Every AI agent needs two kinds of connections: to the tools and data it acts on, and to other agents it needs to coordinate with. Open protocols for both are emerging quickly enough that architecture decisions made without them in mind are already starting to look short-sighted.
Before standardized protocols, connecting an AI agent to a tool, database, or another agent meant a…
MCP standardizes how an AI agent discovers and calls external tools and data sources — a…
A2A addresses a different problem — how agents built on different frameworks or by different…
Before standardized protocols, connecting an AI agent to a tool, database, or another agent meant a bespoke integration — one more custom connector for every tool-agent or agent-agent pairing, an M×N integration problem that scales badly as both the number of agents and the number of tools grow. Open protocols turn that into an M+N problem: a tool or agent implements the protocol once, and anything else speaking the same protocol can connect to it without a custom integration.
MCP standardizes how an AI agent discovers and calls external tools and data sources — a database, a file system, a SaaS API — through a common interface, rather than each agent framework needing its own bespoke connector per tool. For enterprise architecture, this means a tool or internal system can expose an MCP-compatible interface once and be usable by any MCP-compatible agent, instead of building and maintaining separate integrations per AI tool or vendor.
A2A addresses a different problem — how agents built on different frameworks or by different vendors discover each other’s capabilities and coordinate on a task, without requiring every pair of agents to share a common framework. This matters architecturally for any organization that expects to run agents from multiple vendors or build teams, rather than standardizing on a single agent framework for everything, which is already the more common reality than a single-vendor agent stack.
If you’re building more than one or two agent integrations, evaluating protocol support now avoids accumulating bespoke integrations that become migration debt later. A single, simple agent project can reasonably defer the decision, but anything expected to scale benefits from starting with the standard interface.
They’re complementary, not competing — MCP addresses agent-to-tool/data connections, A2A addresses agent-to-agent coordination. A mature agentic AI architecture generally needs both, not a choice between them.
It creates a new interface that needs the same governance as any API — access control, authentication, and audit logging on what an MCP server exposes and who can call it. The protocol standardizes the connection mechanism; it doesn’t handle authorization and governance on your behalf.