A2A Protocol: How Agent-to-Agent Communication Is Creating the Internet of AI

We've spent the last two years building AI agents. Now we need them to talk to each other.

That's the core premise behind Google's Agent-to-Agent (A2A) protocol — an open standard that lets AI agents from different vendors discover each other, negotiate capabilities, and collaborate on tasks. Think of it as HTTP for the agent era: a common language that lets independently built AI systems work together without custom integration for every pair.

If MCP (Model Context Protocol) gave agents hands to use tools, A2A gives them the ability to delegate, negotiate, and form working relationships with other agents.

And for businesses building automation stacks, this changes everything.

The Interoperability Problem

Right now, most businesses deploying AI agents face a familiar pattern:

• Vendor lock-in: Your customer service agent can't talk to your finance agent because they're built on different platforms
• Custom glue code: Every agent-to-agent interaction requires bespoke integration
• Siloed intelligence: Each agent knows its domain but can't leverage knowledge from others
• Scaling ceiling: Adding a new agent means integrating with every existing one

Sound familiar? It's the same problem the web faced before HTTP, the same problem APIs faced before REST, and the same problem devices faced before USB.

The answer is always the same: a standard protocol.

What A2A Actually Does

A2A defines three core capabilities:

1. Agent Discovery

Every A2A-compatible agent publishes an Agent Card — a machine-readable description of what it can do, what inputs it accepts, and what outputs it produces. Think of it as a business card for AI agents.

{
  "name": "Invoice Processing Agent",
  "description": "Extracts, validates, and routes invoices",
  "capabilities": ["document_extraction", "validation", "routing"],
  "inputFormats": ["pdf", "image", "email"],
  "outputFormats": ["structured_json", "accounting_entry"],
  "authentication": "oauth2",
  "endpoint": "https://agents.example.com/invoices"
}

Any agent on the network can discover this card, understand the capability, and initiate collaboration — without a human wiring things together.

2. Task Negotiation

When Agent A wants Agent B to do something, they don't just fire and forget. A2A supports a structured negotiation:

1. Request: "Can you process this invoice?"
2. Capability check: "Yes, I handle PDFs. Estimated time: 30 seconds."
3. Agreement: "Proceed. Here's the document and my callback."
4. Execution: Agent B processes the task
5. Delivery: Results returned in the agreed format

This negotiation model means agents can gracefully handle situations where they're overloaded, don't support a format, or need additional information.

3. Collaborative Workflows

The real power emerges when multiple agents form workflows dynamically:

• A customer service agent receives a complaint about a billing error
• It discovers and delegates to a billing agent to investigate the charge
• The billing agent finds a discrepancy and escalates to a finance agent for adjustment
• The finance agent processes the credit and notifies the notification agent
• The customer gets an email confirmation — all without human intervention

Each agent is independently built, possibly by different vendors, running on different infrastructure. A2A makes it work.

A2A vs MCP: Complementary, Not Competing

There's understandable confusion about how A2A relates to Anthropic's Model Context Protocol. Here's the simple distinction:

	MCP	A2A
Purpose	Connect agents to tools and data	Connect agents to each other
Analogy	USB port (plug in peripherals)	TCP/IP (network communication)
Scope	Agent ↔ Tool	Agent ↔ Agent
Initiated by	Anthropic	Google
Use case	"Read this database"	"Ask the finance agent to verify this"

In practice, a well-built agent uses both: MCP to access tools and data sources, A2A to collaborate with other agents. They're complementary layers of the agent infrastructure stack.

Why Business Leaders Should Care

1. Best-of-Breed Agent Selection

Without A2A, choosing an AI vendor means buying into their entire ecosystem. With A2A, you can pick the best customer service agent from one vendor, the best finance agent from another, and the best HR agent from a third — and they'll work together.

This is the same shift we saw with microservices replacing monoliths, and with best-of-breed SaaS replacing all-in-one enterprise suites.

2. Reduced Integration Costs

Every custom integration between agents currently costs time and money to build and maintain. A2A replaces bespoke point-to-point integrations with a standard protocol. Instead of building N×N integrations, you build N agents that all speak the same language.

3. Future-Proofing

As the agent ecosystem explodes, the businesses that adopt standard protocols early will be able to plug in new capabilities as they emerge — without rebuilding their entire automation stack.

4. Supply Chain AI

Imagine your procurement agent negotiating with your supplier's sales agent, your logistics agent coordinating with your shipping partner's dispatch agent, and your quality control agent sharing inspection results with your customer's receiving agent.

A2A makes inter-organisational agent collaboration possible. This is where it gets genuinely transformative.

Real-World Scenarios

Scenario 1: Automated Vendor Onboarding

1. Procurement agent identifies a new supplier need
2. Discovers potential vendor agents through A2A registry
3. Requests capability cards and pricing from each
4. Compliance agent checks each vendor against regulatory requirements
5. Finance agent verifies payment terms and credit risk
6. Legal agent reviews contract terms
7. Best vendor is selected and onboarded — largely autonomously

Today this process takes weeks and involves five departments. With A2A-enabled agents, it could take hours.

Scenario 2: Cross-Company Project Delivery

A consultancy's project management agent coordinates with:

• The client's requirements agent for specification changes
• A freelancer marketplace's talent matching agent for specialist skills
• An infrastructure provider's deployment agent for environment setup
• A testing service's QA agent for automated review

Each agent is independently operated, but A2A enables fluid collaboration.

Implementation Considerations

Security and Trust

A2A includes authentication and authorisation layers. You control which external agents can discover your agents, which capabilities they can access, and what data is shared. Think OAuth for agents.

Key considerations:

• Agent identity verification — Is this really your bank's compliance agent?
• Capability scoping — The marketing agent can request customer counts but not individual records
• Audit trails — Every inter-agent interaction is logged
• Rate limiting — Prevent runaway agent conversations

Starting Small

You don't need to expose agents externally to benefit from A2A. Start with internal agent interoperability:

1. Build agent cards for your existing internal agents
2. Implement A2A for agent-to-agent communication within your organisation
3. Standardise how your agents discover and delegate to each other
4. Gradually extend to trusted partner agents

The Agent Registry

As A2A matures, expect enterprise agent registries — internal directories of available agents, their capabilities, SLAs, and owners. Think of it as an internal API catalogue, but for agents.

The Bigger Picture

A2A represents a philosophical shift in how we think about AI deployment. We're moving from:

• Individual agents → Agent ecosystems
• Custom integrations → Standard protocols
• Vendor lock-in → Interoperability
• Human orchestration → Agent self-organisation

The internet didn't become transformative because of individual websites. It became transformative because websites could link to each other, APIs could call each other, and data could flow between systems. A2A is doing the same thing for AI agents.

What to Do Now

1. Understand the landscape: MCP for tools, A2A for agent-to-agent. Both matter.
2. Design agents with interoperability in mind: Clean inputs, structured outputs, well-defined capabilities.
3. Watch the ecosystem: Major vendors (Google, Salesforce, SAP) are adopting A2A. Your enterprise software may gain A2A support natively.
4. Start with internal protocols: Even before formal A2A adoption, structuring your agents with discoverable capabilities and standard interfaces pays dividends.
5. Plan for the multi-vendor future: The days of single-vendor AI stacks are numbered.

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The companies that win the AI era won't be those with the smartest individual agents. They'll be the ones whose agents work best together. A2A is how that happens.

Interested in building interoperable AI agent systems? Get in touch to discuss your multi-agent strategy.