Agent-to-Agent Protocol (A2A)¶

Planned

Status: In Development

The Agent-to-Agent Protocol implementation is currently in the planning and design phase. This page outlines the planned features and roadmap.

Overview¶

The Agent-to-Agent Protocol (A2A), formerly known as the Agent Communication Protocol (ACP), is a standardized communication protocol that enables independent AI agents to interact, collaborate, and coordinate with each other.

A2A provides a foundation for building multi-agent systems where agents can:

Discover and connect with other agents
Exchange messages and data
Delegate tasks and coordinate workflows
Stream real-time updates
Maintain conversation context

Vision¶

As AI systems evolve, the need for multiple specialized agents to work together becomes critical. A2A aims to be the standard protocol for agent collaboration, enabling:

Horizontal Scaling: Multiple agents handling different aspects of complex tasks
Specialization: Each agent focused on specific domains or capabilities
Resilience: Fault-tolerant systems with agent redundancy
Interoperability: Agents from different vendors working together

Planned Features¶

Core Protocol¶

Message Passing: Standardized message formats for agent communication
Task Management: Task creation, delegation, and lifecycle tracking
Capability Discovery: Agents can query and advertise their capabilities
Context Management: Maintain conversation and task context across agents
Error Handling: Comprehensive error types and recovery mechanisms

Transport Bindings¶

Multiple protocol bindings for different use cases:

JSON-RPC 2.0: Lightweight RPC for simple scenarios
gRPC: High-performance binary protocol
REST: HTTP-based for web integrations
WebSocket: Real-time bidirectional communication

Authentication & Authorization¶

Agent Identity: Cryptographic agent identification
Trust Models: Configurable trust levels between agents
Permission System: Fine-grained authorization for operations
Audit Logging: Comprehensive tracking of agent interactions

Advanced Features¶

Streaming: Real-time data streaming between agents
Pub/Sub: Event-driven agent coordination
Routing: Intelligent message routing in agent networks
Load Balancing: Distribute work across agent pools

Architecture (Planned)¶

flowchart TD
    subgraph "Agent A"
        A_App["Application Logic"]
        A_A2A["A2A Client"]
    end

    subgraph "Agent B"
        B_App["Application Logic"]
        B_A2A["A2A Server"]
    end

    subgraph "Agent C"
        C_App["Application Logic"]
        C_A2A["A2A Client/Server"]
    end

    A_A2A <-->|Task Delegation| B_A2A
    B_A2A <-->|Collaboration| C_A2A
    C_A2A <-->|Data Request| A_A2A

Use Cases¶

1. Multi-Agent Task Decomposition¶

Break complex tasks into subtasks handled by specialized agents:

Research agent gathers information
Analysis agent processes data
Writing agent generates content
Review agent validates output

2. Agent Collaboration¶

Multiple agents working together on shared goals:

Code generation agent creates implementation
Testing agent validates functionality
Documentation agent writes guides
Deployment agent handles release

3. Agent Orchestration¶

Coordinator agent managing a team of worker agents:

Load balancing across agents
Task prioritization and scheduling
Resource allocation
Result aggregation

4. Agent Networks¶

Distributed networks of agents:

Service mesh of specialized agents
Dynamic agent discovery
Fault-tolerant communication
Scalable architecture

Roadmap¶

Phase 1: Specification & Design (Q1 2025)¶

Phase 2: Core Implementation (Q2 2025)¶

Phase 3: Advanced Features (Q3 2025)¶

Phase 4: Ecosystem (Q4 2025)¶

Technical Specifications (Draft)¶

Message Format¶

pub struct A2AMessage {
    pub id: MessageId,
    pub from: AgentId,
    pub to: AgentId,
    pub message_type: MessageType,
    pub payload: serde_json::Value,
    pub timestamp: DateTime<Utc>,
}

pub enum MessageType {
    TaskRequest,
    TaskResponse,
    TaskStatus,
    DataQuery,
    DataResponse,
    CapabilityQuery,
    CapabilityResponse,
}

Agent Interface¶

#[async_trait]
pub trait Agent: Send + Sync {
    async fn handle_task(&self, task: Task) -> Result<TaskResult, Error>;
    async fn query_data(&self, query: DataQuery) -> Result<DataResponse, Error>;
    async fn capabilities(&self) -> Vec<Capability>;
    async fn status(&self) -> AgentStatus;
}

Task Lifecycle¶

pub enum TaskStatus {
    Pending,
    Accepted,
    InProgress,
    Completed,
    Failed,
    Cancelled,
}

pub struct Task {
    pub id: TaskId,
    pub description: String,
    pub parameters: serde_json::Value,
    pub status: TaskStatus,
    pub created_at: DateTime<Utc>,
}

Integration with MCP¶

A2A and MCP are complementary protocols:

MCP: Connect AI models to tools and resources
A2A: Enable communication between AI agents

Planned integration:

MCP-to-A2A Bridge: Expose A2A agents as MCP tools
A2A-to-MCP Adapter: Allow A2A agents to use MCP servers
Unified Client: Single client supporting both protocols

Contributing¶

We're actively seeking community input on the A2A design!

How to Contribute¶

Design Discussions: Share ideas in GitHub Discussions
Specification Review: Comment on the protocol specification
Use Case Input: Share your multi-agent use cases
Early Testing: Join the early adopter program (coming soon)

Areas of Interest¶

Protocol design and message formats
Transport binding selection
Authentication and security models
Agent discovery mechanisms
Task management patterns

Resources¶

External Resources¶

Stay Updated¶

Follow development progress:

GitHub: Watch the repository for updates
Discussions: Join GitHub Discussions
Issues: Track A2A-related issues

Interested in A2A? Star the repository and watch for updates! We'll announce when the implementation begins.