Agents Working Together › Real-World Multi-Agent Frameworks

The patterns we've discussed are useful concepts, but implementing multi-agent systems from scratch can be complex. Fortunately, several frameworks have emerged to make this easier. Let's look at some of the most important ones.

One of the most significant developments in multi-agent systems is the Agent-to-Agent (A2A) Protocol, developed by Google as an open standard. Think of A2A as a common language that allows different AI agents to discover each other, understand what each can do, and work together, even if they were built by different teams using different technologies.

The key innovation of A2A is the concept of Agent Cards. Just as a business card tells you what someone does and how to contact them, an Agent Card describes an agent's capabilities, what inputs it accepts, what outputs it produces, and how other agents can communicate with it.

Here's what an Agent Card might look like conceptually:

{
  "agent_name": "Research Assistant",
  "capabilities": [
    "web_search",
    "document_analysis",
    "fact_checking"
  ],
  "input_format": {
    "type": "text",
    "max_length": 2000
  },
  "output_format": {
    "type": "structured_report",
    "includes": ["sources", "summary", "confidence_score"]
  },
  "endpoint": "https://api.example.com/research-agent",
  "authentication": "bearer_token"
}

With A2A, when your personal assistant needs research help, it can:

1. Discover available research agents by querying an agent registry
2. Understand what each agent can do by reading their Agent Cards
3. Communicate with the chosen agent using the standardized protocol
4. Receive results in a predictable format

This standardization is crucial because it means agents from different organizations, built with different frameworks, can still work together. Your assistant built with Anthropic's Claude could seamlessly collaborate with a specialized research agent built with OpenAI's GPT-5 or a data analysis agent built with Google's Gemini.

The A2A protocol also handles important practical concerns:

• Security: Supports standard authentication and encrypted communication
• Task Management: Provides structures for defining, tracking, and completing collaborative tasks
• Modality Support: Works with text, audio, video, and other data types

While A2A focuses on agent-to-agent communication, other frameworks address different aspects of multi-agent systems:

Model Context Protocol (MCP): Developed by Anthropic, MCP focuses on connecting agents with tools and data sources. While A2A helps agents talk to each other, MCP helps agents access the resources they need. In practice, you might use both: MCP to give your agents access to databases and APIs, and A2A to let those agents coordinate with each other.

Agent Development Kit (ADK): Google's ADK is a toolkit for building agents. Think of it this way: ADK helps you build individual agents, while A2A helps those agents communicate. They're complementary. ADK is the construction tool, A2A is the communication protocol.

LangGraph: A framework for building stateful, multi-agent applications with explicit control flow. LangGraph excels at defining complex workflows where agents need to maintain state and follow specific patterns of interaction.