On April 9, 2025, Google introduced something that fundamentally changes how AI agents work together: the Agent-to-Agent Protocol (A2A). If you've ever wished AI agents could act more like a coordinated team and less like disconnected bots working in isolation, this is the protocol to watch.
The artificial intelligence landscape is evolving at breakneck speed, but there's a critical problem lurking beneath the surface of all those impressive AI demos and pilot projects: your AI agents can't actually talk to each other.
New technology, new integration nightmare
Picture this scenario: Your marketing team uses an AI agent built with LangGraph for campaign analysis, your sales team relies on a CrewAI-powered lead qualification system, and your customer support runs on Google's Agent Development Kit. Each works brilliantly in isolation, but when you need them to collaborate on a complex customer journey, you're stuck building expensive custom integrations.
Currently, most agents are locked into their own ecosystems. They don't talk to agents from other vendors, and they need custom glue code to integrate. This creates an O(N²) complexity problem that’s unsustainable as your AI ecosystem grows – and it's one of the biggest barriers preventing businesses from putting AI agents to work.
A2A helps businesses avoid an expensive mess while building AI agents at scale.
What is the A2A protocol?
[Image from Google]
Google's Agent2Agent (A2A) protocol fixes this fundamental problem. It's like introducing HTTP for AI agents – a standard way for them to discover what other agents can do, assign and track tasks, share files and context, and negotiate how they interact with users.
But here's what makes A2A particularly smart: it's a horizontal integration protocol designed to complement, not replace, existing standards.
How is A2A different from MCP?
[Image from Google]
MCP gives agents the tools they need to access databases, APIs, and external resources. A2A gives them colleagues – a shared language for delegating tasks, negotiating formats, exchanging data, and collaborating on multi-step workflows.
Think of it this way:
- MCP (Model Context Protocol by Anthropic) = Agent ↔ Tools (Vertical Integration)
- A2A (by Google) = Agent ↔ Agent (Horizontal Integration)
As Google puts it: "If MCP is the socket wrench, A2A is the conversation between mechanics."
|
Feature |
A2A |
MCP (Anthropic) |
|
Focus |
Agent-to-agent communication |
Agent-to-tool integration |
|
Type |
Horizontal integration |
Vertical integration |
|
Scope |
Multi-agent collaboration |
Single-agent capability expansion |
|
Interoperability |
Cross-platform, cross-vendor |
Mostly tied to an individual agent stack |
Together, they create a powerful, modular AI ecosystem.
How A2A actually works
[Image from Google]
The protocol's elegance lies in its practical simplicity. A2A consists of three core components that mirror how human teams actually collaborate:
Agent cards: digital business cards
Each agent publishes an Agent Card in JSON format—essentially a capability profile that answers: "What I can do, how to talk to me, what formats I support." When agents meet for the first time, they exchange these cards to understand each other's capabilities.
Tasks: the work units
Agents exchange structured Tasks with clear states like submitted, working, input-required, completed, and failed. These are the atomic units of work, containing rich message types: text, structured data, files, or even multimedia content.
Client & remote architecture
One agent takes the lead (client), while others respond (remote). But critically, all agents stay independent—no shared memory or tight coupling required. This opaque agent design means agents can collaborate without revealing their internal logic, enabling secure cooperation without exposing trade secrets.
Industry adoption for A2A is widespread
[Image from Google]
One of the strongest indicators of A2A's potential impact is its unprecedented industry support. The protocol launched with backing from over 50 technology partners, including:
- Enterprise platforms: SAP, Salesforce, ServiceNow, Workday
- AI frameworks: LangChain, Cohere, Anthropic
- Infrastructure: MongoDB, PayPal, Atlassian, Box
Microsoft has already announced support, with A2A coming to Azure AI Foundry and Copilot Studio. This breadth of support suggests this isn't another "protocol war" but rather industry recognition that interoperability is essential for AI's next growth phase.
The business case for A2A: why CTOs should care
The financial implications extend far beyond avoiding integration headaches. Consider that S&P Global data shows 42% of companies abandoned most AI projects in 2025, often citing cost and unclear value as primary reasons.
A2A directly addresses these concerns:
Dramatic cost reduction: Instead of building custom code for each agent pairing, developers implement A2A universally. For an enterprise with ten AI systems, this could mean avoiding 45 custom integrations.
Operational efficiency at scale: Multiple agents handle distinct workflow steps with reduced manual intervention. Additional agents can be added seamlessly, creating true scalability.
AI innovation acceleration: Businesses can test new agent roles and configurations easily, enabling rapid experimentation with dynamic multi-agent workflows.
Enterprise-grade security: The opaque agent model enables secure collaboration without exposing internal logic, making it perfect for large organizations using agents across HR automations, customer support, finance workflows, and cross-team coordination.
Getting started: A2A implementation reality
[Video from Google]
For technical teams, Google has made adoption straightforward. The company open-sourced an SDK (currently Python, with others coming) that's as simple as pip install a2a-sdk. The GitHub repository includes sample projects like:
- Expense Reimbursement Agent (Google ADK)
- Currency Converter (LangGraph)
- Image Generator (CrewAI)
However, timing considerations matter. A2A is still evolving, with a production-ready version expected later in 2025. This presents both opportunity and risk – early adopters can gain competitive advantages while helping shape the standard's evolution.
A2A bigger picture: era of multi-agent AI systems
If A2A reaches widespread adoption – and all industry signals point to yes – it could be as transformative as OAuth was for authentication, Kubernetes for cloud-native architecture, or REST/HTTP for the web.
PwC predicts that AI agents could easily double knowledge workforces in roles like sales and field support, fundamentally transforming speed to market and customer interactions. Organizations that master agent orchestration through protocols like A2A will have significant advantages in this emerging agentic economy.
By solving agent interoperability at the protocol level, A2A lets companies treat agents as plug-and-play teammates – not one-off, siloed scripts. This turns isolated agents into something far more powerful: a coordinated AI workforce.
More A2A Resources
