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Code Interpreter Container Failing (Timeout) on Create EastUS2
Hello, we've been using code interpreter reliably for over a year, but starting on 2/21/2026, containers created in our EastUS2 Foundry instance will intermittently fail. It is easy to reproduce this, by running the below cURL. Try to run it a few times, and it will succeed the first 1-4 times, and then you will hit the timeout. Sometimes the timeout occurs on the second create, sometimes on the 3rd, 4th or 5th. During the timeout, if any other requests to create a container are made, they also hang. This impacts all users if code interpreter is set to auto container creation, with the tool enabled, during normal chat. For now we've redeployed resources in US West and do not get the error but do not have quota on more advanced models there so need this resolved ASAP. curl -X POST "https://[redacted].cognitiveservices.azure.com/openai/v1/containers" \ -H "Content-Type: application/json" \ -H "api-key: [redacted]" \ -d '{ "name": "test-container-eastus2-repro", "expires_after": { "anchor": "last_active_at", "minutes": 20 } }'
Title: Synthetic Dataset Format from AI Foundry Not Compatible with Evaluation Schema
Current Situation The synthetic dataset created from AI Foundry Data Synthetic Data is generated in the following messages format { "messages": [ { "role": "system", "content": "You are a helpful assistant" }, { "role": "user", "content": "What is the primary purpose?" }, { "role": "assistant", "content": "The primary purpose is..." } ] } Challenge When attempting evaluation, especially RAG evaluation, the documentation indicates that the dataset must contain structured fields such as question - The query being asked ground_truth - The expected answer Recommended additional fields reference_context metadata Example required format { "question": "", "ground_truth": "", "reference_context": "", "metadata": { "document": "" } } Because the synthetic dataset is in messages format, I am unable to directly map it to the required evaluation schema. Question Is there a recommended or supported way to convert the synthetic dataset generated in AI Foundry messages format into the structured format required for evaluation? Can the user role be mapped to question? Can the assistant role be mapped to ground_truth? Is there any built in transformation option within AI 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!
New Foundry Agent Issue
Hi all, I’m creating my first agent via New Foundry, so my questions are probably basic. As always, everything seemed straightforward… until deployment. I created an agent using gpt-4.1, added a list of instructions, and then used the Tools → Upload files functionality to attach a selection of reference documents. Everything worked perfectly in Preview mode. I then used the default option to Create a bot service, and it deployed successfully. To test it, I used the Individual Scope option (with the intention to share later with a couple of people — I haven’t worked that part out yet). Like magic, it appeared in my Teams and M365 Copilot, which was amazing… and then I ran my first search. It thought for a long time and then returned an error. In Co-pilot: and Teams: Nothing happens at all I’ve looked around for help but drawn a blank. I’m fairly sure it’s some kind of permissioning / access issue somewhere, but I can’t find where. Any help would be hugely appreciated.
Is there a way to connect 2 Ai foundry to the same cosmos containers?
I defined Azure AI Foundry Connection for Azure Cosmos DB and BYO Thread Storage in Azure AI Agent Service by using these instructions: Integration with Azure AI Agent Service - Azure Cosmos DB for NoSQL | Microsoft Learn I see that it created 3 containers under the cosmos I provided: <guid>-agent-entity-store v-system-thread-message-store <guid>-thread-message-store Now I created another AI foundry and added a connection for the same AI foundry, and it created 3 different containers under the same DB. Is there a way that they'll use the same exact containers? I want to use multiple AI foundries, and they will use the same Cosmos containers to manage the data.Searching for a simple guide to index SharePoint and publish an agent in Foundry
Hey all, Does anyone have a good guide or best practices for this setup in Foundry? SharePoint as data source GPT model (document + image indexing, ideally vectorized/embeddings) Create an Agent an Share the Agent Restrict access to Agent to specific users/groups only Looking for tutorials, examples, or real-world setups. Thanks!
Publishing New Foundry Agent to M365 and Teams (Org scope)
Hello all, I've been trying to publish a small agent from new Foundry to M365 and Teams following the official documentation but I am missing something. Please help! The creation part of the agent is easy and I get to the point where I want to publish this to users with an Org scope: At this point, I would need to deploy the agent in Microsoft 365 Admin Center (MAC) to users. However when I open MAC, there is nothing to validate! My new agent doesn't appear anywhere in M365 Copilot or teams, for me of for my users. What am I missing?? Do I need to do something in Entra as well? Thanks!
Azure Document Intelligence and Content Understanding
Hello, Our customer has dozens of Excel and PDF files. These files come in various formats, and the layouts may change over time. For example, some files provide data in a standard tabular structure, others use pivot-style Excel layouts, and some follow more complex or semi-structured formats. In total, we currently have approximately 150 distinct Excel templates and 80 distinct PDF templates. We need to extract information from these files and ingest it into normalized tables. Therefore, our requirement is to automatically infer the structure of each file, extract the required values, and load the results into Databricks tables. Given that there are already many template variations—and that new templates may emerge over time—what would be the recommended pipeline, technology stack, and architecture? Should we prefer Azure Document Intelligence? One option would be to create a custom model per template type. However, when a user uploads a new file, how can we reliably match the file to the correct existing model? Additionally, what should happen if a user uploads an Excel/PDF file in a significantly different format that does not resemble any existing template?Open AI model continuity plan for Standard Deployments in Australia East
Hi, I am working with an Azure customer in Australia on Agentic AI solutions. We have provisioned standard deployments of GPT-4o in Aus East due to the customer's need for data sovereignty. We have recently noticed in the customer's Azure AI Foundry that the standard deployment of GPT-4o in Aus East has a model retirement date of 3rd June 2026. This is the most advanced OpenAI model available for this deployment type. What is Azure's plan for Open AI model availability for standard deployments in Aus East going forward? Will our customer have access to 4o or a replacement model? ThanksAI Hub --> Project Structure In Microsoft Foundry
The AI Hub → Project structure works great for a single team. But when you've got a large org with multiple departments, each running their own hub with several projects. I found it doesn't quite fit the deployment model we needed. Here's the scenario: I create a hub per department, and they can share resources and apply governance across their projects. But I also need org-level policies that apply across all department hubs. And visibility into programs that span multiple departments. With the current two-level structure, I don't have a structural layer for that. Current options both have tradeoffs: Single org-wide hub with departments as projects = lose department-level resource isolation and independent governance Separate hubs per department = manually replicate org-level policies, no rollup reporting across departments For my scenario, it would help if: there was an intermediate level , either nested hubs or an explicit "portfolio/program" grouping, so governance can work at both org and department levels, with rollup visibility. Curious: are others running into this? How are you structuring org-level governance across multiple department hubs? Looking forward for suggestions on this, how others are doing this.
