There was a time when getting two software systems to communicate was mostly an API problem.
You defined an endpoint, documented the request format, handled authentication, exchanged JSON, and hoped nobody changed the contract without telling you.
AI agents are making that model feel a little old-fashioned.
An AI agent isn’t just another piece of software waiting for a fixed command. It may interpret a request, decide what information it needs, call tools, delegate work, wait for another system, and eventually come back with a result.
That creates an interesting problem:
What happens when one AI agent needs another AI agent to get the job done?
This is where the Agent2Agent, or A2A, protocol enters the picture.
And while the acronym may sound like yet another AI buzzword, the underlying idea is actually pretty important.
What exactly is A2A?
Agent2Agent is an open protocol designed to allow AI agents to discover each other, communicate, exchange information and collaborate on tasks.
Google introduced A2A in April 2025 as an open protocol for agent interoperability. The idea was straightforward: companies shouldn’t have to build a custom integration every time they want one AI agent to work with another agent built by a different company or framework.
Think about it like this.
Imagine a travel company’s AI assistant receives this request:
“Find me the cheapest flight to Goa, check hotel availability near the beach, and arrange airport transportation.”
One giant AI system could theoretically do everything.
But that’s not necessarily the best architecture.
Instead, you could have:
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A Flight Agent that understands airline inventory
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A Hotel Agent that handles accommodation
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A Transport Agent that understands taxis and airport transfers
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A Payment Agent that handles transactions
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A Travel Coordinator Agent that brings everything together
The coordinator doesn’t need to know every internal detail of every system.
It needs a reliable way to communicate with them.
That’s the promise of A2A.
Why can’t normal APIs solve this?
This is where things get interesting.
Traditional APIs are excellent when the interaction is predictable.
For example:
GET /customer/123
The server knows exactly what you’re asking for.
But agents aren’t always predictable.
An agent might receive:
“Help me find the best supplier for this order.”
It may need to investigate several options, ask another agent for pricing, compare delivery times, request additional information, and potentially continue working for several minutes.
Google describes this distinction as one of the reasons A2A exists: traditional APIs are generally rigid and deterministic, while agents need to support more dynamic collaboration.
That doesn’t mean APIs are going away.
Far from it.
Instead, we may end up with a layered architecture:
APIs → tools and deterministic services
MCP → agent access to tools, data and context
A2A → agent-to-agent collaboration
That distinction is becoming increasingly useful for developers building serious agentic systems.
MCP and A2A are not competitors
The AI industry has accumulated enough acronyms recently to make anyone’s head hurt.
MCP.
A2A.
UCP.
AP2.
A2UI.
And yes, there are even more.
But MCP and A2A solve different problems.
A simple way to remember it is:
MCP helps an agent use things.
A2A helps an agent work with other agents.
For example, a customer-support agent might use MCP to access:
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Customer records
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A CRM
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Internal documents
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Order information
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A ticketing system
But suppose the customer’s problem requires a specialist agent from another department.
The support agent could potentially communicate with that specialist through an agent-to-agent protocol.
Google’s own developer guidance describes A2A as a way for agents to discover and communicate with other agents, including agents built by different teams, frameworks and deployed on different servers.
That’s a much bigger architectural idea than simply giving a chatbot more tools.
The real shift: from AI assistant to AI team
This may be the most important part.
Most people still imagine AI as:
Human → AI → Answer
But agentic systems are gradually moving toward something more like:
Human → Agent → Other Agents → Tools → Data → Decision → Action
The AI isn’t necessarily doing every task itself.
It becomes a coordinator.
Imagine a small online business.
Instead of hiring separate software systems for every function, the company could eventually operate a network of specialized agents:
Sales Agent
Finds leads, qualifies prospects and schedules meetings.
Research Agent
Collects market information and competitor updates.
Finance Agent
Analyzes invoices, payments and cash flow.
Customer Support Agent
Handles routine customer problems.
Operations Agent
Monitors orders and coordinates suppliers.
Manager Agent
Looks across the other agents and decides what needs attention.
That starts looking less like a chatbot and more like a digital organization.
We’re not completely there yet.
But the architecture is beginning to point in that direction.
Why this matters for businesses
The biggest opportunity may not be consumer chatbots.
It may be enterprise workflows.
Consider a loan application.
Today, a financial institution may have separate systems for:
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Customer onboarding
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KYC
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Credit assessment
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Fraud detection
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Document verification
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Banking
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Collections
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Customer communication
Connecting these systems has historically required a significant amount of integration work.
Now imagine specialized AI agents operating around those systems.
A customer onboarding agent could collect information.
A document agent could check submitted files.
A fraud agent could investigate suspicious patterns.
A credit agent could analyze the application.
A communication agent could explain the next steps to the customer.
The important part isn’t that every one of these systems uses the same AI model.
In fact, they probably shouldn’t.
One agent could use Gemini.
Another could use an open-weight model.
Another might not use an LLM at all.
A2A’s value is the possibility of interoperability between these different systems.
Google has already demonstrated cross-language multi-agent architectures where agents built using different programming languages can collaborate through A2A.
That’s an important detail.
The future of AI probably won’t be one giant model doing everything.
It may look more like a collection of specialized systems that know how to cooperate.
But there is a catch
This sounds exciting.
It also creates a new class of problems.
If one AI agent can ask another AI agent to perform an action, who is responsible when something goes wrong?
Suppose:
Agent A asks Agent B to approve a transaction.
Agent B delegates part of the task to Agent C.
Agent C makes a mistake.
Who made the decision?
Who has permission to perform the action?
Who is allowed to see the data?
Who keeps the audit trail?
These questions become extremely important when agents move beyond generating text and start taking real-world actions.
Security therefore becomes just as important as interoperability.
A2A was designed with enterprise authentication and authorization considerations, and the protocol is built on established technologies including HTTP, Server-Sent Events and JSON-RPC. It also supports long-running tasks, which matters for workflows that don’t finish in a few seconds.
But having a protocol doesn’t automatically make an agent system safe.
Companies still need strong identity, permissions, monitoring, logging, human approvals and clear boundaries around what an agent is allowed to do.
The boring part might actually be the most important
There’s a tendency in AI discussions to focus on the flashy stuff.
Bigger models.
More parameters.
Better benchmarks.
Humanoid robots.
AI that writes code.
But infrastructure can be just as transformative.
Nobody gets excited about TCP/IP anymore.
Yet the internet wouldn’t exist in its modern form without common communication standards.
The same thing could happen with AI agents.
If thousands of agents eventually need to interact across companies, applications and cloud providers, interoperability becomes infrastructure.
And infrastructure is rarely exciting until it becomes indispensable.
Where A2A could go next
The interesting question isn’t whether every application will suddenly become a multi-agent system.
It won’t.
Many applications don’t need one.
If a simple API can solve the problem, using an AI agent may actually make the system slower, more expensive and harder to control.
But for complex workflows, the equation changes.
Areas such as:
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Financial services
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Healthcare administration
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Enterprise IT
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Supply chains
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Travel
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E-commerce
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Software development
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Customer support
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Research
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Business operations
could benefit from specialized agents collaborating across organizational boundaries.
Google’s recent A2A work highlights use cases ranging from commerce and DevOps to telecommunications and scientific workflows, showing that the concept is already moving beyond simple demonstrations.
So, are AI agents replacing software?
Not really.
They’re changing what software can look like.
For decades, we designed software around predefined workflows.
Someone writes the rules.
The system follows the rules.
AI introduces a different possibility:
The system can interpret the goal and dynamically decide which capabilities it needs.
A2A adds another layer:
The system may be able to ask another intelligent system for help.
That’s a subtle change, but potentially a very big one.
The next generation of enterprise software may therefore not be a collection of isolated applications.
It could be a network of specialized agents working across those applications.
And the winners may not necessarily be the companies with the biggest AI model.
They may be the companies that figure out how to make all these models, agents, tools and data systems work together reliably.
Final Thought
We’re still early.
A2A isn’t some magic switch that turns today’s chatbots into autonomous companies overnight. There are still hard problems around security, reliability, cost, identity, governance and accountability.
But the direction is worth watching.
The first phase of generative AI was largely about talking to AI.
The next phase is increasingly about giving AI the ability to do things.
And the phase after that could be about AI systems working with other AI systems.
That’s when the conversation gets much more interesting.
Because once agents can reliably collaborate, we aren’t just building smarter assistants anymore.
We’re starting to build networks of software that can work together.
Sources & Further Reading
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Google Developers — Agent2Agent Protocol
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Google Developers — How A2A is Building a World of Collaborative Agents
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Google Developers — Developer’s Guide to AI Agent Protocols
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Google Developers — Cross-Language Multi-Agent Team with ADK and A2A