "active",
  createdAt: Date.now(),
  updatedAt: Date.now()
});

                    

                      // Execute multi-agent workflow
import { A2AClient } from "@a2a/sdk";

const client = new A2AClient({ apiKey: process.env.A2A_API_KEY });

// Start workflow
const execution = await client.workflow.execute({
  workflowId: "workflow_research_report",
  inputs: {
    topic: "Quantum Computing Applications"
  },
  agents: [
    { id: "researcher", framework: "crewai" },
    { id: "writer", framework: "langgraph" },
    { id: "editor", framework: "custom" }
  ]
});

// Monitor execution
execution.on("agent_completed", (event) => {
  console.log(`${event.agentId} completed: ${event.output}`);

  // Log to ontology
  await ctx.db.insert("events", {
    type: "task_completed",
    groupId: ctx.auth?.getOrganizationId?.(),
    actorId: event.agentId,
    targetId: workflowId,
    metadata: {
      protocol: "a2a",
      taskId: event.taskId,
      result: event.output
    },
    timestamp: Date.now()
  });
});

const result = await execution.wait();
return result.outputs.finalReport;

                    

                      // A2A with error handling and fallback agents
const workflow = {
  tasks: [
    {
      id: "primary_search",
      agent: "web_search_agent",
      fallback: {
        agent: "cache_search_agent",
        condition: "timeout || rate_limit"
      }
    }
  ]
};

// Implement fallback handling
try {
  result = await executeWithA2A(workflow.tasks[0]);
} catch (error) {
  if (shouldUseFallback(error, workflow.tasks[0].fallback)) {
    result = await executeWithA2A(workflow.tasks[0].fallback);

    // Log fallback event
    await ctx.db.insert("events", {
      type: "agent_fallback",
      metadata: {
        protocol: "a2a",
        primary: workflow.tasks[0].agent,
        fallback: workflow.tasks[0].fallback.agent,
        reason: error.message
      },
      timestamp: Date.now()
    });
  }
}