AI agent integrations connect an AI application to outside tools, services, data, or other agents so it can retrieve information or take actions beyond generating a response. A regular API or HTTP connection often suits a conventional service; MCP standardizes access to tools and resources, while A2A lets agents delegate tasks and collaborate. These approaches can coexist in one application.
What an AI agent integration does
An integration gives an AI application a defined route to something outside its own model response. That might be a calendar, database, search service, calculator, design application, or another agent. The Model Context Protocol (MCP) project describes MCP as “an open-source standard for connecting AI applications to external systems.”
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“Integration” does not describe one universal architecture. A developer might connect a model directly to a function or service, expose capabilities through an MCP server, or have an orchestrator send work to a separate A2A agent. The useful first question is: what is on the other side, and does the application need a discrete capability or another agent’s workflow?
How an integration works
- Discover capabilities: The application or orchestrator learns what the connected service or agent can do.
- Route a request: When a user’s task needs that capability, the application sends the relevant request to the endpoint.
- Perform the work: The endpoint retrieves information, takes an action, or handles a delegated task.
- Return a result: The service or agent sends back information or a response that the calling application can use in the user-facing task.
This is a high-level description, not a claim that every framework uses identical internals. MCP provides standardized access to tools, APIs, and resources. A2A defines a contract for sending tasks to external agents, sharing structured metadata, and receiving predictable responses. See the MCP overview and A2A documentation.
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MCP vs. A2A: what is the difference?
| Decision point | MCP | A2A |
|---|---|---|
| What is on the other side? | A tool, API, data source, resource, or workflow | Another agent, often with its own domain-specific reasoning or workflow |
| Main purpose | Access information or invoke a defined capability | Delegate a task, exchange context, and collaborate across agents |
| Typical examples | Search, database access, calendar access, calculators, or application actions | Cross-framework or cross-vendor agent workflows and task delegation |
| Key security question | Which tools or resources can be reached, and with what identity and permissions? | Which agent is being called, what data is shared, what it may do, and how its work is monitored? |
MCP and A2A are complementary roles, not competing choices. An application can use MCP to equip one agent with tools and A2A to delegate a particular task to another agent. The A2A project describes agent collaboration without requiring agents to share their internal memory, tools, or proprietary logic; that separation does not eliminate the need to govern access and monitor results.
When to use an API, MCP, or A2A
- Use a direct API or HTTP connector when the endpoint is a conventional service and the application only needs a straightforward request and response. Agent-to-agent task exchange may add unnecessary complexity. Microsoft’s Copilot Studio guidance also describes HTTP tools and MCP as integration options.
- Use MCP when the application needs a standardized way to connect to tools, APIs, data sources, or resources. It is a fit for capabilities such as search, calendar access, and database queries.
- Use A2A when the remote component is itself an A2A-capable agent and its independent reasoning or workflow is useful. The caller can delegate a task and receive a response rather than treating the endpoint as a single-purpose tool.
- Combine approaches when different endpoints call for different interaction patterns. Microsoft says multiple integration models can be used within one Copilot Studio agent.
Deployment: what an A2A endpoint needs
A remote agent needs an endpoint that the calling application can reach, along with authentication appropriate to the deployment. Microsoft’s Copilot Studio A2A example uses HTTPS and names Azure App Service or containers as possible hosting options. It presents Dev Tunnels for local development and demonstrations, not production. These are options in one vendor’s example, not universal A2A hosting requirements.
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A protocol defines ways to communicate; it does not by itself make a connection safe or reliable. Before enabling an integration, decide what crosses the boundary, which identities may connect, what each connection is permitted to access or change, and how the work will be monitored. For connected agents, Microsoft’s guidance calls out data handling, permissions, trustworthiness, observability, traceability, and human oversight.
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- Identity and permissions: Verify who is making the request and limit access to the tools, resources, and actions needed for the task.
- Trust and monitoring: Assess the connected system and make its activity and outcomes observable and traceable.
- Human review: Decide which tasks require approval or oversight instead of allowing an agent to proceed autonomously.
Microsoft Copilot Studio channel caveat
Microsoft’s separate MCP and A2A channels documentation was last updated October 1, 2026, and labels the described functionality prerelease, with availability limited to early release cycle environments. In that documented setup, Copilot Studio publishes an HTTPS endpoint and clients authenticate with Microsoft Entra ID on behalf of the signed-in user; access is still checked for that user. Configuration uses an app registration and delegated permissions. These details apply to that Copilot Studio configuration, not to every MCP or A2A implementation.
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