MCP and A2A in Practice: Building Interoperable Agent Ecosystems Across Vendors

Every major shift in enterprise software eventually runs into the same problem: the first wave of solutions are isolated islands, and the technology only becomes truly transformative once a common protocol lets those islands talk to each other. The web needed HTTP. Enterprise applications needed standardized APIs. Agentic AI is now going through exactly this transition, and two emerging standards — the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol — are at the center of it.

If you’ve been building agents on a single framework or platform, these standards might feel abstract. The moment you need an agent built on one platform to use a tool built for another, or an agent from one vendor to coordinate with an agent from a different vendor, they become the difference between a connected ecosystem and a pile of incompatible silos.

The Problem These Standards Actually Solve

Before MCP, if you wanted an agent to use an external tool or data source — a database, a search API, an internal company system — you typically wrote custom integration code specific to that exact combination of agent framework and tool. Every new tool meant new integration work. Every new agent framework meant redoing that integration work again, even for tools you’d already connected elsewhere. This is the same “N times M” integration problem that plagued enterprise software before standardized APIs became the norm — if you have ten agent frameworks and twenty tools, you potentially need two hundred custom integrations rather than thirty standardized ones.

A2A solves a related but distinct problem: once you have multiple agents — possibly built on different frameworks, by different teams, or even by different vendor organizations entirely — how do they discover each other, understand what each one is capable of, and exchange the information needed to collaborate on a task, without every pair of agents requiring custom-built coordination logic?

How MCP Works, Conceptually

MCP standardizes the connection between an AI agent and the external tools or data sources it needs to use. Think of it as a universal adapter: a tool or data source exposes its capabilities through an MCP “server,” and any MCP-compatible agent — regardless of which underlying model or framework it’s built on — can connect to that server and use its capabilities without custom, one-off integration code.

In practice, this means an organization can build a single MCP server exposing access to, say, its customer data system, and that one piece of integration work becomes usable by every MCP-compatible agent the organization builds afterward — whether that agent is built on one framework today or a different one next year. This is a meaningful shift from the previous norm of bespoke, framework-specific integrations that had to be rebuilt every time the underlying tooling changed.

The standard has seen rapid, broad adoption since its introduction, with major model providers and platform vendors converging on it as a shared way to expose tools and data to agents — to the point that it’s increasingly treated as a default expectation for any new agent-facing tool or data integration, rather than one option among several.

How A2A Works, Conceptually

Where MCP connects an agent to tools and data, A2A connects agents to other agents. The protocol defines a standardized way for an agent to publish what it’s capable of doing, for other agents to discover and understand those capabilities, and for agents to exchange the structured information needed to collaborate on a multi-step task — including handing off partial work, requesting clarification, and reporting results — without requiring custom point-to-point integration between every specific pair of agents that might need to interact.

This matters enormously as organizations move from “we built one agent” to “we have a growing portfolio of agents built by different teams, on different platforms, for different purposes” — which is precisely the trajectory most large enterprises, including banks, are now on. Without a shared coordination protocol, that portfolio risks becoming an unmanageable tangle of custom integrations between every pair of agents that ever need to work together. A2A, alongside related industry stewardship efforts that have moved control of the protocol toward open, vendor-neutral governance, is aimed squarely at preventing that outcome.

A Concrete Example Combining Both

Picture a bank with a loan origination agent (built on one platform) and a fraud-screening agent (built on a different platform, by a different team, possibly using a different underlying model). The loan agent, partway through processing an application, needs the fraud-screening agent’s assessment before it can proceed.

With A2A, the loan agent can discover that a fraud-screening agent exists, understand what input it needs and what output it provides, and request an assessment — receiving a structured response it can act on — without the two teams having had to build custom, bilateral integration code specifically for this one connection. Meanwhile, both agents independently use MCP to connect to the specific tools and data sources each of them individually needs — the loan agent to the loan origination system, the fraud agent to transaction history and device intelligence feeds — without either team needing to build that tool integration from scratch either.

The combination is what makes a genuinely modular, composable agent ecosystem possible: new agents and new tools can be added over time, each speaking a shared protocol, rather than every new addition requiring a fresh round of custom integration work across the entire existing landscape.

Why This Matters Especially for Regulated Industries

For banks specifically, this has a governance dimension worth calling out directly. As agent ecosystems grow, the question of “which agents are allowed to talk to which other agents, and which tools is any given agent allowed to access” becomes a serious security and compliance question, not just an engineering convenience question. Standardized protocols create a natural place to enforce that governance consistently — permission and access control can be implemented at the protocol layer itself, applied uniformly across every agent and tool that speaks the standard, rather than re-implemented inconsistently inside every individual integration.

This also directly affects vendor risk management. A bank evaluating any agentic AI vendor in 2026 increasingly has good reason to ask specifically whether that vendor’s platform supports these open standards, or instead locks agents and tools into a closed, proprietary integration approach — because the latter has real long-term cost and flexibility implications, as discussed in the platform comparison post earlier in this series.

Practical Implications for Architects Building Today

A few concrete recommendations follow from understanding these standards:

Default to MCP for new tool integrations, even if your current agent framework doesn’t strictly require it. Building tool access this way from the start means that integration work remains usable as your agent landscape inevitably evolves, rather than becoming throwaway code tied to today’s specific framework choice.

Design your agent capability descriptions deliberately, anticipating that other agents — possibly built by other teams, possibly in the future — may need to discover and use them through A2A. A clearly documented, well-scoped agent capability is far more reusable across the organization than one whose purpose and interface are only understood by the team that originally built it.

Build governance into the protocol layer, not just into individual agents. Where your platform supports it, enforce access permissions and audit logging at the MCP/A2A connection layer itself, so that governance is consistent across your entire agent ecosystem rather than dependent on every individual agent implementing it correctly and consistently on its own.

Track this space actively. Both standards are evolving quickly, with new capabilities, security extensions, and governance frameworks being actively developed by the broader industry. What’s accurate about their exact capabilities today is likely to expand within the next year, and staying current is a genuine, ongoing part of the architect’s job in this space right now.

Coming Up Next

We’ve now covered how agents talk to tools and to each other. The next post shifts to a different but related architectural question: when should you reach for a small, specialized language model instead of a large, general-purpose one — a decision with real implications for cost, latency, and even data governance.