AI Agent Interoperability: What MCP and A2A Mean for Enterprise Governance

Two new protocols are quietly reshaping how AI agents operate inside enterprise environments — and most governance programs aren’t ready for them.

Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol are emerging standards that make AI agents dramatically more capable: capable of connecting to any tool, calling any API, and coordinating with other agents across platforms — without custom integration work. That’s the promise. The governance challenge is that the same properties that make these protocols powerful also make them difficult to control.

If your organization is deploying agentic AI — or evaluating it — these two protocols deserve your attention now, not after they’ve become your default infrastructure.

Model Context Protocol, originally developed by Anthropic and now gaining broad adoption, standardizes the way AI agents connect to external tools, data sources, and APIs. Think of it as a universal adapter layer: an MCP-compatible agent can call any MCP-compatible tool without requiring a custom integration for each pairing.

Before MCP, connecting an AI agent to a database, a ticketing system, or an internal API required purpose-built glue code for every combination. MCP collapses that complexity. An agent that knows how to speak MCP can plug into an expanding ecosystem of tools — CRMs, code execution environments, file systems, search APIs, communication platforms — with minimal friction.

For development teams, this is transformative. For security architects and enterprise governance leaders, it’s a significant new attack surface.

Agent-to-Agent protocol addresses a different problem: how AI agents communicate with each other. Where MCP governs tool connectivity, A2A governs agent-to-agent coordination.

In multi-agent architectures, an orchestrator agent might delegate subtasks to specialized agents — one for research, one for code generation, one for customer data retrieval. A2A provides a standard communication layer for that delegation, enabling agents built on entirely different platforms and by different vendors to share context, pass instructions, and coordinate without requiring shared infrastructure.

The practical implication: agents built on OpenAI, Anthropic, Google, and proprietary enterprise platforms can — increasingly — talk to each other natively. That makes multi-agent workflows dramatically easier to assemble. It also means enterprise agents are no longer isolated within a single vendor’s ecosystem. They’re participants in a broader, less controlled network of AI actors.

Interoperability standards exist to reduce friction. The governance challenge is that friction, in controlled amounts, is also a control mechanism.

When connecting an agent to a tool required custom integration work, there was an implicit review process — a developer had to build it, someone had to approve the deployment, and the connection was at least visible. MCP removes that friction. Any developer who can configure an MCP server can give an agent access to tools and data sources that the security team may never see.

A2A introduces a parallel challenge at the agent layer. When agents communicate across platforms, the enterprise loses visibility into what’s being shared, what permissions are being implicitly passed from one agent to another, and what decisions are being made at the boundary between systems. An agent operating within your governance perimeter may delegate a task to an external agent operating outside it — and there may be no audit record of what was communicated or authorized.

These aren’t hypothetical risks. They’re structural consequences of how the protocols are designed, and they apply to any organization deploying agentic AI at scale.

Governance programs building for the MCP and A2A reality need to address three distinct layers of control.

MCP Gateway for Tool-Layer Control

Without a centralized MCP gateway, tool connectivity is a developer-level decision. Individual teams configure MCP servers, connect agents to data sources, and iterate — all outside of any central visibility layer. A gateway changes that. It positions the enterprise as the broker of all MCP connections, enabling policy enforcement on which agents can access which tools, under what conditions, and with what logging. It’s the difference between tool access that’s governed and tool access that’s assumed.

Agent Identity and Authorization for A2A Communication

For A2A governance, the foundational requirement is agent identity. When an agent receives a request from another agent, it needs a reliable way to answer: who is this agent, is it authorized to make this request, and what permissions should it inherit? Without an identity and authorization standard for agents, A2A communication operates on implicit trust — and implicit trust at scale is how data ends up in the wrong place.

Enterprise governance programs need to define authorization models that travel with agents as they delegate tasks across system boundaries. That means issuing agent identities, enforcing least-privilege access even in inter-agent workflows, and ensuring that a high-privilege orchestrator cannot inadvertently grant its permissions downstream.

Audit Architecture That Spans Both Protocols

Visibility requires an audit trail that doesn’t stop at the edge of a single system. In complex multi-agent workflows, a single user action can trigger a chain of tool calls (via MCP) and agent delegations (via A2A) that span multiple platforms, data sources, and decision points. If your audit architecture only captures what happens inside one platform, you’re missing most of the story.

The organizations that will be able to demonstrate AI governance compliance — to regulators, auditors, and internal stakeholders — are the ones building audit infrastructure now that spans the full interaction chain

MCP and A2A are not theoretical. They are being adopted in production systems today. GitHub Copilot supports MCP. Claude supports MCP. Google’s A2A specification is already drawing broad vendor commitment. The standards are moving faster than the governance frameworks designed to control them.

Organizations that build governance controls now — gateways, identity standards, audit architecture — will be operating from a position of control when these protocols reach full deployment scale. Organizations that wait will spend the next several years retrofitting governance onto infrastructure that was never designed with it in mind. That’s a harder problem, and a more expensive one.

The pattern is familiar. It’s the same dynamic that played out with cloud adoption, SaaS sprawl, and shadow IT. The technology moves first. Governance catches up later — or it doesn’t.

Airia’s platform is built for the interoperability reality. The MCP Gateway provides centralized control over tool-layer connectivity — giving security and architecture teams visibility and policy enforcement over every MCP connection across the enterprise. Cross-agent governance capabilities extend that control to A2A workflows, with agent identity, authorization enforcement, and audit trails that span multi-agent interaction chains.

These aren’t features bolted onto a single-agent platform. They’re architecture decisions made for organizations deploying AI at the complexity level that MCP and A2A represent.

Governance doesn’t have to lag behind the protocol. But it requires building the right infrastructure before the defaults are set.

Ready to see how Airia governs MCP and A2A in practice? Book a Demo