Both Microsoft and Google have launched major platforms for building and deploying AI agents, but they've created a fundamental problem: each platform only governs AI assets within its own cloud. For enterprises using both Azure and Google Cloud, this means AI agents still lack a shared source of context across platforms, forcing companies to build their own workarounds.

What's the Real Difference Between Microsoft Foundry and Google's Agent Platform?

Microsoft rebranded Azure AI Foundry to Microsoft Foundry in 2025, positioning it as the company's control plane for building and deploying AI applications and agents across Azure and Microsoft 365. Google took a different approach, evolving its Vertex AI platform into the Gemini Enterprise Agent Platform in April 2026, organizing everything around four pillars: build, scale, govern, and optimize.

The platforms share similar capabilities. Both support no-code agents for users who want configuration-only setups, as well as hosted agents where developers bring their own code. Both can access most frontier AI models and support MCP (Model Context Protocol), which allows agents to connect to external tools and data sources. But here's where the similarity ends.

Why Multi-Cloud Enterprises Are Stuck in the Middle?

The critical gap emerges when organizations need to operate across both clouds. Each platform keeps its retrieval-augmented generation (RAG) systems and tool catalogs locked within its own cloud environment. RAG is a technique that lets AI agents pull relevant information from a company's documents or databases before answering questions, making their responses more accurate and grounded in real data.

Neither Microsoft Foundry nor Google's Gemini Enterprise Agent Platform ships with a cross-cloud context layer. This means a multi-cloud enterprise still needs to build its own shared source of context across both platforms. In practical terms, if your company uses Azure for some workloads and Google Cloud for others, your AI agents won't have a unified view of your business data and context.

The platforms also differ in how they organize their core infrastructure. Microsoft Foundry uses a resource hierarchy model, with a top-level Foundry resource handling security and deployments, and Foundry projects serving as isolation boundaries. Google's approach centers agents themselves as the organizing unit, with everything structured around agent capabilities and lifecycle management.

How to Choose Between the Two Platforms

• Existing Cloud Commitment: Start with whichever cloud platform your organization has already standardized on. Microsoft Foundry is the natural fit for enterprises already using Azure and Microsoft 365, while Google's Gemini Enterprise Agent Platform is designed for organizations already on Google Cloud.
• Data Integration Needs: If your critical business data lives in BigQuery, Google's platform offers native integration. If your data is in Azure services like Fabric or Azure AI Search, Microsoft Foundry provides tighter connectivity without extra configuration steps.
• Model Preferences: Both platforms support most frontier models, but Google's platform offers native access to Gemini models with optimized performance, while Microsoft Foundry integrates deeply with OpenAI models and other partners through Azure's ecosystem.
• Agent Development Style: Microsoft Foundry supports multiple agent frameworks like LangGraph and the Microsoft Agent Framework for hosted agents. Google's platform is more framework-agnostic, allowing developers to use Google's open-source Agent Development Kit (ADK) or any other framework of their choice.

For enterprises already standardized on Azure and Microsoft 365, Microsoft Foundry represents the path of least resistance. For organizations on Google Cloud that want native Gemini models and direct BigQuery data integration, Google's Gemini Enterprise Agent Platform is the better fit.

What About Memory and Context Management?

Both platforms handle agent memory differently. Microsoft Foundry supports short-term, long-term, and procedural memory as fully managed services, meaning the platform handles memory storage and retrieval automatically. Google's platform separates session memory (what happens during a single conversation) from cross-session memory (what the agent remembers across multiple conversations), giving developers more explicit control over how agents retain information.

The lack of a unified context layer across clouds remains the elephant in the room. As enterprises deploy more AI agents across multiple cloud providers, the inability to share context, governance policies, and data access rules becomes increasingly painful. Both platforms acknowledge this gap exists, but neither has yet shipped a solution that bridges it.

For now, organizations choosing between Microsoft Foundry and Google's Gemini Enterprise Agent Platform should base their decision on their existing cloud infrastructure and data locations, not on the promise of seamless multi-cloud agent orchestration. That capability, it seems, will have to be built in-house for the foreseeable future.