The actual energy of brokers comes from their skill to attach to one another, to enterprise information, and to the methods the place work will get finished.
This weblog submit is the fifth out of a six-part weblog collection referred to as Agent Manufacturing facility which is able to share finest practices, design patterns, and instruments to assist information you thru adopting and constructing agentic AI.
An agent that may’t speak to different brokers, instruments, and apps is only a silo. The actual energy of brokers comes from their skill to attach to one another, to enterprise information, and to the methods the place work will get finished. Integration is what transforms an agent from a intelligent prototype right into a pressure multiplier throughout a enterprise.
With Azure AI Foundry prospects and companions, we see the shift in all places: customer support brokers collaborating with retrieval brokers to resolve advanced circumstances, analysis brokers chaining collectively throughout datasets to speed up discovery, and enterprise brokers appearing in live performance to automate workflows that when took groups of people. The story of agent growth has moved from “can we construct one?” to “how can we make them work collectively, safely and at scale?”
Trade developments present integration because the unlock
At Microsoft through the years, I’ve seen how open protocols form ecosystems. From OData, which standardized entry to information APIs, to OpenTelemetry, which gave builders widespread floor for observability, open requirements have constantly unlocked innovation and scale throughout industries. Immediately, prospects in Azure AI Foundry are on the lookout for flexibility with out vendor lock-in. The identical sample is now unfolding with AI brokers. Proprietary, closed ecosystems create danger if brokers, instruments, or information can’t interoperate, inflicting innovation to stall and a rise in switching prices.
- Commonplace protocols taking root: Open requirements just like the Mannequin Context Protocol (MCP) and Agent2Agent (A2A) are making a lingua franca for the way brokers share instruments, context, and outcomes throughout distributors. This interoperability is vital for enterprises who need the liberty to decide on best-of-breed options and guarantee their brokers, instruments, and information can work collectively, no matter vendor or framework.
- A2A collaboration on MCP: Specialist brokers more and more collaborate as groups, with one dealing with scheduling, one other querying databases, and one other summarizing. This mirrors human work patterns, the place specialists contribute to shared objectives. Study extra about how this connects to MCP and A2A in our Agent2Agent and MCP weblog.
- Related ecosystems: From Microsoft 365 to Salesforce to ServiceNow, enterprises count on brokers to behave throughout all their apps, not only one platform. Integration libraries and connectors have gotten as vital as fashions themselves. Open requirements be certain that as new platforms and instruments emerge, they are often built-in seamlessly, eliminating the danger of remoted level options.
- Interop throughout frameworks: Builders need the liberty to construct with LangGraph, AutoGen, Semantic Kernel, or CrewAI—and nonetheless have their brokers speak to one another. Framework variety is right here to remain.
What integration at scale requires
From our work with enterprises and open-source communities, an image emerges of what’s wanted to attach brokers, apps, and information:
- Cross-agent collaboration by design: Multi-agent workflows require open protocols that enable totally different runtimes and frameworks to coordinate. Protocols like A2A and MCP are quickly evolving to help richer agent collaboration and integration. A2A expands agent-to-agent collaboration, whereas MCP is rising right into a foundational layer for context sharing, software interoperability, and cross-framework coordination.
- Shared context via open requirements: Brokers want a protected, constant approach to move context, instruments, and outcomes. MCP permits this by making instruments reusable throughout brokers, frameworks, and distributors.
- Seamless enterprise system entry: Enterprise worth solely occurs when brokers can act: replace a CRM report, submit in Groups, or set off an ERP workflow. Integration materials with prebuilt connectors take away the heavy raise. Enterprises can join new and legacy methods with out pricey rewrites or proprietary obstacles.
- Unified observability: As workflows span brokers and apps, tracing and debugging throughout boundaries turns into important. Groups should see the chain of reasoning throughout a number of brokers to make sure security, compliance, and belief. Open telemetry and analysis requirements give enterprises the transparency and management they should function at scale.
How Azure AI Foundry permits integration at scale
Azure AI Foundry was designed for this linked future. It makes brokers interoperable, enterprise prepared, and built-in into the methods the place companies run.
- Mannequin Context Protocol (MCP): Foundry brokers can name MCP-compatible instruments straight, enabling builders to reuse present connectors and unlock a rising market of interoperable instruments. Semantic Kernel additionally helps MCP for pro-code builders.
- A2A help: By way of Semantic Kernel, Foundry implements A2A so brokers can collaborate throughout totally different runtimes and ecosystems. Multi-agent workflows—like a analysis agent coordinating with a compliance agent earlier than drafting a report—simply work.
- Enterprise integration material: Foundry comes with hundreds of connectors into SaaS and enterprise methods. From Dynamics 365 to ServiceNow to customized APIs, brokers can act the place enterprise occurs with out builders rebuilding integrations from scratch. And with Logic Apps now supporting MCP, present workflows and connectors might be leveraged straight inside Foundry brokers.
- Unified observability and governance: Tracing, analysis, and compliance checks prolong throughout multi-agent and multi-system workflows. Builders can debug cross-agent reasoning and enterprises can implement id, coverage, and compliance end-to-end.
Why this issues now
Enterprises don’t need remoted level options—they need linked methods that scale. The following aggressive benefit in AI isn’t simply constructing smarter brokers, it’s constructing linked agent ecosystems that work throughout apps, frameworks, and distributors. Interoperability and open requirements are the muse for this future, giving prospects the flexibleness, alternative, and confidence to spend money on AI with out worry of vendor lock-in.
Azure AI Foundry makes that attainable:
- Versatile protocols (MCP and A2A) for agentic collaboration and interoperability.
- Enterprise connectors for system integration.
- Guardrails and governance for belief at scale.
With these foundations, organizations can transfer from siloed prototypes to actually linked AI ecosystems that span the enterprise.
What’s subsequent
Partially six of the Agent Manufacturing facility collection, we’ll give attention to some of the vital dimensions of agent growth: belief. Constructing highly effective brokers is barely half the problem. Enterprises want to make sure these brokers function with the best requirements of safety, id, and governance.
Did you miss these posts within the collection?
