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ArchitectureOct 12, 2025

Designing Multi-Agent Systems for Scale

Exploring the Google A2A (Agent-to-Agent) Protocol and how it enables the next generation of interoperable AI ecosystems.

The era of isolated AI agents is ending. As we move from simple chatbots to complex autonomous systems, the primary bottleneck isn't intelligence—it's interoperability.

Enter the A2A (Agent-to-Agent) Protocol. Originally introduced by Google and now an open standard under the Linux Foundation, A2A provides the missing "TCP/IP" layer for AI agents, allowing them to discover, negotiate, and collaborate on complex tasks without sharing internal state.

The Architecture of A2A

At its core, A2A decouples the agent's internal logic from its communication interface. This separation allows an agent built in LangGraph to seamlessly delegate a sub-task to an agent built in CrewAI or AutoGen.

The protocol relies on three key pillars:

  • Agent Cards: Standardized manifests that declare an agent's capabilities, inputs, and pricing.
  • Discovery Layer: A decentralized registry where agents can publish their presence and query for collaborators.
  • Secure Handshake: A mechanism for authentication and context negotiation before task execution.

A2A Architectural Flow

The following diagram illustrates the lifecycle of an A2A transaction, from discovery to task completion.

Initiator Agent(Client)Discovery RegistryLookup CapabilitiesTarget Agent(Provider)1. Query Agent Card2. Address & Key3. A2A Protocol
Figure 1: High-level data flow in an A2A transaction.

Why It Matters

By standardizing the interface, A2A allows us to build composite AI systems. A specialized "Research Agent" can be swapped out for a better one without rewriting the core orchestration logic, much like how microservices transformed web development.

This "public internet for agents" encourages a modular ecosystem where developers focus on building specialized, high-performing agents rather than monolithic, walled-garden applications.