The actual energy of brokers comes from their capability to attach to one another, to enterprise knowledge, and to the programs the place work will get executed.
This weblog submit is the fifth out of a six-part weblog collection referred to as Agent Manufacturing unit 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 discuss to different brokers, instruments, and apps is only a silo. The actual energy of brokers comes from their capability to attach to one another, to enterprise knowledge, and to the programs the place work will get executed. Integration is what transforms an agent from a intelligent prototype right into a power multiplier throughout a enterprise.
With Azure AI Foundry prospects and companions, we see the shift all over the place: customer support brokers collaborating with retrieval brokers to resolve advanced circumstances, analysis brokers chaining collectively throughout datasets to speed up discovery, and enterprise brokers performing in live performance to automate workflows that after 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?”
Business traits present integration because the unlock
At Microsoft over time, I’ve seen how open protocols form ecosystems. From OData, which standardized entry to knowledge APIs, to OpenTelemetry, which gave builders frequent floor for observability, open requirements have persistently unlocked innovation and scale throughout industries. As we speak, prospects in Azure AI Foundry are in search of flexibility with out vendor lock-in. The identical sample is now unfolding with AI brokers. Proprietary, closed ecosystems create danger if brokers, instruments, or knowledge can’t interoperate, inflicting innovation to stall and a rise in switching prices.
- Customary protocols taking root: Open requirements just like the Mannequin Context Protocol (MCP) and Agent2Agent (A2A) are making a lingua franca for a way brokers share instruments, context, and outcomes throughout distributors. This interoperability is essential for enterprises who need the liberty to decide on best-of-breed options and guarantee their brokers, instruments, and knowledge 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 anticipate brokers to behave throughout all their apps, not only one platform. Integration libraries and connectors have gotten as essential as fashions themselves. Open requirements be sure that as new platforms and instruments emerge, they are often built-in seamlessly, eliminating the chance 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 discuss to one another. Framework range 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 knowledge:
- 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 secure, constant option to go context, instruments, and outcomes. MCP allows 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 document, submit in Groups, or set off an ERP workflow. Integration materials with prebuilt connectors take away the heavy carry. Enterprises can join new and legacy programs with out pricey rewrites or proprietary limitations.
- 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 allows integration at scale
Azure AI Foundry was designed for this linked future. It makes brokers interoperable, enterprise prepared, and built-in into the programs 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 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 programs. 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 may be leveraged straight inside Foundry brokers.
- Unified observability and governance: Tracing, analysis, and compliance checks lengthen throughout multi-agent and multi-system workflows. Builders can debug cross-agent reasoning and enterprises can implement identification, coverage, and compliance end-to-end.
Why this issues now
Enterprises don’t need remoted level options—they need linked programs 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 inspiration for this future, giving prospects the flexibleness, alternative, and confidence to put money into 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
Partly six of the Agent Manufacturing unit collection, we’ll give attention to some of the essential dimensions of agent growth: belief. Constructing highly effective brokers is barely half the problem. Enterprises want to make sure these brokers function with the very best requirements of safety, identification, and governance.
Did you miss these posts within the collection?