Recent Discussions
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
Import error: Cannot import name "PromptAgentDefinition" from "azure.ai.projects.models"
Hello, I am trying to build the agentic retrieval using Azure Ai search. During the creation of agent i am getting "ImportError: cannot import name 'PromptAgentDefinition' from 'azure.ai.projects.models'". Looked into possible ways of building without it but I need the mcp connection. This is the documentation i am following: https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-how-to-create-pipeline?tabs=search-perms%2Csearch-development%2Cfoundry-setup Note: There is no Promptagentdefinition in the directory of azure.ai.projects.models. ['ApiKeyCredentials', 'AzureAISearchIndex', 'BaseCredentials', 'BlobReference', 'BlobReferenceSasCredential', 'Connection', 'ConnectionType', 'CosmosDBIndex', 'CredentialType', 'CustomCredential', 'DatasetCredential', 'DatasetType', 'DatasetVersion', 'Deployment', 'DeploymentType', 'EmbeddingConfiguration', 'EntraIDCredentials', 'EvaluatorIds', 'FieldMapping', 'FileDatasetVersion', 'FolderDatasetVersion', 'Index', 'IndexType', 'ManagedAzureAISearchIndex', 'ModelDeployment', 'ModelDeploymentSku', 'NoAuthenticationCredentials', 'PendingUploadRequest', 'PendingUploadResponse', 'PendingUploadType', 'SASCredentials', 'TYPE_CHECKING', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_enums', '_models', '_patch', '_patch_all', '_patch_evaluations', '_patch_sdk'] Traceback (most recent call last): Please let me know what i should do and if there is any other alternative. Thanks in advance.Timeline for General Availability of SharePoint Data Source in Azure AI Search
The SharePoint data source feature in Azure AI Search is currently in preview. Could Microsoft or anyone here provide any guidance on the expected timeline for its General Availability (GA)? This functionality is essential for enabling seamless integration of enterprise content into AI-powered search solutions, and clarity on the roadmap will help organizations plan their adoption strategies effectively.Issue with: Connected Agent Tool Forcing from an Orchestrator Agent
Hi Team, I am trying to force tool selection for my Connected Agents from an Orchestrator Agent for my Multi-Agent Model. Not sure if that is possible Apologies in advance for too much detail as I really need this to work! Please let me know if there is a flaw in my approach! The main intention behind going towards Tool forcing was because with current set of instructions provided to my Orchestrator Agent, It was providing hallucinated responses from my Child Agents for each query. I have an Orchestrator Agent which is connected to the following Child Agents (Each with detailed instructions) Child Agent 1 - Connects to SQL DB in Fabric to fetch information from Log tables. Child Agent 2 - Invokes OpenAPI Action tool for Azure Functions to run pipelines in Fabric. I have provided details on 3 approaches. Approach 1: I have checked the MS docs "CONNECTED_AGENT" is a valid property for ToolChoiceType "https://learn.microsoft.com/en-us/python/api/azure-ai-agents/azure.ai.agents.models.agentsnamedtoolchoicetype?view=azure-python" Installed the latest Python AI Agents SDK Beta version as it also supports "Connected Agents": https://pypi.org/project/azure-ai-agents/1.2.0b6/#create-an-agent-using-another-agents The following code is integrated into a streamlit UI code. Python Code: agents_client = AgentsClient( endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential( exclude_environment_credential=True, exclude_managed_identity_credential=True ) ) # ------------------------------------------------------------------- # UPDATE ORCHESTRATOR TOOLS (executed once) # ------------------------------------------------------------------- fabric_tool = ConnectedAgentTool( id=FABRIC_AGENT_ID, name="Fabric_Agent", description="Handles Fabric pipeline questions" ) openapi_tool = ConnectedAgentTool( id=OPENAPI_AGENT_ID, name="Fabric_Pipeline_Trigger", description="Handles OpenAPI pipeline triggers" ) # Update orchestrator agent to include child agent tools agents_client.update_agent( agent_id=ORCH_AGENT_ID, tools=[ fabric_tool.definitions[0], openapi_tool.definitions[0] ], instructions=""" You are the Master Orchestrator Agent. Use: - "Fabric_Agent" when the user's question includes: "Ingestion", "Trigger", "source", "Connection" - "Fabric_Pipeline_Trigger" when the question mentions: "OpenAPI", "Trigger", "API call", "Pipeline start" Only call tools when needed. Respond clearly and concisely. """ ) # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger","pipeline","connection"]): return fabric_tool if any(k in text for k in ["openapi", "api call", "pipeline start"]): return openapi_tool # No forced routing → let orchestrator decide return None forced_tool = choose_tool(user_query) run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice={ "type": "connected_agent", "function": forced_tool.definitions[0] } Error: Azure.core.exceptions.HttpResponseError: (invalid_value) Invalid value: 'connected_agent'. Supported values are: 'code_interpreter', 'function', 'file_search', 'openapi', 'azure_function', 'azure_ai_search', 'bing_grounding', 'bing_custom_search', 'deep_research', 'sharepoint_grounding', 'fabric_dataagent', 'computer_use_preview', and 'image_generation'. Code: invalid_value Message: Invalid value: 'connected_agent'. Supported values are: 'code_interpreter', 'function', 'file_search', 'openapi', 'azure_function', 'azure_ai_search', 'bing_grounding', 'bing_custom_search', 'deep_research', 'sharepoint_grounding', 'fabric_dataagent', 'computer_use_preview', and 'image_generation'." Approach 2: Create ConnectedAgentTool as you do, and pass its definitions to update_agent(...). Force a tool by name using tool_choice={"type": "function", "function": {"name": "<tool-name>"}}. Do not set type: "connected_agent" anywhere—there is no such tool_choice.type. Code: from azure.identity import DefaultAzureCredential from azure.ai.agents import AgentsClient # Adjust imports to your SDK layout: # e.g., from azure.ai.agents.tool import ConnectedAgentTool agents_client = AgentsClient( endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential( exclude_environment_credential=True, exclude_managed_identity_credential=True # keep your current credential choices ) ) # ------------------------------------------------------------------- # CREATE CONNECTED AGENT TOOLS (child agents exposed as function tools) # ------------------------------------------------------------------- fabric_tool = ConnectedAgentTool( id=FABRIC_AGENT_ID, # the **child agent ID** you created elsewhere name="Fabric_Agent", # **tool name** visible to the orchestrator description="Handles Fabric pipeline questions" ) openapi_tool = ConnectedAgentTool( id=OPENAPI_AGENT_ID, # another child agent ID name="Fabric_Pipeline_Trigger", # tool name visible to the orchestrator description="Handles OpenAPI pipeline triggers" ) # ------------------------------------------------------------------- # UPDATE ORCHESTRATOR: attach child tools # ------------------------------------------------------------------- # NOTE: definitions is usually a list of ToolDefinition objects produced by the helper agents_client.update_agent( agent_id=ORCH_AGENT_ID, tools=[ fabric_tool.definitions[0], openapi_tool.definitions[0] ], instructions=""" You are the Master Orchestrator Agent. Use: - "Fabric_Agent" when the user's question includes: "Ingestion", "Trigger", "source", "Connection" - "Fabric_Pipeline_Trigger" when the question mentions: "OpenAPI", "Trigger", "API call", "Pipeline start" Only call tools when needed. Respond clearly and concisely. """ ) # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger", "pipeline", "connection"]): return "Fabric_Agent" # return the **tool name** if any(k in text for k in ["openapi", "api call", "pipeline start"]): return "Fabric_Pipeline_Trigger" # return the **tool name** return None forced_tool_name = choose_tool(user_query) # ------------------------- RUN INVOCATION ------------------------- if forced_tool_name: # FORCE a specific connected agent by **function name** run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice={ "type": "function", # <-- REQUIRED "function": { "name": forced_tool_name # <-- must match the tool's name as registered } } ) else: # Let the orchestrator auto-select (no tool_choice → "auto") run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID ) Error: azure.core.exceptions.HttpResponseError: (None) Invalid tool_choice: Fabric_Agent. You must also pass this tool in the 'tools' list on the Run. Code: None Message: Invalid tool_choice: Fabric_Agent. You must also pass this tool in the 'tools' list on the Run. Approach 3: Modified version of the 2nd Approach with Took Definitions call: # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger","pipeline","connection"]): # return "Fabric_Agent" return ( "Fabric_Agent", fabric_tool.definitions[0] ) if any(k in text for k in ["openapi", "api call", "pipeline start"]): # return "Fabric_Pipeline_Trigger" return ( "Fabric_Pipeline_Trigger", openapi_tool.definitions[0] ) # No forced routing → let orchestrator decide # return None return (None, None) # forced_tool = choose_tool(user_query) forced_tool_name, forced_tool_def = choose_tool(user_query) # ------------------------- ORCHESTRATOR CALL ------------------------- if forced_tool_name: tool_choice = { "type": "function", "function": { "name": forced_tool_name } } run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice=tool_choice, tools=[ forced_tool_def ] # << only the specific tool ) else: # no forced tool, orchestrator decides run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID ) Error: TypeError: azure.ai.agents.operations._patch.RunsOperations.create() got multiple values for keyword argument 'tools'Structured Outputs fail with server_error when Bing Grounding is enabled in Azure AI Agents
Hi everyone, I’m running into a reproducible issue when using Structured Outputs (response_format: json_schema) together with Azure AI Agents that have the Bing Grounding tool enabled. The API always returns: "last_error": { "code": "server_error", "message": "Sorry, something went wrong." } The call returns HTTP 200, but the run fails immediately before the model generates any tokens (prompt_tokens = 0). Environment Azure AI Foundry (Sweden Central) Project: Azure AI Agents Model: gpt-4.1 (Standard DataZone) Agent with tool: bing_grounding (created from the UI) API version visible in logs: 2025-05-15-preview SDK: azure-ai-projects 1.2.0b6 azure-ai-agents 1.2.0b6 What I am Trying to Do I am attempting to enforce a JSON Schema output using: response_format = ResponseFormatJsonSchemaType( json_schema=ResponseFormatJsonSchema( name="test_schema", description="Simple structured output test", schema={ "type": "object", "properties": { "mensaje": {"type": "string"} }, "required": ["mensaje"], "additionalProperties": False } ) ) Then calling: run = client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent.id, response_format=response_format ) This same schema works successfully when the agent does NOT have Bing grounding enabled or when using the model outside of Agents. Observed Behavior The API request succeeds (HTTP 200), but the run immediately fails: Full run status: { "id": "run_XXXX", "status": "failed", "last_error": { "code": "server_error", "message": "Sorry, something went wrong." }, "model": "gpt-4.1-EU-LDZ", "tools": [ { "type": "bing_grounding", "bing_grounding": { "search_configurations": [ { "connection_id": "...", "market": "es-es", "set_lang": "es", "count": 5 } ] } } ], "response_format": { "type": "json_schema", "json_schema": { "name": "test_schema", "schema": { "type": "object", "properties": {"mensaje": {"type": "string"}}, "required": ["mensaje"], "additionalProperties": false } } }, "usage": { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 } } Key points: prompt_tokens = 0 → The failure happens before the model receives the prompt. The same code works if: The agent has no tools Or I remove response_format The error is always the same: server_error. How to Reproduce Create an Azure AI Agent in AI Foundry. Add Bing Grounding to the agent. Set the model to gpt-4.1. Run the following minimal Python script: from azure.ai.projects import AIProjectClient from azure.ai.agents.models import ResponseFormatJsonSchema, ResponseFormatJsonSchemaType from azure.identity import AzureCliCredential client = AIProjectClient( endpoint="YOUR_ENDPOINT", credential=AzureCliCredential() ) agent_id = "YOUR_AGENT_ID" schema = { "type": "object", "properties": {"mensaje": {"type": "string"}}, "required": ["mensaje"] } response_format = ResponseFormatJsonSchemaType( json_schema=ResponseFormatJsonSchema( name="test_schema", description="Test schema", schema=schema ) ) thread = client.agents.threads.create() client.agents.messages.create( thread_id=thread.id, role="user", content="Say hello" ) run = client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent_id, response_format=response_format ) print(run.status, run.last_error) Result: status = failed, last_error = server_error. Expected Behavior Structured Outputs should work when the agent has tools enabled (including Bing grounding), or at least return a meaningful validation error instead of server_error. Question Is the combination Agents + Bing Grounding + Structured Outputs (json_schema) + gpt-4.1 currently supported? Is this a known limitation or bug? Is there a recommended workaround? I am happy to provide full request IDs (X-Request-ID and apim-request-id) privately via support channels if needed. Thanks!
Azure AI foundry projects
Hello, my use case: I need to be able to call my agent, which I created inside my azure ai foundry project. I have API route and also some API key, and the most crucial thing - agent id. Now, can someone explain to me, why all the documentation is telling me that I need some sort of authorization. I have already tried it and it is working. Now I am trying to think about something else. How to use this agent in some production ready apps? I am not able to create accounts for everybody who try to call my service. How this can be done ?
Synchronous REST API for Language Text Summarization
This topic referenced this Language Text Summarization: https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/text-summarization?source=recommendations Microsoft documentation on Language Text Summarization (Abstractive and Extractive) were for asynchronous REST API call. This is ideal for situation where we need to pass in files or long text for summarization. I need to implement solution where I need to call REST API synchronously for short text summarization. Is this even possible? If yes, please point me to the resource/documentation. Thanks, briancodeyAzure OpenAI Model Upgrades: Prompt Safety Pitfalls with GPT-4o and Beyond
Upgrading to New Azure OpenAI Models? Beware Your Old Prompts Might Break. I recently worked on upgrading our Azure OpenAI integration from gpt-35-turbo to gpt-4o-mini, expecting it to be a straightforward configuration change. Just update the Azure Foundry resource endpoint, change the model name, deploy the code — and voilà, everything should work as before. Right? Not quite. The Unexpected Roadblock As soon as I deployed the updated code, I started seeing 400 status errors from the OpenAI endpoint. The message was cryptic: The response was filtered due to the prompt triggering Azure OpenAI's content management policy. At first, I assumed it was a bug in my SDK call or a malformed payload. But after digging deeper, I realized this wasn’t a technical failure — it was a content safety filter kicking in before the prompt even reached the model. The Prompt That Broke It Here’s the original system prompt that worked perfectly with gpt-35-turbo: YOU ARE A QNA EXTRACTOR IN TEXT FORMAT. YOU WILL GET A SET OF SURVEYJS QNA JSONS. YOU WILL CONVERT THAT INTO A TEXT DOCUMENT. FOR THE QUESTIONS WHERE NO ANSWER WAS GIVEN, MARK THOSE AS NO ANSWER. HERE IS THE QNA: BE CREATIVE AND PROFESSIONAL. I WANT TO GENERATE A DOCUMENT TO BE PUBLISHED. {{$style}} +++++ {{$input}} +++++ This prompt had been reliable for months. But with gpt-4o-mini, it triggered Azure’s new input safety layer, introduced in mid-2024. What Changed with GPT-4o-mini? Unlike gpt-35-turbo, the gpt-4o family: Applies stricter content filtering — not just on the output, but also on the input prompt. Treats system messages and user messages as role-based chat messages, passing them through moderation before the model sees them. Flags prompts that look like prompt injection attempts like aggressive instructions like “YOU ARE…”, “BE CREATIVE”, “GENERATE”, “PROFESSIONAL”. Flags unusual formatting (like `+++++`), artificial delimiters or token markers as it may look like encoded content. In short, the model didn’t even get a chance to process my prompt — it was blocked at the gate. Fixing It: Softening the Prompt The solution wasn’t to rewrite the entire logic, but to soften the system prompt and remove formatting that could be misinterpreted. Here’s what helped: - Replacing “YOU ARE…” with a gentler instruction like “Please help convert the following Q&A data…” - Removing creative directives like “BE CREATIVE” or “PROFESSIONAL” unless clearly contextualized. - Avoiding raw JSON markers and template syntax (`{{ }}`, `+++++`) in the prompt. Once I made these changes, the model responded smoothly — and the upgrade was finally complete. Evolving the Prompt — Not Abandoning It Interestingly, for some prompts I didn’t have to completely eliminate the “YOU ARE…” structure. Instead, I refined it to be more natural and less directive. Here’s a comparison: ❌ Old Prompt (Blocked) ✅ New Prompt (Accepted) YOU ARE A SOURCING AND PROCUREMENT MANAGER. YOU WILL GET BUYER'S REQUIREMENTS IN QNA FORMAT. HERE IS THE QNA: {{$input}} +++++ YOU WILL GENERATE TOP 10 {{$category}} RELATED QUESTIONS THAT CAN BE ASKED OF A SUPPLIER IN JSON FORMAT. THE JSON MUST HAVE QUESTION NUMBER AS THE KEY AND QUESTION TEXT AS THE QUESTION. DON'T ADD ANY DESCRIPTION TEXT OR FORMATTING IN THE OUTPUT. BE CREATIVE AND PROFESSIONAL. I WANT TO GENERATE AN RFX. You are an AI assistant that helps clarify sourcing requirements. You will receive buyer's requirements in QnA format. Here is the QnA: {$input} Your task is to generate the top 10 {$category} related questions that can be asked of a supplier, in JSON format. - The JSON must use the question number as the key and the question text as the value. - Do not include any description text or formatting in the output. - Focus on creating clear, professional, and relevant questions that will help prepare an RFX. Key Takeaways - Model upgrades aren’t just about configuration changes — they can introduce new moderation layers that affect prompt design. - Prompt safety filtering is now a first-class citizen in Azure OpenAI, especially for newer models. - System prompts need to be rewritten with moderation in mind, not just clarity or creativity. This experience reminded me that even small upgrades can surface big learning moments. If you're planning to move to gpt-4o-mini or any newer Azure OpenAI model, take a moment to review your prompts — they might need a little more finesse than before.BYO Thread Storage in Azure AI Foundry using Python
Build scalable, secure, and persistent multi-agent memory with your own storage backend As AI agents evolve beyond one-off interactions, persistent context becomes a critical architectural requirement. Azure AI Foundry’s latest update introduces a powerful capability — Bring Your Own (BYO) Thread Storage — enabling developers to integrate custom storage solutions for agent threads. This feature empowers enterprises to control how agent memory is stored, retrieved, and governed, aligning with compliance, scalability, and observability goals. What Is “BYO Thread Storage”? In Azure AI Foundry, a thread represents a conversation or task execution context for an AI agent. By default, thread state (messages, actions, results, metadata) is stored in Foundry’s managed storage. With BYO Thread Storage, you can now: Store threads in your own database — Azure Cosmos DB, SQL, Blob, or even a Vector DB. Apply custom retention, encryption, and access policies. Integrate with your existing data and governance frameworks. Enable cross-region disaster recovery (DR) setups seamlessly. This gives enterprises full control of data lifecycle management — a big step toward AI-first operational excellence. Architecture Overview A typical setup involves: Azure AI Foundry Agent Service — Hosts your multi-agent setup. Custom Thread Storage Backend — e.g., Azure Cosmos DB, Azure Table, or PostgreSQL. Thread Adapter — Python class implementing the Foundry storage interface. Disaster Recovery (DR) replication — Optional replication of threads to secondary region. Implementing BYO Thread Storage using Python Prerequisites First, install the necessary Python packages: pip install azure-ai-projects azure-cosmos azure-identity Setting Up the Storage Layer from azure.cosmos import CosmosClient, PartitionKey from azure.identity import DefaultAzureCredential import json from datetime import datetime class ThreadStorageManager: def __init__(self, cosmos_endpoint, database_name, container_name): credential = DefaultAzureCredential() self.client = CosmosClient(cosmos_endpoint, credential=credential) self.database = self.client.get_database_client(database_name) self.container = self.database.get_container_client(container_name) def create_thread(self, user_id, metadata=None): """Create a new conversation thread""" thread_id = f"thread_{user_id}_{datetime.utcnow().timestamp()}" thread_data = { 'id': thread_id, 'user_id': user_id, 'messages': [], 'created_at': datetime.utcnow().isoformat(), 'updated_at': datetime.utcnow().isoformat(), 'metadata': metadata or {} } self.container.create_item(body=thread_data) return thread_id def add_message(self, thread_id, role, content): """Add a message to an existing thread""" thread = self.container.read_item(item=thread_id, partition_key=thread_id) message = { 'role': role, 'content': content, 'timestamp': datetime.utcnow().isoformat() } thread['messages'].append(message) thread['updated_at'] = datetime.utcnow().isoformat() self.container.replace_item(item=thread_id, body=thread) return message def get_thread(self, thread_id): """Retrieve a complete thread""" try: return self.container.read_item(item=thread_id, partition_key=thread_id) except Exception as e: print(f"Thread not found: {e}") return None def get_thread_messages(self, thread_id): """Get all messages from a thread""" thread = self.get_thread(thread_id) return thread['messages'] if thread else [] def delete_thread(self, thread_id): """Delete a thread""" self.container.delete_item(item=thread_id, partition_key=thread_id) Integrating with Azure AI Foundry from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential class ConversationManager: def __init__(self, project_endpoint, storage_manager): self.ai_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=project_endpoint ) self.storage = storage_manager def start_conversation(self, user_id, system_prompt): """Initialize a new conversation""" thread_id = self.storage.create_thread( user_id=user_id, metadata={'system_prompt': system_prompt} ) # Add system message self.storage.add_message(thread_id, 'system', system_prompt) return thread_id def send_message(self, thread_id, user_message, model_deployment): """Send a message and get AI response""" # Store user message self.storage.add_message(thread_id, 'user', user_message) # Retrieve conversation history messages = self.storage.get_thread_messages(thread_id) # Call Azure AI with conversation history response = self.ai_client.inference.get_chat_completions( model=model_deployment, messages=[ {"role": msg['role'], "content": msg['content']} for msg in messages ] ) assistant_message = response.choices[0].message.content # Store assistant response self.storage.add_message(thread_id, 'assistant', assistant_message) return assistant_message Usage Example # Initialize storage and conversation manager storage = ThreadStorageManager( cosmos_endpoint="https://your-cosmos-account.documents.azure.com:443/", database_name="conversational-ai", container_name="threads" ) conversation_mgr = ConversationManager( project_endpoint="your-project-connection-string", storage_manager=storage ) # Start a new conversation thread_id = conversation_mgr.start_conversation( user_id="user123", system_prompt="You are a helpful AI assistant." ) # Send messages response1 = conversation_mgr.send_message( thread_id=thread_id, user_message="What is machine learning?", model_deployment="gpt-4" ) print(f"AI: {response1}") response2 = conversation_mgr.send_message( thread_id=thread_id, user_message="Can you give me an example?", model_deployment="gpt-4" ) print(f"AI: {response2}") # Retrieve full conversation history history = storage.get_thread_messages(thread_id) for msg in history: print(f"{msg['role']}: {msg['content']}") Key Highlights: Threads are stored in Cosmos DB under your control. You can attach metadata such as region, owner, or compliance tags. Integrates natively with existing Azure identity and Key Vault. Disaster Recovery & Resilience When coupled with geo-replicated Cosmos DB or Azure Storage RA-GRS, your BYO thread storage becomes resilient by design: Primary writes in East US replicate to Central US. Foundry auto-detects failover and reconnects to secondary region. Threads remain available during outages — ensuring operational continuity. This aligns perfectly with the AI-First Operational Excellence architecture theme, where reliability and observability drive intelligent automation. Best Practices Area Recommendation Security Use Azure Key Vault for credentials & encryption keys. Compliance Configure data residency & retention in your own DB. Observability Log thread CRUD operations to Azure Monitor or Application Insights. Performance Use async I/O and partition keys for large workloads. DR Enable geo-redundant storage & failover tests regularly. When to Use BYO Thread Storage Scenario Why it helps Regulated industries (BFSI, Healthcare, etc.) Maintain data control & audit trails Multi-region agent deployments Support DR and data sovereignty Advanced analytics on conversation data Query threads directly from your DB Enterprise observability Unified monitoring across Foundry + Ops The Future BYO Thread Storage opens doors to advanced use cases — federated agent memory, semantic retrieval over past conversations, and dynamic workload failover across regions. For architects, this feature is a key enabler for secure, scalable, and compliant AI system design. For developers, it means more flexibility, transparency, and integration power. Summary Feature Benefit Custom thread storage Full control over data Python adapter support Easy extensibility Multi-region DR ready Business continuity Azure-native security Enterprise-grade safety Conclusion Implementing BYO thread storage in Azure AI Foundry gives you the flexibility to build AI applications that meet your specific requirements for data governance, performance, and scalability. By taking control of your storage, you can create more robust, compliant, and maintainable AI solutions.Open-Source SDK for Evaluating AI Model Outputs (Sharing Resource)
Hi everyone, I wanted to share a helpful open-source resource for developers working with LLMs, AI agents, or prompt-based applications. One common challenge in AI development is evaluating model outputs in a consistent and structured way. Manual evaluation can be subjective and time-consuming. The project below provides a framework to help with that: AI-Evaluation SDK https://github.com/future-agi/ai-evaluation Key Features: - Ready-to-use evaluation metrics - Supports text, image, and audio evaluation - Pre-defined prompt templates - Quickstart examples available in Python and TypeScript - Can integrate with workflows using toolkits like LangChain Use Case: If you are comparing different models or experimenting with prompt variations, this SDK helps standardize the evaluation process and reduces manual scoring effort. If anyone has experience with other evaluation tools or best practices, I’d be interested to hear what approaches you use.Open-Source SDK for Evaluating AI Model Outputs (Sharing Resource)
Hi everyone, I wanted to share a helpful open-source resource for developers working with LLMs, AI agents, or prompt-based applications. One common challenge in AI development is evaluating model outputs in a consistent and structured way. Manual evaluation can be subjective and time-consuming. The project below provides a framework to help with that: AI-Evaluation SDK https://github.com/future-agi/ai-evaluation Key Features: - Ready-to-use evaluation metrics - Supports text, image, and audio evaluation - Pre-defined prompt templates - Quickstart examples available in Python and TypeScript - Can integrate with workflows using toolkits like LangChain Use Case: If you are comparing different models or experimenting with prompt variations, this SDK helps standardize the evaluation process and reduces manual scoring effort. If anyone has experience with other evaluation tools or best practices, I’d be interested to hear what approaches you use
Recent Blogs
- Turning a real world decision problem into a solver ready optimization model can take days—sometimes weeks—even for experienced teams. The hardest part is often not solving the problem; it’s translat...Jan 15, 2026
- Microsoft Foundry will now integrate Hugging Face’s gated models, giving enterprises secure steps access to advanced open-source AI models directly within their Azure environment. New gated models wi...Jan 14, 2026
