Yina Arenas shares how Microsoft’s Agent Factory and Azure AI Foundry use open standards like MCP and A2A to connect agents, enterprise data, and applications for powerful, interoperable AI ecosystems.

Agent Factory: Connecting Agents, Apps, and Data with Open Standards (MCP, A2A)

Author: Yina Arenas
Source: Microsoft Azure Blog

Introduction

The adoption of AI agents is moving beyond isolated prototypes to fully integrated, enterprise-grade solutions. Central to this evolution is the ability for agents to connect with each other, enterprise data, and core business systems. This article discusses how open standards such as Model Context Protocol (MCP) and Agent2Agent (A2A) are powering these transformations, with a focus on Microsoft’s Azure AI Foundry.

The Power of Integration

Connecting Agents: The greatest value in agentic AI emerges when agents coordinate, access organizational data, and interact with business systems. Integration transforms agents from standalone tools into true business accelerators.
Azure AI Foundry in the Enterprise: As adoption grows, customer service agents, research bots, and workflow automation tools are increasingly collaborating via Azure AI Foundry, combining their strengths to deliver outcomes across use cases.

Industry Trends Toward Open Standards

Standard Protocols: Open standards like MCP and A2A establish common languages for sharing tools, data, and context. This promotes interoperability between different vendors, platforms, and frameworks, enabling organizations to avoid vendor lock-in and integrate best-of-breed solutions.
Multi-Agent Collaboration: With protocols such as A2A, specialist agents can interact much as human teams do, each contributing expertise (e.g., scheduling, data querying, summary).
Connected Ecosystems: Modern enterprises expect their agents to operate across diverse platforms (Microsoft 365, Salesforce, ServiceNow, etc.), leveraging integration libraries and connectors to bridge environments.
Framework Interoperability: Developers use various frameworks (LangGraph, Semantic Kernel, CrewAI, etc.). Open standards ensure agents remain compatible, regardless of toolset.

Requirements for Integration at Scale

Collaboration by Design: Workflows require protocols (A2A, MCP) that allow agents using different frameworks to coordinate efficiently.
Shared Context Management: Safe, standardized sharing of tools, context, and results (enabled by MCP) is essential.
Enterprise System Access: Integration fabrics and prebuilt connectors enable agents to act across both modern and legacy systems, streamlining enterprise adoption.
Unified Observability: With agents operating across boundaries, observability tools and open telemetry standards provide traceability, compliance, and governance.

Azure AI Foundry’s Approach

MCP Support: Foundry agents can directly interact with MCP-compatible tools. Developers benefit from reusable connectors and an expanding marketplace of interoperable solutions.
A2A Integration: Through frameworks like Semantic Kernel, Azure AI Foundry implements A2A, allowing complex, multi-agent workflows that span vendor and framework boundaries.
Enterprise Integration Fabric: With thousands of out-of-the-box connectors, Foundry agents can act in a wide range of enterprise contexts. The addition of Logic Apps support for MCP brings even more extensibility.
Unified Observability & Governance: Developers gain end-to-end tracing and enterprises retain control over security, policy, and compliance, even as workflows grow in complexity.

Conclusion

Microsoft’s commitment to open standards positions Azure AI Foundry as a key enabler for organizations seeking to build connected, scalable, and flexible AI agent ecosystems. The use of MCP and A2A offers interoperability, choice, and future-proofing against vendor lock-in. With this approach, enterprises can confidently transition from siloed approaches to fully connected AI-driven workflows.