Is Microsoft Foundry the Future of Enterprise AI?
After reviewing several recent Microsoft Foundry presentations, one theme stood out: The future of AI isn't about having the best model. It's about connecting models to business context, systems, governance, and observability. The slides consistently highlight the challenges organizations face: Choosing and managing models Connecting AI to enterprise knowledge Integrating with business systems Governing agents at scale Microsoft's answer appears to be Foundry, an AI app and agent factory that brings together models, knowledge, tools, orchestration, security, and lifecycle management into a single platform. What I found most interesting is the emphasis on Foundry IQ and business context. The message seems to be that AI becomes truly valuable when it understands not just language, but how your organization operates. As AI evolves from chatbots → copilots → agents, governance and context may become more important than raw model intelligence.
Making Azure AI Foundry Agents Explainable — Knowledge Graphs + Source Attribution
90% of Azure AI demos work on stage — most never ship. The gap is architecture, not the model. I wrote up a field guide on taking Azure AI Foundry agents from POC to production, with knowledge graphs (GraphRAG) doing the grounding and source attribution. What the post covers Grounding with a knowledge graph — multi-hop retrieval + inline citations so every answer is traceable to a source (critical for regulated/medical use cases). Externalized state — keeping agent memory and session state outside the model. Identity at the boundary — Entra ID / RBAC instead of trusting the prompt. Observability & compliance — tracing, evaluation, and auditability. The stack — Azure AI Foundry + Model Context Protocol (MCP) + Azure Functions (Flex Consumption) + Azure OpenAI, from a real build (VeritasGraph medical MCP server). 📖 Full write-up: https://bibinprathap.com/blog/azure-ai-proof-of-concept-to-production ▶️ 6-min walkthrough: https://youtu.be/z-CPS5WUvyw?si=fmpo1RN28Bh9KbIb Questions for the community How are you grounding Foundry agents today — vector RAG, GraphRAG, or hybrid? Anyone combining knowledge graphs with MCP tools in Foundry? What worked / broke? For regulated domains, how are you handling source attribution and audit trails? Curious to compare notes — happy to share more detail on the GraphRAG retrieval design if useful.
Azure AI Foundry Agent Unable to Use Credentials Stored in Key Vault Through Playwright MCP Tool
Hello everyone, I am trying to understand how Azure AI Foundry agents interact with Azure Key Vault when using custom MCP tools, and I would appreciate any guidance from the community. My Setup - Created an Azure AI Foundry agent. - Created an Azure Key Vault and configured all permissions according to Microsoft's official documentation. - Stored the required website credentials (username and password) in the Key Vault. - Deployed the official Playwright MCP Docker image. - Exposed the MCP server using ngrok and verified that the endpoint is accessible. - Connected the MCP endpoint as a Custom MCP Tool in Azure AI Foundry. - Performed all configuration through the Azure portal, Foundry UI, and Playground only (no SDK or custom application code involved). The Issue The agent can access and use the Playwright MCP tool. However, when I ask it to log in to a website using credentials that are already stored in Key Vault, it does not populate the username and password fields. My expectation was that the agent would be able to retrieve the secrets from Key Vault and provide them to the Playwright tool during execution. Questions Is there currently a supported mechanism for Azure AI Foundry agents to automatically retrieve Key Vault secrets and pass them to a Custom MCP tool? Does the Playwright MCP Docker image have any built-in integration with Azure Key Vault? When using only the Foundry UI (without SDK code), can a Foundry agent securely inject Key Vault secrets into MCP tool calls? Are additional configurations required beyond Key Vault permissions and agent connections? Has anyone successfully implemented a similar setup where a Foundry agent uses credentials stored in Key Vault to perform browser automation through Playwright MCP? Any clarification on the expected architecture and whether this scenario is currently supported in Azure AI Foundry would be greatly appreciated. Thank you.
Data Visualisation / Charting in Azure Foundry
Hi Foundry community, We are working on an agent that can query internal data sources, and are looking for ways that we can visualise data (think pie charts, bar charts, etc.). This would be consumed by end users through Copilot/Teams. However we are unable to find a way to do so, which is surprising given that you easily can create charts through M365 Copilot Chat and through Copilot Studio. We have tried using the 'Code Interpreter' tool, but the Teams/Copilot client UIs just do not render the results inline, either interactive or as an embedded image. They also do not give any option to download them. Has anyone tackled this before? How have you been able generate charts? Many thanks!
o3-deep-research is failed with the status incomplete with the reason as content filter
I working on an to do an deep research on internal data. I'm using currently the Azure OpenAI Responses API with MCP Tool. The underlying MCP server deployed into ACA with search and fetch tool with signatures in complaint with the specification (https://developers.openai.com/apps-sdk/build/mcp-server#company-knowledge-compatibility). OpenAI client created with 03-deep-research model with MCP tool, in a loop response status being checked. (https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/deep-research#remote-mcp-server-with-deep-research) Deep Research is being carried out for sometime, I could see in the log that handshake has been made, ListTools invoked, search tool is called post that fetch is called for the queries framed by the model.. But intermittently, the response status is becoming "incomplete" with incomplete reason as "content_filter". Otherwise the deep research is working fine. Not able identify the root cause as there is seems to be no way to identify what caused the content filtration whether its the prompt or completion. How to debug and check the root cause and rectify this ? Or is there known issue with the o3-deep-research model's intermediate reasoning completions Or search and fetch tool results are causing this ? I had uploaded a file made it available to MCP server, the search and fetch tool uses an Azure OpenAI agent to search the data using File Search and fetch tool gets the content of the file based on the id passed. For same file and same research topic the issue is not occurring always but intermittently.
GPT-5.5-Pro not listed in foundry?
The model is mentioned in this blog post : https://azure.microsoft.com/en-us/blog/openais-gpt-5-5-in-microsoft-foundry-frontier-intelligence-on-an-enterprise-ready-platform/ But it is currently not listed on Foundry. Only latest pro model is 5.4-pro. When will 5.5-pro model be available on azure foundry?Foundry Agent deployed to Copilot/Teams Can't Display Images Generated via Code Interpreter
Hello everyone, I’ve been developing an agent in the new Microsoft Foundry and enabled the Code Interpreter tool for it. In Agent Playground, I can successfully start a new chat and have the agent generate a chart/image using Code Interpreter. This works as expected in both the old and new Foundry experiences. However, after publishing the agent to Copilot/Teams for my organization, the same prompt that works in Agent Playground does not function properly. The agent appears to execute the code, but the image is not accessible in Teams. When reviewing the agent traces (via the Traces tab in Foundry), I can see that the agent generates a link to the image in the Code Interpreter sandbox environment, for example: `[Download the bar chart](sandbox:/mnt/data/bar_chart.png)` This works correctly within Foundry, but the sandbox path is not accessible from Teams, so the link fails there. Is there an officially supported way to surface Code Interpreter–generated files/images when the agent is deployed to Copilot/Teams, or is the recommended approach perhaps to implement a custom tool that uploads generated files to an external storage location (e.g., SharePoint, Blob Storage, or another file hosting service) and returns a publicly accessible link instead? I've been having trouble finding anything about this online. Any guidance would be greatly appreciated. Thank you!
Unable to Connect Localhost MCP Server from Azure AI Foundry Hosted Agent (o4-mini)
I'm using the Azure AI Foundry Toolkit in VS Code and have configured an MCP server running on my local machine (localhost). When I run my Azure AI Foundry-hosted agent (o4-mini), it fails to connect to the MCP server. Based on the error logs, it appears that the hosted agent cannot reach the localhost endpoint. My understanding is that the MCP server is running correctly locally, but the hosted agent seems unable to access services running on my machine. Has anyone successfully connected a locally hosted MCP server to an Azure AI Foundry-hosted agent while using the Foundry Toolkit in VS Code?Failed to add tool to agent - Preview Feature Required?
Hi, We’ve recently run into an issue where we’re no longer able to add tools to our Foundry agent. This was previously working without problems in our development environment, but now every attempt results in the following error: “Failed to add tool to agent Request failed with status code 403.” After inspecting the request in the browser’s developer console, we noticed an additional message: "This operation requires the following opt-in preview feature(s): AgentEndpoints=V1Preview. Include the 'Foundry-Features: AgentEndpoints=V1Preview' header in your request." How can we opt in for this foundry preview feature? and when was this change introduced? We are unsure if the issue is related the the preview feature missing, or some other forbidden issue. Any help would be very much appreciated. Kind regards, ArneNeed Guidance on cost breakdown of Microsoft Foundry Agent portal I created
I have developed a complaint handling portal for customers and employees using Azure AI Foundry. The solution is built with Foundry agents, models from the catalog, input/output caching, agent logging/tracing, and other Foundry capabilities. The frontend and orchestration layer are deployed on Azure Container Apps. While Azure Cost Analysis provides an overview of spending, several parts remain unclear or act as a black box for accurate estimation, including: Token consumption assumptions (input/output tokens across different models and agents) User concurrency, sessions, and behavior patterns Agent logging and observability costs Impact of input/output caching Detailed resource consumption and billing in Azure Container Apps What is the best way to accurately calculate or estimate the total running cost for such an Azure AI Foundry-based platform with Container Apps frontend? Are there official Microsoft documentation, pricing guides, or reference architectures for cost breakdown? How do companies typically present costs for such AI platforms to attract customers (e.g., TCO models or per-user pricing)? I want to know how the platform costs are shown to customers. Thank you.
