When Agent-to-Agent Interoperability Helps and When It Just Adds Complexity

A2A becomes valuable when independent agents really need to collaborate across boundaries. It becomes expensive when teams use it to postpone simpler workflow and governance decisions.

A lot of technical leaders are hearing a more ambitious pitch: not just better agents, but interoperable agents. Agents that can discover each other, delegate tasks, collaborate securely, and work across platforms.

That sounds like the next logical step. Sometimes it is. But sometimes, it's just a more sophisticated way to add complexity too early.

Google and the A2A project describe Agent2Agent as an open protocol for communication and interoperability between independent agentic systems. The protocol is designed so agents can discover capabilities, negotiate interaction modalities, and collaborate on long-running tasks without exposing internal state, memory, or tools. While Google Cloud documents how to host A2A agents on Cloud Run and Gemini Enterprise allows admins to register them, the Gemini feature is still in Preview (Google Cloud Documentation).

This makes A2A important, but not automatically urgent.

The practical question in 2026 is not “Should we support agent interoperability?” The better question is: “Do we have a real coordination problem between independent agent systems that justifies another protocol layer, another security surface, and another operating model?” This matters even more because the Model Context Protocol (MCP) is also maturing quickly, with a clear roadmap focused on standardizing tool and context access. Many teams are still solving a context problem, not an interoperability problem—and those are not the same thing (OpenAI GitHub).

A2A and MCP solve different problems

This is the first thing technical leaders need to get clear.

MCP is about standardizing how applications provide tools and context to models. OpenAI’s current Agents SDK supports hosted MCP tools, Streamable HTTP MCP servers, and stdio MCP servers, and it explicitly says SSE is deprecated for new integrations. In other words, MCP is becoming the standard context and tool-access layer (OpenAI GitHub).

A2A is different. Its goal is not to expose tools to one model. Its goal is to let separate agents communicate and collaborate as peers, even when they are built on different frameworks, by different vendors, or on separate servers. Google Cloud’s A2A overview and the A2A project documentation both make that clear (Google Cloud Documentation).

That distinction matters because many teams hear “interoperability” and assume they need A2A now.

Often they do not.

If the problem is still “how does this agent access tools, data, or systems,” MCP is usually closer to the right answer. If the problem is “how do these separate agents coordinate with each other across system boundaries,” then A2A starts to make sense (OpenAI GitHub).

When A2A genuinely helps

1. When independent agents need to coordinate across real boundaries

A2A is useful when you already have multiple independent agents or agentic applications that need to collaborate without collapsing into one monolithic orchestrator. The A2A project describes this clearly: the protocol exists to let opaque agentic applications communicate and collaborate without exposing their internal state, memory, or tools. That is a real need when systems are owned by different teams, vendors, or runtime environments (GitHub).

This is especially relevant when:

• Different business units own different agents
• Different vendors or frameworks are already in production
• One agent needs to delegate a job to another agent rather than call a simple tool
• The systems should remain separate for governance or organizational reasons

That is a real interoperability problem, not just a nicer integration story (GitHub).

2. When long-running, multi-step collaboration is the real workload

A2A is stronger when the work is not a one-shot tool call. The protocol is specifically described around collaborative tasks, long-running jobs, and negotiated modalities. That means it is better suited to agent-to-agent coordination patterns than to simple “fetch this document” or “run this command” cases (GitHub).

If your environment has one agent that gathers requirements, another that checks policy, and another that executes a specialized downstream step, interoperability can become more valuable than adding one more tool to one agent. That is where A2A starts to move from interesting to useful (GitHub).

3. When organizational separation matters as much as technical separation

A2A helps when the architecture needs to preserve boundaries. Google Cloud’s A2A documentation emphasizes that agents can work together as peers without exposing their internal logic. That is not just a technical feature. It is an operating model choice. It allows one team or vendor to maintain ownership of an agent while still letting another system collaborate with it (Google Cloud Documentation).

This can matter when:

• Procurement boundaries separate systems
• Internal platform teams need to preserve ownership
• Partner ecosystems matter
• Regulated or sensitive workflows require separation of responsibility

In those cases, interoperability can be cleaner than forcing all logic into one platform (Google Cloud Documentation).

4. When you already know a single control plane is not enough

If your team has already reached the point where one orchestration layer cannot realistically own all the work, A2A becomes more compelling. Google’s A2A positioning is explicitly about moving from isolated agents to interconnected ecosystems. That is not a day-one architecture. It is what becomes relevant after agent systems start to specialize (Google Cloud).

In other words, A2A helps after specialization becomes real. Not before.

When A2A just adds complexity

1. When the real problem is still tool access, not agent collaboration

This is the biggest source of confusion.

If your team is still figuring out how one agent accesses repos, tickets, documentation, databases, or internal APIs, that is usually an MCP or workflow-design problem, not an A2A problem. OpenAI’s MCP documentation is already rich enough to show how much can be solved through tool access, approval flow, filtering, and transport choice before agent-to-agent coordination becomes necessary (OpenAI GitHub).

A2A adds a coordination layer. If the simpler problem is not solved yet, adding that layer usually makes the architecture more impressive without making it more effective (OpenAI GitHub).

2. When teams have not standardized one governed workflow yet

If your team cannot clearly explain:

• What the agent is allowed to do
• What requires approval
• How review happens
• What context is exposed
• Who owns the workflow

then it is not ready to standardize interoperability.

This is an inference, but it is strongly grounded in the current product landscape. MCP itself is prioritizing governance maturation and enterprise readiness. Gemini Enterprise A2A registration is still Preview. These are signals that the ecosystem is still working through the operational discipline required for broader production use (Model Context Protocol).

3. When preview-stage enterprise support is being mistaken for operational maturity

This one matters.

Gemini Enterprise lets admins register A2A agents, but the documentation clearly marks the feature as Preview and states that model armor does not protect conversations with registered A2A agents in the Gemini Enterprise web app. That does not make A2A unusable. It does mean technical leaders should not confuse ecosystem momentum with finished enterprise readiness (Google Cloud).

If your rollout depends on protections or governance assumptions that the preview surface does not yet guarantee, standardizing too early can create future rework (Google Cloud).

4. When the architecture is trying to solve politics with protocols

This is a subtle but common failure mode.

Sometimes teams reach for interoperability because different groups cannot agree on one platform, one workflow, or one owner. A2A can help with genuine boundary-preserving collaboration. It cannot fix unclear ownership, weak standards, or missing review design. If those problems are still unresolved, interoperability often becomes a protocol-shaped workaround for a management problem (GitHub).

The real decision is about coordination maturity

The best question to ask is not “Is A2A important?”

It is.

The better question is “What level of coordination maturity are we at?”

You are probably not ready to standardize A2A yet if:

• You are still choosing the primary control plane
• You have not standardized review and approval
• Your context layer is still immature
• MCP would solve most of the actual problem
• Interoperability demand is hypothetical, not real

You may be ready to evaluate A2A seriously if:

• Multiple independent agents already exist
• They are owned by different teams, vendors, or systems
• Long-running collaboration across boundaries is a real use case
• One orchestrator is no longer an accurate model of the work
• Governance and review are already stronger than the protocol layer itself

That is the line between architectural fit and premature complexity (GitHub).

A practical decision lens for technical leaders

Here is the framework I would use.

Step 1: classify the real problem

Is this about:

• Tool access
• Context sharing
• Workflow review
• Agent coordination
• Cross-boundary delegation

If it is the first three, A2A is probably too early. If it is the last two, it may be worth evaluating (OpenAI GitHub).

Step 2: ask whether the agents are truly independent

If one team owns everything and one orchestrator could reasonably manage it, interoperability may be unnecessary. If the systems are truly separate and should remain separate, A2A becomes more plausible (GitHub).

Step 3: check governance before protocol

Do not standardize interoperability before you standardize:

• Review
• Approval
• Context boundaries
• Ownership
• Escalation paths

Preview-stage platform support and evolving roadmap signals make this even more important in 2026 (Google Cloud).

Step 4: prefer the smallest working architecture

If MCP plus one orchestrator solves the real problem, do that first. Only add A2A when the architecture genuinely needs peer-to-peer agent collaboration across boundaries (OpenAI GitHub).

My take

Agent-to-agent interoperability is real.

It is also very easy to romanticize.

The strongest case for A2A is not “the future is multi-agent.” That is too vague. The strongest case is much more practical: independent agents, owned in different places, need to collaborate on long-running work without collapsing into one brittle control plane. That is when interoperability earns its keep (GitHub).

For most teams in 2026, though, the more urgent work is still closer to home:

• Define the workflow
• Standardize review
• Control context access
• Design the primary lane
• Decide whether MCP belongs in the stack

A2A becomes more useful after those questions are answered, not before (OpenAI GitHub).

Key takeaways

A2A helps when independent agent systems really need to collaborate across organizational, platform, or runtime boundaries, especially for long-running work where preserving separation matters. Google Cloud’s A2A documentation and the A2A project both make that role clear (Google Cloud Documentation).

A2A adds complexity when teams are still solving simpler problems like tool access, workflow design, review logic, and context boundaries. In those cases, MCP or a clearer internal operating model is usually the better next move. Preview-stage enterprise support and explicit protection gaps in Gemini Enterprise make the timing question even more important (OpenAI GitHub).

Further Reading

• MCP in 2026: The Context Layer for Technical Leaders
• What an AI Architecture Review Should Cover Before You Scale
• AI Development Operations Is a Management Problem
• What CTOs Should Standardize First in the AI Dev Stack

From Assessment to Operating Model

If you need a structured way to decide whether your team is ready for interoperability or should strengthen the stack first, start with our AI Readiness Assessment.

If the issue is broader and you need help designing the operating model behind agents, protocols, and workflow coordination, see our AI Consulting services.

And if you want the broader framing behind why this is now an AI development operations problem rather than a protocol-shopping exercise, explore our work in AI Development Operations.