Loading...
🤝
A2A Protocol (Agent2Agent)(A2A)
Open standard for AI agent interoperability enabling seamless collaboration across platforms
Complexity: highMulti-Agent
🎯 30-Second Overview
Pattern: Open standard enabling seamless interoperability between AI agents across platforms
Why: Eliminates vendor lock-in, enables agent ecosystems, accelerates enterprise adoption with standardized communication
Key Insight: Universal Agent Card discovery + OAuth auth + Task ID correlation + SSE streaming = cross-platform collaboration
⚡ Quick Implementation
1Discover:Fetch Agent Card from /.well-known/agent.json
2Authenticate:OAuth 2.0 or API key exchange
3Initiate:POST /tasks with unique Task ID
4Stream:SSE for real-time state updates
5Monitor:Track progress & handle failures
Example: discover() → auth() → initiate_task() → stream_updates() → aggregate_results()
📋 Do's & Don'ts
✅Use standard Agent Card format for capability discovery
✅Implement proper OAuth 2.0 or JWT authentication
✅Support Server-Sent Events for real-time updates
✅Use unique Task IDs for correlation and tracking
✅Handle network failures with exponential backoff
✅Implement message versioning for compatibility
✅Use schema-first contracts for messages and artifacts
✅Prevent circular agent loops with hop-count limits
❌Hardcode agent endpoints or capabilities
❌Send sensitive data without encryption
❌Ignore task lifecycle state management
❌Block on synchronous calls to remote agents
❌Skip capability validation before task delegation
❌Use long-lived or overly broad tokens
🚦 When to Use
Use When
- • Cross-platform agent collaboration
- • Enterprise multi-vendor systems
- • Scalable agent ecosystems
- • Standardized interoperability needs
- • Long-running collaborative tasks
- • Real-time agent coordination
Avoid When
- • Single-platform deployments
- • Simple single-agent workflows
- • Ultra-low-latency requirements
- • Offline-only applications
- • Prototype/development phases
- • Tightly coupled agent systems
📊 Key Metrics
Interoperability Rate
% successful cross-platform tasks
Discovery Latency
Time to fetch & parse Agent Card
Authentication Success
% successful auth handshakes
Task Completion Rate
% tasks completed end-to-end
Real-time Updates
SSE message delivery rate
Network Resilience
% recovery from connection failures
Protocol Compliance
% messages following A2A spec
💡 Top Use Cases
Cross-Platform Research: Research agent (LangChain) → Analysis agent (Google ADK) → Visualization agent (Custom)
Enterprise Integration: Salesforce agent ↔ ServiceNow agent ↔ Slack agent for automated workflows
Multi-Vendor Ecosystems: Customer agent (Vendor A) delegates to specialist agents (Vendors B, C, D)
Distributed Computing: Load balancing across agent instances on different cloud providers
Real-time Collaboration: Multiple agents streaming updates while processing shared documents
Agent Marketplaces: Dynamic discovery and integration of third-party specialist agents
Federated Learning: Agents sharing model updates while preserving data locality
Supply Chain Coordination: Supplier, manufacturer, and logistics agents coordinating via A2A
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🤝
A2A Protocol (Agent2Agent)(A2A)
Open standard for AI agent interoperability enabling seamless collaboration across platforms
Complexity: highMulti-Agent
🎯 30-Second Overview
Pattern: Open standard enabling seamless interoperability between AI agents across platforms
Why: Eliminates vendor lock-in, enables agent ecosystems, accelerates enterprise adoption with standardized communication
Key Insight: Universal Agent Card discovery + OAuth auth + Task ID correlation + SSE streaming = cross-platform collaboration
⚡ Quick Implementation
1Discover:Fetch Agent Card from /.well-known/agent.json
2Authenticate:OAuth 2.0 or API key exchange
3Initiate:POST /tasks with unique Task ID
4Stream:SSE for real-time state updates
5Monitor:Track progress & handle failures
Example: discover() → auth() → initiate_task() → stream_updates() → aggregate_results()
📋 Do's & Don'ts
✅Use standard Agent Card format for capability discovery
✅Implement proper OAuth 2.0 or JWT authentication
✅Support Server-Sent Events for real-time updates
✅Use unique Task IDs for correlation and tracking
✅Handle network failures with exponential backoff
✅Implement message versioning for compatibility
✅Use schema-first contracts for messages and artifacts
✅Prevent circular agent loops with hop-count limits
❌Hardcode agent endpoints or capabilities
❌Send sensitive data without encryption
❌Ignore task lifecycle state management
❌Block on synchronous calls to remote agents
❌Skip capability validation before task delegation
❌Use long-lived or overly broad tokens
🚦 When to Use
Use When
- • Cross-platform agent collaboration
- • Enterprise multi-vendor systems
- • Scalable agent ecosystems
- • Standardized interoperability needs
- • Long-running collaborative tasks
- • Real-time agent coordination
Avoid When
- • Single-platform deployments
- • Simple single-agent workflows
- • Ultra-low-latency requirements
- • Offline-only applications
- • Prototype/development phases
- • Tightly coupled agent systems
📊 Key Metrics
Interoperability Rate
% successful cross-platform tasks
Discovery Latency
Time to fetch & parse Agent Card
Authentication Success
% successful auth handshakes
Task Completion Rate
% tasks completed end-to-end
Real-time Updates
SSE message delivery rate
Network Resilience
% recovery from connection failures
Protocol Compliance
% messages following A2A spec
💡 Top Use Cases
Cross-Platform Research: Research agent (LangChain) → Analysis agent (Google ADK) → Visualization agent (Custom)
Enterprise Integration: Salesforce agent ↔ ServiceNow agent ↔ Slack agent for automated workflows
Multi-Vendor Ecosystems: Customer agent (Vendor A) delegates to specialist agents (Vendors B, C, D)
Distributed Computing: Load balancing across agent instances on different cloud providers
Real-time Collaboration: Multiple agents streaming updates while processing shared documents
Agent Marketplaces: Dynamic discovery and integration of third-party specialist agents
Federated Learning: Agents sharing model updates while preserving data locality
Supply Chain Coordination: Supplier, manufacturer, and logistics agents coordinating via A2A
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.