If you’ve been following the AI agent space — and if you’re building software in 2026, you should be — you’ll have noticed a curious gap. We have brilliant individual agents. We have MCP for connecting agents to tools and data sources. But until recently, we had no standard way for agents to talk to each other.
That’s changing fast. Google’s Agent2Agent (A2A) protocol, now under the Linux Foundation’s stewardship, is rapidly becoming the lingua franca of agent-to-agent communication. With version 0.3 recently released and over 50 major technology partners on board — including Salesforce, SAP, Anthropic, and OpenAI — this isn’t a speculative technology. It’s infrastructure that’s being built right now.
TL;DR
- The A2A protocol enables AI agents built on different frameworks and by different vendors to communicate, collaborate, and coordinate tasks — as agents, not just tools.
- MCP connects agents to tools; A2A connects agents to other agents. You’ll likely need both.
- Version 0.3 introduces gRPC support, signed security cards, and extended Python SDK support — making it production-ready for enterprise use.
- JetBrains Central, launched March 2026, is the first major platform to build orchestration around multi-agent interoperability as a core concern.
- Development teams should start thinking about agent communication strategy now, before vendor lock-in sets in.
MCP and A2A: Complementary, Not Competing
There’s a common misconception that A2A competes with Anthropic’s Model Context Protocol (MCP). It doesn’t. Think of it this way: MCP is how an agent connects to your database, your CRM, your file system — it’s the agent-to-tool layer. A2A is how an agent connects to another agent — it’s the agent-to-agent layer.
In practice, you need both. A customer support agent might use MCP to pull order data from your backend, then use A2A to hand off a complex billing dispute to a specialised finance agent that lives in a completely different system. Neither protocol alone handles this workflow.
Google’s own developer documentation now explicitly frames these as complementary layers in what they call the “agent protocol stack.” If your team has already invested in MCP integrations, A2A doesn’t replace that work — it extends it.
How A2A Actually Works
At its core, A2A solves four problems that every multi-agent system eventually runs into:
1. Capability Discovery. Before agents can collaborate, they need to know what each other can do. A2A introduces “Agent Cards” — JSON documents that describe an agent’s capabilities, accepted input formats, and authentication requirements. Think of them as API documentation, but for agents. Any agent can query another’s Agent Card to decide whether collaboration makes sense.
2. Task Management. A2A defines a task lifecycle with clear states: submitted, working, completed, failed. This might seem basic, but without a shared vocabulary for task states, inter-agent workflows devolve into polling, timeouts, and brittle error handling. The protocol handles this at the infrastructure level.
3. Context Sharing. When one agent delegates work to another, it needs to pass context — not just raw data, but instructions, constraints, and priorities. A2A’s message format supports structured context sharing that goes beyond simple text prompts.
4. Security and Trust. Version 0.3’s signed security cards are significant. Agents can now cryptographically verify each other’s identity and capabilities, which is non-negotiable for enterprise deployments where agents handle sensitive data across organisational boundaries.
Why This Matters Now: The JetBrains Central Signal
The strongest signal that agent interoperability has moved from “interesting research” to “production concern” came on 24 March 2026, when JetBrains launched Central — an open platform for orchestrating multi-agent software teams.
JetBrains Central isn’t just another AI coding tool. It’s an orchestration layer that treats agent management as a first-class engineering concern, with governance, cost attribution, identity management, and execution infrastructure built in. Critically, it supports agents from multiple providers — JetBrains, Anthropic, OpenAI, and Google — which only works if those agents can communicate through standardised protocols.
When a company like JetBrains, with its massive developer ecosystem, bets this heavily on multi-agent orchestration, it tells you where enterprise software development is heading. The question isn’t whether you’ll have multiple AI agents in your stack. It’s whether they’ll be able to work together when you do.
The Practical Implications for Development Teams
So what should you actually do about this? Here’s where it gets concrete.
Audit your current agent landscape. Most organisations we work with at REPTILEHAUS already have three to five AI agents or tools in play — coding assistants, CI/CD bots, customer-facing chatbots, internal knowledge bases. Map them. Understand which ones would benefit from being able to communicate with each other.
Avoid premature lock-in. If you’re building custom agents, design them with interoperability in mind from day one. Implement Agent Cards. Use standard task lifecycle states. The cost of retrofitting interoperability is significantly higher than building it in.
Think about agent identity and access control. As agents start talking to each other, your existing IAM (Identity and Access Management) strategy needs to extend to non-human actors. Which agents can talk to which? What data can they share? A2A’s security model gives you the building blocks, but you need to define the policies.
Start with a bounded use case. Don’t try to wire everything together at once. Pick one workflow where two agents currently operate in silos — say, a code review agent and a deployment agent — and build a proof of concept using A2A. Learn the protocol’s strengths and limitations in a low-risk environment.
Budget for agent operations. JetBrains isn’t the only one talking about FinOps for AI agents. When agents collaborate, costs multiply. You need visibility into which agents are calling which, how often, and at what cost. This is a new operational concern that most teams haven’t accounted for.
What’s Coming Next
The Linux Foundation’s adoption of A2A signals that this is becoming a true open standard, not a Google-controlled project. Expect rapid iteration through 2026, with enterprise SDKs in Java and Go joining the existing Python support, and deeper integration with cloud platforms.
The bigger picture is that we’re watching the emergence of an “agent internet” — a networked layer where AI agents discover, negotiate with, and delegate to each other, much like microservices do today. The teams that understand this architectural shift early will have a significant advantage.
Need Help Navigating the Agent Stack?
At REPTILEHAUS, we’re actively building AI agent systems for clients — from MCP integrations to multi-agent workflows and production deployment strategies. Whether you’re planning your first agent implementation or trying to bring order to an existing sprawl of AI tools, our team can help you build it right from the start. Get in touch.
📷 Photo by Steve Johnson on Unsplash
