If you've been watching the agentic AI space closely, you'll have noticed two protocol names surfacing repeatedly in engineering discussions: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent protocol (A2A). Both arrived in quick succession, and the natural instinct — particularly for procurement committees and architecture review boards — is to ask which one to back. That instinct leads to the wrong question. The more useful framing is: what problem does each protocol actually solve, and where does each one sit in your stack?
The reason this matters now, rather than at some future point when the dust settles, is that enterprise AI systems are being architected today. Teams building on agentic frameworks are making structural decisions that will be expensive to unpick in six months. Getting the mental model right early — understanding that MCP and A2A occupy distinct layers with distinct responsibilities — is the difference between a coherent architecture and a tangle of ad hoc integrations.
MCP: The 'Tools Down' Layer
Model Context Protocol was introduced by Anthropic to solve a specific and unglamorous problem: giving AI models a standardised way to reach down into the systems they need to act usefully — APIs, databases, file systems, internal tooling, SaaS platforms. Before MCP, every integration was bespoke. An agent connecting to a CRM, a document store, and a code execution environment required three separate custom connectors, each with its own authentication logic, error handling, and context serialisation. MCP standardises that surface. It defines how a model exposes tools, how those tools are invoked, and how results are returned to the model's context window.
Think of MCP as the wiring between an AI agent and the world of data and services that agent needs to act on. It is deliberately scoped to that vertical relationship — model to resource. It does not concern itself with how multiple agents might coordinate, delegate to one another, or track the progress of a composite task. That is a different problem, and it is the problem A2A exists to solve.
A2A: The 'Agents Across' Layer
Google's Agent-to-Agent protocol addresses coordination between autonomous agents rather than between an agent and a tool. In a multi-agent architecture — which is increasingly the pattern for enterprise AI systems handling complex, multi-step workflows — you will typically have an orchestrator agent that breaks a goal into subtasks, and specialist agents that execute those subtasks. A2A provides the lingua franca for that delegation: how does the orchestrator express what it needs? How does a specialist agent communicate its capabilities? How is task state tracked, progress reported, and failure surfaced?
A2A adoption has moved quickly since its release, partly because the problem it solves is one that any team building non-trivial agentic systems will run into almost immediately. Without a shared coordination protocol, multi-agent systems either become tightly coupled — with hardwired handoffs between specific agent implementations — or they rely on informal conventions that break under operational load. A2A brings structure to the horizontal layer of agent-to-agent communication in the same way that MCP brings structure to the vertical layer of agent-to-tool communication.
Why the Two-Layer Model Matters for Architecture
The practical value of understanding MCP and A2A as distinct layers is that it gives your architecture team a clean separation of concerns. At the tool layer, MCP servers expose capabilities — a database connector, a web search tool, an internal knowledge base. At the coordination layer, A2A handles the routing of work between agents that may each have their own MCP-connected toolsets. An agent acting as a financial data specialist might expose an MCP interface to Bloomberg feeds and internal ledger systems; a separate compliance-checking agent might expose different tools entirely. A2A is what allows an orchestrator to delegate to both, track their outputs, and compose a result — without any of that coordination logic being baked into the individual agents themselves.
For UK organisations operating in regulated sectors — financial services, healthcare, legal — this layering has an additional benefit: it creates natural audit and governance boundaries. MCP interactions (what data was accessed, which tool was called, with what parameters) are separable from A2A interactions (which agent delegated to which, what task was assigned, what was returned). That separation makes it considerably easier to satisfy audit requirements, implement access controls at the right granularity, and debug failures in production. A monolithic integration approach collapses those boundaries and makes compliance reporting significantly harder.
Common Pitfalls to Avoid Right Now
The most common architectural mistake we see in early agentic builds is conflating the two layers — specifically, using MCP to wire agents together rather than using it exclusively for tool access. This works at small scale but creates serious problems as the system grows. MCP was not designed to carry the semantics of task delegation, capability negotiation, or inter-agent state management. Stretching it into that role produces brittle systems that are difficult to extend and nearly impossible to observe in production.
A second pitfall is assuming you need to choose one protocol and exclude the other. Some teams, overwhelmed by the pace of change in the agentic space, are deferring both decisions entirely and building proprietary glue code instead. That approach carries its own risk: as A2A and MCP tooling matures — and it is maturing rapidly — organisations with proprietary coordination layers will find themselves unable to benefit from off-the-shelf agent frameworks, community-maintained MCP servers, or the broader ecosystem forming around both standards.
The practical takeaway for senior decision-makers and technical leads is straightforward: if your team is architecting an enterprise AI system that involves more than a single agent, both protocols belong in your stack — at their respective layers. MCP handles the interface between agents and the tools, data, and services they depend on. A2A handles the interface between agents themselves. Neither is optional in a production-grade multi-agent system, and treating them as alternatives will create architectural debt that compounds quickly.
At iCentric, we are already helping UK organisations design agentic systems with this two-layer model baked in from day one — with clear governance boundaries, observable coordination points, and integrations that are built to extend rather than to be rewritten. If you are at the point of making foundational architecture decisions for an AI system, it is worth having that conversation before the first lines of infrastructure code are committed.
Is MCP an open standard or proprietary to Anthropic's Claude models?
MCP was introduced by Anthropic but is designed as an open protocol, not tied exclusively to Claude. Other model providers and framework developers have begun adopting it, and a growing ecosystem of community-maintained MCP servers exists independently of any single vendor. That said, Anthropic currently leads its development, so organisations should track the governance model as it matures.
Has Google's A2A protocol been adopted beyond Google's own products?
Yes. Since its release in early 2025, A2A has seen adoption from a number of agent framework developers and enterprise AI platform vendors beyond Google's own ecosystem. The speed of adoption suggests it is likely to become a de facto standard for multi-agent coordination, though the specification is still evolving and teams should build with that in mind.
Do we need to use both MCP and A2A, or can a single-agent system get by with just MCP?
A single-agent system — one model, multiple tools — can operate effectively with just MCP handling its tool connections. A2A becomes necessary when you introduce multiple autonomous agents that need to coordinate, delegate tasks, or exchange state. If your current architecture is single-agent but may grow into a multi-agent system, it is worth designing with A2A in mind from the outset to avoid structural refactoring later.
How mature are the tooling ecosystems around MCP and A2A right now?
MCP's ecosystem is somewhat more mature, with a range of open-source MCP servers available for common integrations including databases, web search, and popular SaaS platforms. A2A's tooling is developing rapidly but is earlier stage — production-grade implementations are emerging, but teams should expect to contribute to or extend open tooling rather than rely on fully polished solutions in the near term.
What is the performance overhead of routing agent communication through A2A rather than direct function calls?
A2A introduces some overhead relative to tightly coupled direct calls, primarily through the serialisation and negotiation steps involved in its coordination semantics. In practice, for most enterprise workflows where task latency is measured in seconds rather than milliseconds, this overhead is negligible. For latency-sensitive, high-frequency agent interactions, it is worth profiling your specific use case before committing to the protocol layer.
How should access control and permissions be handled across an MCP and A2A stack?
MCP handles tool-level permissions — defining which tools an agent can invoke and with what parameters. A2A handles agent-level delegation — which agents can assign tasks to which other agents, and with what scope. In practice, a well-governed implementation will define permission boundaries at both layers independently: tool access controls in your MCP server configuration, and delegation policies in your A2A orchestration layer. This separation makes it easier to audit and enforce least-privilege principles.
Can existing LangChain or AutoGen-based agent systems be migrated to use MCP and A2A?
Incrementally, yes. MCP can often be adopted at the tool integration layer without restructuring the broader agent logic — replacing custom tool connectors with MCP-compliant servers. Adopting A2A typically requires more deliberate re-architecture of how agents hand off tasks, but it does not require rebuilding agents from scratch. A phased migration approach, starting with MCP at the tool layer, is usually the most practical path.
Are there UK-specific regulatory considerations that affect how MCP or A2A should be implemented?
The protocols themselves are neutral on regulation, but their implementation choices carry compliance implications. For sectors regulated by the FCA, ICO, or CQC, the key consideration is ensuring that both tool invocations (MCP) and inter-agent task delegations (A2A) are logged with sufficient fidelity for audit purposes. Data residency is also relevant — MCP server deployments that route data through third-party cloud infrastructure need to align with UK GDPR data transfer requirements.
What happens if an A2A-delegated task fails partway through — how is error handling managed?
A2A includes mechanisms for reporting task state, including failure states, back to the orchestrating agent. How a system responds to those failures — retrying, escalating, or gracefully degrading — is an application-level decision that sits above the protocol. Teams should design explicit failure-handling logic into their orchestration layer rather than assuming the protocol will resolve ambiguous failure scenarios automatically.
How do we evaluate whether a vendor's 'MCP-compatible' or 'A2A-compatible' claims are substantive?
Ask vendors to demonstrate interoperability against a reference implementation rather than accepting compatibility claims at face value. For MCP, verify that their server correctly implements tool discovery, invocation, and context return per the published specification. For A2A, test task delegation across agents from different vendors in a sandboxed environment. Compliance with the published spec and willingness to participate in cross-vendor interoperability testing are the clearest signals of substantive compatibility.
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