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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? Thanks
Published agent from Foundry doesn't work at all in Teams and M365
I've switched to the new version of Azure AI Foundry (New) and created a project there. Within this project, I created an Agent and connected two custom MCP servers to it. The agent works correctly inside Foundry Playground and responds to all test queries as expected. My goal was to make this agent available for my organization in Microsoft Teams / Microsoft 365 Copilot, so I followed all the steps described in the official Microsoft documentation: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/publish-copilot?view=foundry Issue description The first problems started at Step 8 (Publishing the agent). Organization scope publishing I published the agent using Organization scope. The agent appeared in Microsoft Admin Center in the list of agents. However, when an administrator from my organization attempted to approve it, the approval always failed with a generic error: “Sorry, something went wrong” No diagnostic information, error codes, or logs were provided. We tried recreating and republishing the agent multiple times, but the result was always the same. Shared scope publishing As a workaround, I published the agent using Shared scope. In this case, the agent finally appeared in Microsoft Teams and Microsoft 365 Copilot. I can now see the agent here: Microsoft Teams → Copilot Microsoft Teams → Applications → Manage applications However, this revealed the main issue. Main problem The published agent cannot complete any query in Teams, despite the fact that: The agent works perfectly in Foundry Playground The agent responds correctly to the same prompts before publishing In Teams, every query results in messages such as: “Sorry, something went wrong. Try to complete a query later.” Simplification test To exclude MCP or instruction-related issues, I performed the following: Disabled all MCP tools Removed all complex instructions Left only a minimal system prompt: “When the user types 123, return 456” I then republished the agent. The agent appeared in Teams again, but the behavior did not change — it does not respond at all. Permissions warning in Teams When I go to: Teams → Applications → Manage Applications → My agent → View details I see a red warning label: “Permissions needed. Ask your IT admin to add InfoConnect Agent to this team/chat/meeting.” This message is confusing because: The administrator has already added all required permissions All relevant permissions were granted in Microsoft Entra ID Admin consent was provided Because of this warning, I also cannot properly share the agent with my colleagues. Additional observation I have a similar agent configured in Copilot Studio: It shows the same permissions warning However, that agent still responds correctly in Teams It can also successfully call some MCP tools This suggests that the issue is specific to Azure AI Foundry agents, not to Teams or tenant-wide permissions in general. Steps already taken to resolve the issue Configured all required RBAC roles in Azure Portal according to: https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/rbac-foundry?view=foundry-classic During publishing, an agent-bot application was automatically created. I added my account to this bot with the Azure AI User role I also assigned Azure AI User to: The project’s Managed Identity The project resource itself Verified all permissions related to AI agents publishing in: Microsoft Admin Center Microsoft Teams Admin Center Simplified and republished the agent multiple times Deleted the automatically created agent-bot and allowed Foundry to recreate it Created a new Foundry project, configured several simple agents, and published them — the same issue occurs Tried publishing with different models: gpt-4.1, o4-mini Manually configured permissions in: Microsoft Entra ID → App registrations / Enterprise applications → API permissions Added both Delegated and Application permissions and granted Admin consent Added myself and my colleagues as Azure AI User in: Foundry → Project → Project users Followed all steps mentioned in this related discussion: https://techcommunity.microsoft.com/discussions/azure-ai-foundry-discussions/unable-to-publish-foundry-agent-to-m365-copilot-or-teams/4481420 Questions How can I make a Foundry agent work correctly in Microsoft Teams? Why does the agent fail to process requests in Teams while working correctly in Foundry? What does the “Permissions needed” warning actually mean for Foundry agents? How can I properly share the agent with other users in my organization? Any guidance, diagnostics, or clarification on the correct publishing and permission model for Foundry agents in Teams would be greatly appreciated.
AI 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.
Turning “cool agent demos” into accountable systems – how are you doing this in Azure AI Foundry?
Hi everyone, I’m working with customers who are very excited about the new agentic capabilities in Azure AI Foundry (and the Microsoft Agent Framework). The pattern is always the same: Building a cool agent demo is easy. Turning it into an accountable, production-grade system that governance, FinOps, security and data people are happy with… not so much. I’m curious how others are dealing with this in the real world, so here’s how I currently frame it with customers and I’d love to hear where you do things differently or better. Governance: who owns the agent, and what does “safe enough” mean? - For us, an agent is not “just another script”. It’s a proper application with: - An owner (a real person, not a team name). - A clear purpose and scope. - A policy set (what it can and cannot do). - A minimum set of controls (access, logging, approvals, evaluation, rollback). In Azure AI Foundry terms: we try to push as much as possible into “as code” (config, infra, CI/CD) instead of burying it in PowerPoint and Word docs. The litmus test I use: if this agent makes a bad decision in production, can we show – to audit or leadership – which data, tools, policies and model versions were involved? If the answer is “not really”, we’re not done. FinOps: if you can’t cap it, you can’t scale it Agentic solutions are fantastic at chaining calls and quietly generating cost. We try to design with: Explicit cost budgets per agent / per scenario. A clear separation between “baseline” workloads and “burst / experimentation”. Observability on cost per unit of value (per ticket, per document, per transaction, etc.). Some of this maps nicely to existing cloud FinOps practices, some feels new because of LLM behaviour. My personal rule: I don’t want to ship an agent to production if I can’t explain its cost behaviour in 2–3 slides to a CFO. Data, context and lineage: where most of the real risk lives In my experience, most risk doesn’t come from the model, but from: Which data the agent can see. How fresh and accurate that data is. Whether we can reconstruct the path from data → answer → decision. We’re trying to anchor on: Data products/domains as the main source of truth. Clear contracts around what an agent is allowed to read or write. Strong lineage for anything that ends up in front of a user or system of record. From a user’s point of view, “Where did this answer come from?” is quickly becoming one of the most important questions. GreenOps / sustainability: starting to show up in conversations Some customers now explicitly ask: “What is the energy impact of this AI workload?” “Can we schedule, batch or aggregate work to reduce energy use and cost?” So we’re starting to treat GreenOps as the “next layer” after cost: not just “is it cheap enough?”, but also “is it efficient and responsible enough?”. What I’d love to learn from this community: In your Azure AI Foundry/agentic solutions, where do governance decisions actually live today? Mostly in documentation and meetings, or do you already have patterns for policy-as-code / eval-as-code? How are you bringing FinOps into the design of agents? Do you have concrete cost KPIs per agent/scenario, or is it still “we’ll see what the bill says”? How are you integrating data governance and lineage into your agent designs? Are you explicitly tying agents to data products/domains with clear access rules? Any “red lines” for data they must never touch? Has anyone here already formalised “GreenOps” thinking for AI Foundry workloads? If yes, what did you actually implement (scheduling, consolidation, region choices, something else)? And maybe the most useful bit: what went wrong for you so far? Without naming customers, obviously. Any stories where a nice lab pattern didn’t survive contact with governance, security or operations? I’m especially interested in concrete patterns, checklists or “this is the minimum we insist on before we ship an agent” criteria. Code examples are very welcome, but I’m mainly looking for the operating model and guardrails around the tech. Thanks in advance for any insights, patterns or war stories you’re willing to share.How to Reliably Gauge LLM Confidence?
a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } I’m trying to estimate an LLM’s confidence in its answers in a way that correlates with correctness. Self-reported confidence is often misleading, and raw token probabilities mostly reflect fluency rather than truth. I don’t have grounding options like RAG, human feedback, or online search, so I’m looking for approaches that work in this constraint. What techniques have you found effective—entropy-based signals, calibration (temperature scaling), self-evaluation, or others? Any best practices for making confidence scores actionable?
cosmos_vnet_blocked error with BYO standard agent setup
Hi! We've tried deploying the standard agent setup using terraform as described in the https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/virtual-networks?view=foundry-classic and using the terraform sample available https://github.com/azure-ai-foundry/foundry-samples/tree/main/infrastructure/infrastructure-setup-terraform/15a-private-network-standard-agent-setup/code as a basis to give the necessary support in our codebase. However we keep getting the following error: cosmos_vnet_blocked: Access to Cosmos DB is blocked due to VNET configuration. Please check your network settings and make sure CosmosDB is public network enabled, if this is a public standard agent setup. Has anyone experienced this error?Get to know the core Foundry solutions
Foundry includes specialized services for vision, language, documents, and search, plus Microsoft Foundry for orchestration and governance. Here’s what each does and why it matters: Azure Vision With Azure Vision, you can detect common objects in images, generate captions, descriptions, and tags based on image contents, and read text in images. Example: Automate visual inspections or extract text from scanned documents. Azure Language Azure Language helps organizations understand and work with text at scale. It can identify key information, gauge sentiment, and create summaries from large volumes of content. It also supports building conversational experiences and question-answering tools, making it easier to deliver fast, accurate responses to customers and employees. Example: Understand customer feedback or translate text into multiple languages. Azure Document IntelligenceWith Azure Document Intelligence, you can use pre-built or custom models to extract fields from complex documents such as invoices, receipts, and forms. Example: Automate invoice processing or contract review. Azure SearchAzure Search helps you find the right information quickly by turning your content into a searchable index. It uses AI to understand and organize data, making it easier to retrieve relevant insights. This capability is often used to connect enterprise data with generative AI, ensuring responses are accurate and grounded in trusted information. Example: Help employees retrieve policies or product details without digging through files. Microsoft FoundryActs as the orchestration and governance layer for generative AI and AI agents. It provides tools for model selection, safety, observability, and lifecycle management. Example: Coordinate workflows that combine multiple AI capabilities with compliance and monitoring. Business leaders often ask: Which Foundry tool should I use? The answer depends on your workflow. For example: Are you trying to automate document-heavy processes like invoice handling or contract review? Do you need to improve customer engagement with multilingual support or sentiment analysis? Or are you looking to orchestrate generative AI across multiple processes for marketing or operations? Connecting these needs to the right Foundry solution ensures you invest in technology that delivers measurable results.Index data from SharePoint document libraries => Visioning / Image Analysis
Hi, I`m currently testing the indexing of SharePoint data according to the following instructions https://learn.microsoft.com/en-us/azure/search/search-how-to-index-sharepoint-online So far, so good. My question: Visioning on images is not enabled. Besides the Microsoft links, I found 2-3 other good links for the SharePoint indexer, but unfortunately none for Visioning / Image Analysis. Does anyone here have this working? Any tips or links on how to implement it? Many thanks
