Agent-to-Agent Communication: AI's Missing Link

Why AI Agents Can't Talk to Each Other (And How A2A Aims to Fix It)

Jun 19, 2025

By David Proctor | Published: June 19, 2025 | Part 2 of AI Protocol Analysis Series

Summary

Google’s Agent2Agent (A2A) protocol, launched at Cloud Next ’25, tackles a problem we didn’t know we had until AI agents started actually working: How do you get different agents to talk to each other?

Think about it. Your sales agent needs to coordinate with your inventory agent, which needs to talk to your logistics agent, which might need to work with your vendor’s supply chain agent. Right now, that’s a mess of custom integrations. A2A aims to fix that.

The Big Picture: While MCP handles the vertical scaling (agents connecting to tools), A2A enables horizontal scaling (agents connecting to other agents). Together, they create the multi-directional connectivity that AI systems need to actually be useful at scale.

Bottom Line: Whether A2A wins or not doesn’t matter as much as the fact that someone is solving agent-to-agent communication. This is infrastructure, not innovation, and infrastructure is what makes innovation possible.

The Problem A2A Actually Solves

Before A2A: Integration Chaos

Right now, if you want two AI agents to collaborate, you have three options:

Build custom integrations (expensive, brittle)
Use the same framework (vendor lock-in, limited)
Manual handoffs (defeats the purpose)

It’s like the early days of email, when AOL users couldn’t email CompuServe users. Everyone had email, but nobody could talk to anyone else.

The Restaurant Analogy, Part 2

Remember the MCP restaurant analogy from our previous article? MCP standardized how agents order from the kitchen (tools and data). A2A standardizes how restaurants coordinate with each other.

Before A2A: Your reservation agent can’t talk to the delivery agent, which can’t coordinate with the payment processing agent. Each restaurant (company) operates in isolation.

With A2A: Agents can now discover each other, negotiate capabilities, and collaborate in real time. It’s like Uber Eats for agents, a single orchestration layer that knows how to:

provide menus 🍽️
schedule your driver 🚗
coordinate delivery from multiple kitchens 🍜🍕
handle payment and loyalty rewards 💳

All through independent services that now interoperate via a shared protocol, not via custom APIs or vendor lock-in.

How A2A Actually Works

The Technical Foundation (Without the Jargon)

A2A is built on five core concepts that work together:

1. Agent Cards - Digital Business Cards

Think LinkedIn profiles, but for AI agents. Each agent publishes a JSON file at /.well-known/agent.json that says:

“Hi, I’m the inventory agent”
“I can check stock levels, reserve items, and update quantities”
“Here’s how to authenticate and connect to me”

2. Task Objects - Work Packages

Instead of simple request-response, A2A uses task-oriented communication:

Task: "Reserve 100 units of SKU-12345 for order #67890"
Status: submitted → working → completed
Result: "Reserved. Held until 2025-06-20 14:30 UTC"

3. Message Exchange - The Conversation Layer

Uses JSON-RPC 2.0 over HTTP. Not revolutionary, but reliable. Supports text, audio, and video content.

4. Streaming Updates - Real-time Progress

Long-running tasks (like “analyze this 10GB dataset”) stream progress updates via Server-Sent Events (SSE).

5. Security Framework - Enterprise-Grade Protection

Multiple auth methods (OAuth, API keys, etc.) plus transport encryption and audit logging.

The Flow in Practice

1. Agent Discovery: "Who can handle inventory management?"
2. Capability Check: "Can you reserve 100 units of this SKU?"
3. Task Submission: "Please reserve them for order #67890"
4. Streaming Updates: "Processing... validating... confirmed"
5. Task Completion: "Reserved. Here's your confirmation token"

A2A vs. MCP: The Scaling Dimensions

This is where it gets interesting. A2A and MCP aren’t competitors, they’re complementary pieces of AI infrastructure that enable different types of scaling:

Vertical Scaling (MCP): Agent ↔︎ Tools

What: Agents connecting to databases, APIs, files, systems
Example: Sales agent queries CRM, checks inventory, updates spreadsheet
Benefit: Eliminates custom tool integrations
Caveat:
In my research, I’ve found that MCP, while promising, runs into scalability issues at larger tool volumes. Right now, it lacks dynamic, on-demand tool registration. Every tool must be declared upfront, which bloats context and reduces flexibility.
More on this in Part 3.

Horizontal Scaling (A2A): Agent ↔︎ Agent

What: Agents coordinating with other agents
Example: Sales agent coordinates with inventory agent, which coordinates with logistics agent
Benefit: Eliminates custom agent-to-agent integrations

Multi-Directional Scaling: The Real Magic

When you combine both protocols, you get something powerful:

Each agent can use MCP to connect to their specialized tools while using A2A to coordinate with other agents. This creates a mesh of capabilities that’s greater than the sum of its parts.

The Competitive Landscape: Why This Matters More Than Who Wins

A2A vs. Anthropic’s MCP

People keep asking “Is A2A competing with MCP?” The answer is no, they solve different problems:

A2A vs. Other Agent Protocols

A2A isn’t the only game in town. There’s also:

Agent Network Protocol (ANP) - Focuses on open internet scenarios
Various proprietary solutions - Company-specific implementations

But here’s the thing: It doesn’t matter which one wins.

The Network Protocol Analogy

In the early internet, we had competing routing protocols:

RIP (Routing Information Protocol)
OSPF (Open Shortest Path First)
BGP (Border Gateway Protocol)
IS-IS (Intermediate System to Intermediate System)

Today, BGP dominates external routing, OSPF handles most internal routing. The specifics matter less than the fact that we have standardized routing protocols. Without them, the internet couldn’t exist.

Agent-to-agent communication is at the same inflection point. We need a standard, not necessarily this standard.

Why Agent-to-Agent Protocols Are Critical for AI

The Enterprise Reality Check

Most AI demos show single agents doing isolated tasks. But real enterprise workflows involve multiple systems, departments, and even companies. Consider:

Supply Chain Coordination:

Demand forecasting agent (yours)
Inventory management agent (yours)
Logistics optimization agent (3PL provider's)
Payment processing agent (bank's)
Compliance checking agent (regulatory service)

Without standardized communication, each connection requires custom development. That's N×M complexity, where N is your agents and M is everyone else's agents.

With A2A-style protocols: Each agent speaks the same language. Integration becomes configuration, not coding.

A2A Reality Check: It's Not Global Magic

Here's what A2A actually does (and doesn't do):

What A2A Solves Today

Intra-domain orchestration — your agents discovering and delegating to each other within your controlled environment
Cross-vendor collaboration — but only when all participants register with a common orchestrator or registry
Standardized handshakes — Agent Cards, task negotiation, streaming, and secure communication patterns

What A2A Doesn't Solve (Yet)

Global agent discovery — agents don't magically find each other across the open internet
Public agent marketplaces — no DNS-like system for agent lookup
Automatic cross-company workflows — requires manual configuration of external agent endpoints

The Current Mechanism

A2A agents discover each other through:

Well-known URLs — fetching Agent Cards from /.well-known/agent.json
Internal registries — your orchestrator maintains a catalog of available agents
Direct configuration — manually adding external agent endpoints

Think of it like early corporate email systems: great for internal communication, but external collaboration required explicit setup.

The Network Effects Problem

AI agents are only as valuable as what they can connect to. A scheduling agent that can't coordinate with calendar systems, payment agents, and notification services isn't very useful.

Network effects kick in when agents can easily discover and connect to each other. The more agents that speak A2A (or any standard protocol), the more valuable each individual agent becomes.

But here's the catch: network effects require networks. A2A provides the communication standard, but someone still needs to build the registries and discovery mechanisms.

The Innovation Unlock

Standardized protocols don't just make existing workflows easier, they enable entirely new possibilities:

Cross-Company Workflows: Your project management agent coordinates directly with your vendor's delivery agent and your client's approval agent.

Agent Marketplaces: Plug-and-play specialized agents from different vendors without custom integration work.

Emergent Intelligence: When agents can easily coordinate, new behaviors emerge that no single agent could achieve alone.

The Evolution Path: From Internal to Internet-Scale

The logical next step for A2A is a federated discovery architecture:

Internal Layer

Your orchestrator hosts your team's agents and Agent Cards
Handles internal task routing and coordination

Gateway Layer

Bridges multiple organizations' internal orchestrators
Performs selective discovery of external agents
Could use standardized endpoints (like .well-known/agent.json) for cross-domain discovery

Global Registry Layer

Public directories of A2A-compliant agents
Industry-specific agent catalogs (legal, finance, logistics)
Trust and reputation systems for agent marketplaces

This mirrors how enterprise software typically evolves: internal systems → service meshes → federated APIs → public marketplaces.

The Bottom Line: A2A gives us the communication standard. Now we need to build the networks.

Technical Deep Dive: What Makes A2A Different

The SSE Decision: Why Google Stuck with Server-Sent Events

Most protocols are moving away from Server-Sent Events (SSE) to more modern streaming approaches. Anthropic’s MCP recently migrated from SSE to “Streamable HTTP” for better scalability.

Google kept SSE for A2A. Why?

The Enterprise Use Case: A2A targets enterprise scenarios where:

Infrastructure is predictable and robust
Real-time collaboration is more important than stateless scalability
Persistent connections are acceptable for guaranteed delivery

Task-Oriented Communication: A2A’s task lifecycle model benefits from persistent streaming connections that deliver incremental updates without reconnection overhead.

This decision reveals A2A’s positioning: enterprise-first, reliability over raw scalability.

Security: More Than Just Authentication

A2A implements enterprise-grade security because agent-to-agent communication involves sensitive business data crossing organizational boundaries:

Multiple auth schemes: OAuth, API keys, OpenID Connect
Transport encryption: Mandatory HTTPS/TLS
Audit logging: Comprehensive traceability
Role-based access: Granular permissions

Early Adoption Patterns: What’s Actually Happening

The 50+ Partner Launch

A2A launched with backing from major enterprise vendors:

Platform Partners: Salesforce, ServiceNow, Workday, SAP
System Integrators: Accenture, Deloitte, McKinsey, PwC
AI Platforms: Cohere, LangChain

This isn’t just technology validation, it’s market validation. These companies see agent-to-agent communication as inevitable.

Real-World Use Cases Emerging

Customer Service Orchestration: Multiple specialized agents (diagnostic, knowledge base, resolution) collaborate on complex inquiries.

Supply Chain Coordination: Real-time collaboration between inventory, logistics, and order processing agents across multiple vendors.

Healthcare Care Coordination: Patient record agents, scheduling agents, and clinical decision support agents coordinate while maintaining HIPAA compliance.

The Path Forward: Building Multi-Directional AI Systems

Combining MCP and A2A: A Practical Example

Let’s walk through a real scenario: E-commerce Order Processing

Each agent is specialized and can be developed/maintained independently, but they coordinate seamlessly through standardized protocols.

What This Means for Developers

Yesterday: Building an AI system means either:

Staying within one vendor’s ecosystem (limited capability)
Building custom integrations for everything (expensive, brittle)

Now (with protocols like A2A and MCP): Building an AI system means:

Choose best-of-breed agents from any vendor
Plug them together with standard protocols
Focus on business logic, not integration plumbing

Critical Success Factors: What Could Go Wrong

The Standards War Risk

Multiple competing protocols could fragment the ecosystem. We’ve seen this before:

Video formats: Betamax vs. VHS
Mobile platforms: iOS vs. Android
Messaging: SMS vs. proprietary platforms
High-Def disks: Blue-ray vs. HD DVD

The solution: Focus on interoperability between protocols, not just within them.

The Complexity Trap

A2A could become too complex for widespread adoption. Features like multi-modal messaging, complex authentication, and streaming protocols create implementation overhead.

The balance: Provide simple defaults while supporting advanced use cases.

The Enterprise Bottleneck

A2A's enterprise focus could slow adoption if it doesn't address developer and startup needs. While proving agent-to-agent communication works within corporate intranets has clear value, the true gold rush happens when A2A scales to internet-wide agent discovery and collaboration.

The current value:

Enterprise workflows become more efficient when internal agents can coordinate seamlessly.

The exponential opportunity:

When any agent can discover and collaborate with any other agent across the open internet, turning AI from isolated tools into a global, intelligent network.

The opportunity:

Simplified implementations for smaller use cases while maintaining enterprise capabilities, plus a clear evolution path from intranet protocols to internet-scale agent networks.

Recommendations: How to Navigate This Transition

For Developers Building AI Systems

Start with MCP for agent-to-tool integration (it’s more mature)
Experiment with A2A for agent-to-agent scenarios
Design for protocol independence - don’t lock yourself into any single approach
Focus on business value - protocols are plumbing, not products

For Enterprises Evaluating AI Infrastructure

Prioritize interoperability over feature completeness
Plan for multi-protocol environments - you’ll likely need both MCP and A2A-style protocols
Invest in integration expertise - protocols reduce but don’t eliminate integration work
Start with pilot projects to understand practical implications

For the AI Community

Support protocol interoperability efforts
Contribute to open standards rather than proprietary solutions
Share real-world experience to improve protocol designs
Focus on practical adoption over theoretical perfection

Conclusion: The Infrastructure Moment

We’re at an inflection point for AI infrastructure. The single-agent demos are impressive, but real AI value comes from coordination and collaboration. That requires standardized communication protocols.

Google’s A2A protocol might win, or it might not. What matters is that someone is solving agent-to-agent communication, just like someone needed to solve agent-to-tool communication (MCP’s domain).

The bigger picture: MCP enables vertical scaling (agents to tools), A2A enables horizontal scaling (agents to agents). Together, they create the multi-directional connectivity that transforms AI from isolated demos into integrated systems.

The infrastructure reality: Great infrastructure is invisible. You don’t think about TCP/IP when browsing the web, and you won’t think about A2A when your travel agent coordinates with parking, dining, and entertainment agents to plan your evening.

But without that infrastructure, none of it works.

What’s next: Start experimenting with both MCP and A2A. Build systems that can take advantage of multi-directional scaling. And remember, we’re not just building better agents, we’re building the nervous system for an AI-powered world.

Resources and Next Steps

Try It Yourself

MCP Servers: Trilogy AI CoE MCP Implementation
A2A Specification: Official Google A2A Docs
MCP Inspector: Debug and test MCP connections
MCP Before & After: Visual Representation