Part 1: Agents Without Borders: How A2A, MCP, & OAK Work

Agents Without Borders: How A2A, MCP, and OAK Are Building the Future of AI Infrastructure

We’re standing at the edge of a new era in artificial intelligence — one where models no longer work in silos, but instead collaborate across tasks, coordinate across systems, and negotiate responsibilities with other agents in real time.

The evolution isn’t just about better models. It’s about better protocols — infrastructure that allows agents to interact with tools, with data, and with each other.

And at the center of that evolution are three acronyms that will define the next generation of AI systems:

• MCP (Model Context Protocol): Enables agents to interface with data and tools.
• OAK (Open Agentic Knowledge): Provides agents with machine-readable documentation to use APIs autonomously.
• A2A (Agent-to-Agent Protocol): A new standard from Google that lets agents talk directly to one another, coordinate state, and form dynamic teams.

Let’s unpack what this stack really does — and how it’s already changing the future of software.

MCP + OAK: The First Generation of Agent Infrastructure

Before we talk about multi-agent systems, let’s quickly review the backbone of modern single-agent tooling.

OAK (Open Agentic Knowledge)

First introduced in 2023 and championed by companies like Replit and OpenAgents, OAK is a public, structured format for describing how APIs work in a way agents can understand. It’s essentially the evolution of OpenAPI — but built with LLMs in mind.

Instead of relying on prompt injections or hardcoded calls, agents can:

• Read API capabilities
• Understand request/response schemas
• Validate inputs dynamically
• Self-generate tool usage from scratch

This turns LLMs from “smart assistants” into intelligent operators — capable of executing actions across dozens of APIs, securely and flexibly.

MCP (Model Context Protocol)

Created to solve the tool usage problem, MCP is an open protocol that enables agents to connect to external tools through lightweight, language-agnostic interfaces.

• MCP Clients live inside the agent.
• MCP Servers wrap external tools (like databases, APIs, apps).
• The protocol allows standardized interaction between them — think of it like GraphQL for AI agents.

MCP isn't just about the handshake — it’s about context. Each interaction carries structured metadata that informs the agent about tool results, error states, latency, and more.

It creates a reliable, repeatable structure for intelligent tool use — something that brittle plug-ins or agent-specific wrappers couldn’t offer at scale.

But MCP and OAK are still agent-to-tool protocols.

They don’t help when you need multiple agents working in sync.

The Next Leap: Agent-to-Agent Protocols (A2A)

In early 2024, Google quietly published its vision for the A2A Protocol — short for Agent-to-Agent. And while it may have looked like a simple extension of agent tooling, it represents a far more profound change.

Why A2A?

As AI agents become more capable, we quickly hit a wall:

• One model can’t do everything.
• Specialized agents are better at specific tasks.
• Shared state is difficult without human orchestration.
• Teams of agents often “step on each other’s toes.”

The result? Chaos, inefficiency, and brittle multi-agent systems.

A2A solves this by introducing a formal protocol for communication between agents. Not just for chatting — but for secure, context-rich, negotiated collaboration.

What A2A Enables

Based on Google’s developer blog post, A2A introduces several key capabilities:

1. Agent Authentication & Trust

Each agent carries a signed identity. This enables secure handshakes, reduces spoofing, and introduces governance (i.e., not just any random agent can join a system).

2. Stateful Task Management

Agents share not just messages, but structured representations of tasks — with state, goals, outcomes, and resource dependencies. This enables agents to pause, resume, delegate, or retry without starting from scratch.

3. Capability Negotiation

Think of this like professional collaboration. One agent can ask another:

"Can you summarize this legal document?"

This pattern isn’t possible with current tools that rely on static prompts. A2A makes it a protocol-level feature.

4. Shared World Models

Agents can agree on a shared context: time, location, memory boundaries, privacy constraints, and user instructions. This ensures coordination without confusion — a massive leap forward for multi-agent alignment.

5. Agent Registry & Discovery

Through a central or federated registry, agents can expose capabilities and query others. It’s like an API marketplace — but for autonomous services.

Architecture: The Modern Agent Stack

Here’s how these components work together:

      +---------------------------+
      |      LLM Agents          | ← ChatGPT, Claude, Gemini
      +---------------------------+
                  ↓
        +-------------------+
        |   A2A Protocol    | ← Collaboration Layer
        +-------------------+
         ↓             ↓
+---------------+ +---------------+
|   MCP Client  | |   MCP Client  | ← Each agent still uses MCP for tools
+---------------+ +---------------+
         ↓             ↓
+---------------+ +---------------+
| MCP Servers   | | External APIs |
+---------------+ +---------------+
         ↓
+-------------------------------+
|      Backend Systems          | ← Databases, SaaS, Files
+-------------------------------+
         ↑
+--------------------------+
|         OAK Docs         | ← Tool knowledge repository
+--------------------------+
        

In short:

• OAK tells agents how to use tools.
• MCP lets agents access tools.
• A2A lets agents work with each other.

This stack is not theoretical — it’s already powering experimental LLM networks in labs at Google, Stanford, and across open-source projects like LangGraph and AutoGen.

What A2A Changes for Developers and Enterprises

The implications here are massive.

1. Composable Intelligence

You no longer need one giant model. You can compose intelligent workflows across task-specific agents, each with its own training, knowledge, and specialty.

This is the AI version of microservices — modular, distributed, and scalable.

2. Cross-Model Interoperability

With A2A, you can orchestrate GPT-4 for strategic reasoning, Claude for summarization, Gemini for search, and Perplexity for research — in the same system.

Each agent speaks a common protocol. Vendor silos disappear.

3. Federated AI Systems

Companies can deploy agents across departments — marketing, legal, compliance, finance — and allow them to collaborate, not compete.

A marketing agent can request legal review before launching a campaign. A procurement agent can sync with a compliance agent before issuing contracts.

And all of this can happen autonomously.

🛠️ 4. Agent App Stores Become Reality

Imagine publishing your own "contract reviewer" agent to a public registry. Anyone could query it, collaborate with it, and plug it into their own workflows.

This is how open-source, modular agents will scale — through network effects, not massive retraining.

Final Thoughts: Protocols Over Models

We’ve spent years talking about which LLM is “smarter” — GPT vs Gemini vs Claude.

But the real transformation will come from how well they work together.

We’re moving from “who has the best brain” to “who has the best collaboration layer.”

A2A is the infrastructure for that world.

• MCP got us out of the sandbox.
• OAK gave us documentation and intelligence.
• A2A gives us cooperation.

And taken together? They give us a future where software doesn't just respond — it reasons, coordinates, and works together.