I can't delete my Azure Key Vault Connection in Azure AI Foundry
I have deleted all project under my Azure AI Foundry, but I still can't delete the Azure Key Vault Connection. Error: Azure Key Vault connection [Azure Key Vault Name] cannot be deleted, all credentials will be lost. Why is this happening?Keeping Agents on Track: Introducing Task Adherence in Azure AI Foundry
Task Adherence is coming soon to public preview in both the Azure AI Content Safety API and Azure AI Foundry. It helps developers ensure AI agents stay aligned with their assigned tasks, preventing drift, misuse, or unsafe tool calls.
Upgrade your voice agent with Azure AI Voice Live API
Today, we are excited to announce the general availability of Voice Live API, which enables real-time speech-to-speech conversational experience through a unified API powered by generative AI models. With Voice Live API, developers can easily voice-enable any agent built with the Azure AI Foundry Agent Service. Azure AI Foundry Agent Service, enables the operation of agents that make decisions, invoke tools, and participate in workflows across development, deployment, and production. By eliminating the need to stitch together disparate components, Voice Live API offers a low latency, end-to-end solution for voice-driven experiences. As always, a diverse range of customers provided valuable feedback during the preview period. Along with announcing general availability, we are also taking this opportunity to address that feedback and improve the API. Following are some of the new features designed to assist developers and enterprises in building scalable, production-ready voice agents. More natively integrated GenAI models including GPT-Realtime Voice Live API enables developers to select from a range of advanced AI models designed for conversational applications, such as GPT-Realtime, GPT-5, GPT-4.1, Phi, and others. These models are natively supported and fully managed, eliminating the need for developers to manage model deployment or plan for capacity. These natively supported models may each have a distinct stage in their life cycle (e.g. public preview, generally available) and be subject to varying pricing structures. The table below lists the models supported in each pricing tier. Pricing Tier Generally Available In Public Preview Voice Live Pro GPT-Realtime, GPT-4.1, GPT-4o GPT-5 Voice Live Standard GPT-4o-mini, GPT-4.1-mini GPT-4o-Mini-Realtime, GPT-5-mini Voice Live Lite NA Phi-4-MM-Realtime, GPT-5-Nano, Phi-4-Mini Extended speech languages to 140+ Voice Live API now supports speech input in over 140 languages/locales. View all supported languages by configuration. Automatic multilingual configuration is enabled by default, using the multilingual model. Integrated with Custom Speech Developers need customization to better manage input and output for different use cases. Besides the support for Custom Voice released in May 2025, Voice Live now supports seamless integration with Custom Speech for improved speech recognition results. Developers can also improve speech input accuracy with phrase lists and refine speech synthesis pronunciation using custom lexicons, all without training a model. Learn how to customize speech and voice models for Voice Live API. Natural HD voices upgraded Neural HD voices in Azure AI Speech are contextually aware and engineered to provide a natural, expressive experience, making them ideal for voice agent applications. The latest V2 upgrade enhances lifelike qualities with features such as natural pauses, filler words, and seamless transitions between speaking styles, all available with Voice Live API. Check out the latest demo of Ava Neural HD V2. Improved VAD features for interruption detection Voice Live API now features semantic Voice Activity Detection (VAD), enabling it to intelligently recognize pauses and filler word interruptions in conversations. In the latest en-US evaluation on Multilingual filler words data, Voice Live API achieved ~20% relative improvement from previous VAD models. This leap in performance is powered by integrating semantic VAD into the n-best pipeline, allowing the system to better distinguish meaningful speech from filler noise and enabling more accurate latency tracking and cleaner segmentation, especially in multilingual and noisy environments. 4K avatar support Voice Live API enables efficient integration with streaming avatars. With the latest updates, avatar options offer support for high-fidelity 4K video models. Learn more about the avatar update. Improved function calling and integration with Azure AI Foundry Agent Service Voice Live API enables function calling to assist developers in building robust voice agents with their chosen generative AI models. This release improves asynchronous function calls and enhances integration with Azure AI Foundry Agent Service for agent creation and operation. Learn more about creating a voice live real-time voice agent with Azure AI Foundry Agent Service. More developer resources and availability in more regions Developer resources are available in C# and Python, with more to come. Get started with Voice Live API. Voice Live API is available in more regions now including Australia East, East US, Japan East, and UK South, besides the previously supported regions such as Central India, East US 2, South East Asia, Sweden Central, and West US 2. Check the features supported in each region. Customers adopting Voice Live In healthcare, patient experience is always the top priority. With Voice Live, eClinicalWorks’ healow Genie contact center solution is now taking healthcare modernization a step further. healow is piloting Voice Live API for Genie to inform patients about their upcoming appointments, answer common questions, and return voicemails. Reducing these routine calls saves healthcare staff hours each day and boosts patient satisfaction through timely interactions. “We’re looking forward to using Azure AI Foundry Voice Live API so that when a patient calls, Genie can detect the question and respond in a natural voice in near-real time,” said Sidd Shah, Vice President of Strategy & Business Growth at healow. “The entire roundtrip is all happening in Voice Live API.” If a patient asks about information in their medical chart, Genie can also fetch data from their electronic health record (EHR) and provide answers. Read the full story here. “If we did multiple hops to go across different infrastructures, that would add up to a diminished patient experience. The Azure AI Foundry Voice Live API is integrated into one single, unified solution, delivering speech-to-text and text-to-speech in the same infrastructure.” Bhawna Batra, VP of Engineering at eClinicalWorks Capgemini, a global business and technology transformation partner, is reimagining its global service desk managed operations through its Capgemini Cloud Infrastructure Services (CIS) division. The first phase covers 500,000 users across 45 clients, which is only part of the overall deployment base. The goal is to modernize the service desk to meet changing expectations for speed, personalization, and scale. To drive this transformation, Capgemini launched the “AI-Powered Service Desk” platform powered by Microsoft technologies including Dynamics 365 Contact Center, Copilot Studio, and Azure AI Foundry. A key enhancement was the integration of Voice Live API for real-time voice interactions, enabling intelligent, conversational support across telephony channels. The new platform delivers a more agile, truly conversational, AI-driven service experience, automating routine tasks and enhancing agent productivity. With scalable voice capabilities and deep integration across Microsoft’s ecosystem, Capgemini is positioned to streamline support operations, reduce response times, and elevate customer satisfaction across its enterprise client base. "Integrating Microsoft’s Voice Live API into our platform has been transformative. We’re seeing measurable improvements in user engagement and satisfaction thanks to the API’s low-latency, high-quality voice interactions. As a result, we are able to deliver more natural and responsive experiences, which have been positively received by our customers.” Stephen Hilton, EVP Chief Operating Officer at CIS Capgemini Astra Tech, a fast-growing UAE-based technology group part of G42, is bringing Voice Live API to its flagship platform, botim, a fintech-first and AI-native platform. Eight out of 10 smartphone users in the UAE already rely on the app. The company is now reshaping botim from a communications tool into a fintech-first service, adding features such as digital wallets, international remittances, and micro-loans. To achieve its broader vision, Astra Tech set out to make botim simpler, more intuitive, and more human. “Voice removes a lot of complexity, and it’s the most natural way to interact,” says Frenando Ansari, Lead Product Manager at Astra Tech. “For users with low digital literacy or language barriers, tapping through traditional interfaces can be difficult. Voice personalizes the experience and makes it accessible in their preferred language.” " The Voice Live API acts as a connective tissue for AI-driven conversation across every layer of the app. It gives us a standardized framework so that different product teams can incorporate voice without needing to hire deep AI expertise.” Frenando Ansari, Lead Product Manager at Astra Tech “The most impressive thing about the Voice Live API is the voice activity detection and the noise control algorithm.” Meng Wang, AI Head at Astra Tech Get started Voice Live API is transforming how developers build voice-enabled agent systems by providing an integrated, scalable, and efficient solution. By combining speech recognition, generative AI, and text-to-speech functionalities into a unified interface, it addresses the challenges of traditional implementations, enabling faster development and superior user experiences. From streamlining customer service to enhancing education and public services, the opportunities are endless. The future of voice-first solutions is here—let’s build it together! Voice Live API introduction (video) Try Voice Live in Azure AI Foundry Voice Live API documents Voice Live quickstart Voice Live Agent code sample in GitHub
Introducing the Microsoft Agent Framework
Introducing the Microsoft Agent Framework: A Unified Foundation for AI Agents and Workflows The landscape of AI development is evolving rapidly, and Microsoft is at the forefront with the release of the Microsoft Agent Framework an open-source SDK designed to empower developers to build intelligent, multi-agent systems with ease and precision. Whether you're working in .NET or Python, this framework offers a unified, extensible foundation that merges the best of Semantic Kernel and AutoGen, while introducing powerful new capabilities for agent orchestration and workflow design. Introducing Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps | Azure AI Foundry Blog Introducing Microsoft Agent Framework | Microsoft Azure Blog Why Another Agent Framework? Both Semantic Kernel and AutoGen have pioneered agentic development, Semantic Kernel with its enterprise-grade features and AutoGen with its research-driven abstractions. The Microsoft Agent Framework is the next generation of both, built by the same teams to unify their strengths: AutoGen’s simplicity in multi-agent orchestration. Semantic Kernel’s robustness in thread-based state management, telemetry, and type safety. New capabilities like graph-based workflows, checkpointing, and human-in-the-loop support This convergence means developers no longer have to choose between experimentation and production. The Agent Framework is designed to scale from single-agent prototypes to complex, enterprise-ready systems Core Capabilities AI Agents AI agents are autonomous entities powered by LLMs that can process user inputs, make decisions, call tools and MCP servers, and generate responses. They support providers like Azure OpenAI, OpenAI, and Azure AI, and can be enhanced with: Agent threads for state management. Context providers for memory. Middleware for action interception. MCP clients for tool integration Use cases include customer support, education, code generation, research assistance, and more—especially where tasks are dynamic and underspecified. Workflows Workflows are graph-based orchestrations that connect multiple agents and functions to perform complex, multi-step tasks. They support: Type-based routing Conditional logic Checkpointing Human-in-the-loop interactions Multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic) Workflows are ideal for structured, long-running processes that require reliability and modularity. Developer Experience The Agent Framework is designed to be intuitive and powerful: Installation: Python: pip install agent-framework .NET: dotnet add package Microsoft.Agents.AI Integration: Works with Foundry SDK, MCP SDK, A2A SDK, and M365 Copilot Agents Samples and Manifests: Explore declarative agent manifests and code samples Learning Resources: Microsoft Learn modules AI Agents for Beginners AI Show demos Azure AI Foundry Discord community Migration and Compatibility If you're currently using Semantic Kernel or AutoGen, migration guides are available to help you transition smoothly. The framework is designed to be backward-compatible where possible, and future updates will continue to support community contributions via the GitHub repository. Important Considerations The Agent Framework is in public preview. Feedback and issues are welcome on the GitHub repository. When integrating with third-party servers or agents, review data sharing practices and compliance boundaries carefully. The Microsoft Agent Framework marks a pivotal moment in AI development, bringing together research innovation and enterprise readiness into a single, open-source foundation. Whether you're building your first agent or orchestrating a fleet of them, this framework gives you the tools to do it safely, scalably, and intelligently. Ready to get started? Download the SDK, explore the documentation, and join the community shaping the future of AI agents.Azure AI Foundry: Advancing OpenTelemetry and delivering unified multi-agent observability
Microsoft is enhancing multi-agent observability by introducing new semantic conventions to OpenTelemetry, developed collaboratively with Outshift, Cisco’s incubation engine. These additions—built upon OpenTelemetry and W3C Trace Context—establish standardized practices for tracing and telemetry within multi-agent systems, facilitating consistent logging of key metrics for quality, performance, safety, and cost. This systematic approach enables more comprehensive visibility into multi-agent workflows, including tool invocations and collaboration. These advancements have been integrated into Azure AI Foundry, Microsoft Agent Framework, Semantic Kernel, and Azure AI packages for LangChain, LangGraph, and the OpenAI Agents SDK, enabling customers to get unified observability for agentic systems built using any of these frameworks with Azure AI Foundry. The additional semantic conventions and integration across different frameworks equip developers to monitor, troubleshoot, and optimize their AI agents in a unified solution with increased efficiency and valuable insights. “Outshift, Cisco's Incubation Engine, worked with Microsoft to add new semantic conventions in OpenTelemetry. These conventions standardize multi-agent observability and evaluation, giving teams comprehensive insights into their AI systems.” Giovanna Carofiglio, Distinguished Engineer, Cisco Multi-agent observability challenges Multi-agent systems involve multiple interacting agents with diverse roles and architectures. Such systems are inherently dynamic—they adapt in real time by decomposing complex tasks into smaller, manageable subtasks and distributing them across specialized agents. Each agent may invoke multiple tools, often in parallel or sequence, to fulfill its part of the task, resulting in emergent workflows that are non-linear and highly context dependent. Given the dynamic nature of multi-agent systems and the management across agents, observability becomes critical for debugging, performance tuning, security, and compliance for such systems. Multi-agent observability presents unique challenges that traditional GenAI telemetry standards fail to address. Traditional observability conventions are optimized for single-agent reasoning path visibility and lack the semantic depth to capture collaborative workflows across multiple agents. In multi-agent systems, tasks are often decomposed and distributed dynamically, requiring visibility into agent roles, task hierarchies, tool usage, and decision-making processes. Without standardized task spans and a unified namespace, it's difficult to trace cross-agent coordination, evaluate task outcomes, or analyze retry patterns. These gaps hinder white-box observability, making it hard to assess performance, safety, and quality across complex agentic workflows. Extending OpenTelemetry with multi-agent observability Microsoft has proposed new spans and attributes to OpenTelemetry semantic convention for GenAI agent and framework spans. They will enrich insights and capture the complexity of agent, tool, tasks and plans interactions in multi-agent systems. Below is a list of all the new additions proposed to OpenTelemetry. New Span/Trace/Attributes Name Purpose New span execute_task Captures task planning and event propagation, providing insights into how tasks are decomposed and distributed. New child spans under “invoke_agent” agent_to_agent_interaction Traces communication between agents agent.state.management Effective context, short or long term memory management agent_planning Logs the agent’s internal planning steps agent orchestration Capture agent-to-agent orchestration New attributes in invoke_agent span tool_definitions Describes the tool’s purpose or configuration llm_spans Records model call spans New attributes in “execute_tool” span tool.call.arguments Logs the arguments passed during tool invocation tool.call.results Records the results returned by the tool New event Evaluation - attributes (name, error.type, label) Enables structured evaluation of agent performance and decision-making More details can be found in the following pull-requests merged into OpenTelemetry Add tool definition plus tool-related attributes in invoke-agent, inference, and execute-tool spans Capture evaluation results for GenAI applications Azure AI Foundry delivers unified observability for Microsoft Agent Framework, LangChain, LangGraph, OpenAI Agents SDK Agents built with Azure AI Foundry (SDK or portal) get out-of-the box observability in Foundry. With the new addition, agents built on different frameworks including Microsoft Agent Framework, Semantic Kernel, LangChain, LangGraph and OpenAI Agents SDK can use Foundry for monitoring, analyzing, debugging and evaluation with full observability. Agents built on Microsoft Agent Framework and Semantic Kernel get out-of-box tracing and evaluations support in Foundry Observability. Agents built with LangChain, LangGraph and OpenAI Agents SDK can use the corresponding packages and detailed instructions listed in the documentation to get tracing and evaluations support in Foundry Observability. Customer benefits With standardized multi-agent observability and support across multiple agent frameworks, customers get the following benefits: Unified Observability Platform for Multi-agent systems: Foundry Observability is the unified multi-agent observability platform for agents built with Foundry SDK or other agent frameworks like Microsoft Agent Framework, LangGraph, Lang Chain, OpenAI SDK. End-to-End Visibility into multi-agent systems: Customers can see not just what the system did, but how and why—from user request, through agent collaboration, tool usage, to final output. Faster Debugging & Root Cause Analysis: When something goes wrong (e.g., wrong answer, safety violation, performance bottleneck), customers can trace the exact path, see which agent/tool/task failed, and why. Quality & Safety Assurance: Built-in metrics and evaluation events (e.g. task success and validation scores) help customers monitor and improve the quality and safety of their AI workflows. Cost & Performance Optimization: Detailed metrics on token usage, API calls, resource consumption, and latency help customers optimize efficiency and cost. Get Started today with end-to-end agent observability with Foundry Azure AI Foundry Observability is a unified solution for evaluating, monitoring, tracing, and governing the quality, performance, and safety of your AI systems end-to-end— all built into your AI development loop and backed by the power of Azure Monitor for full stack observability. From model selection to real-time debugging, Foundry Observability capabilities empower teams to ship production-grade AI with confidence and speed. It’s observability, reimagined for the enterprise AI era. With the above OpenTelemetry enhancements Azure AI Foundry now provides detailed multi-agent observability for agents built with different frameworks including Azure AI Foundry, Microsoft Agent Framework, LangChain, LangGraph and OpenAI Agents SDK. Learn more about Azure AI Foundry Observability and get end-to-end agent observability today!Integrating Azure AI Foundry with Copilot Studio: A Strategic and Technical Overview
As organizations accelerate their AI adoption, the need for flexible, scalable, and secure platforms becomes paramount. My previous article, Navigating AI Solutions: Microsoft Copilot Studio vs. Azure AI Foundry | Microsoft Community Hub, represented two powerful yet distinct approaches to building AI agents. While Copilot Studio offers a low-code/no-code interface for rapid deployment, targeting any kind of business user, Azure AI Foundry provides a pro-code environment with deep customization and orchestration capabilities, targeting developer audiences. But what if you would not need to decide between one or the other, but benefit from integrating both platforms and unlock transformative business value across all teams? This is exactly the question I got asked increasingly while I was teaching our “Copilot, Copilot Studio and Azure AI Foundry” Instructor Led Training courses as a Microsoft Technical Trainer. This article starts with the business rationale for integration. From there, I will continue with detailing the influence of cost and ROI parameters as part of decision-making. Last, I will guide you through multiple technical integration capabilities available today, and how both platforms can complement each other. Business Rationale for Integration Copilot Studio is primarily designed for business users to build conversational agents quickly. It excels in rapid prototyping, using a graphical workflow-style interface, identical to Power Automate. Users don’t require much development skills to build such agents. Azure AI Foundry, on the other hand, is tailored for developers and data scientists who are typically in need of model orchestration, customized tool integration and enterprise-grade scalability and governance. Integrating both platforms allows organizations to bridge the gap between business agility and technical depth, enabling the ones closer to the business to prototype while developers can focus on custom features, refining and scale. For example, organizations can start with Copilot Studio for customer-facing bots or internal assistants, but then later, transition to Azure AI Foundry for more complex workflows, multi-agent orchestration or custom model integration. This layered approach supports progressive AI maturity, allowing teams to evolve from simple agents to fully sophisticated AI ecosystems. Cost and ROI Considerations Copilot Studio billing vs Azure AI Foundry consumption cost billing As users interact with Copilot Studio agents, or as the agents perform tasks on behalf of users, users consume Copilot Studio messages. Copilot Studio messages are the key component influencing the monthly cost of using Copilot Studio. Capabilities are available via the Copilot Studio pay-as-you-go meter (pay per message) and the Copilot Studio message pack subscription (25,000 messages monthly) license, or a combination of both. These license options are active on tenant-level. Any user with a Microsoft 365 Copilot license gets access to Copilot Studio, with no message-based charge. More details are available in the Microsoft Copilot Studio Licensing Guide. Azure AI Foundry is part of Azure’s consumption-based model, where you do not get charged for Azure AI Foundry itself, but you get charged a consumption cost for the different models your applications use. This charge can be listed as Microsoft (e.g. Azure OpenAI) or charged through the Azure marketplace (e.g. Cohere). Image: Azure AI Foundry model cost consumption overview from within Azure Cost Analysis Depending on the AI solution architecture your application workloads are based on, you should also take other Azure costs into account such as Azure Storage Accounts, Azure AI Search, Azure App Services, Azure Key Vault and alike. Since Azure AI Foundry charges are identical to any other Azure Resource charges, managing these is not different than your current Azure Cost Analysis approach. ROI and Budget alignment From the previous section, it should be clear that allocating the right budget can become complex, depending on the AI platforms used. By integrating both platforms, organizations can achieve cost optimization, by using Copilot Studio for lightweight tasks but scaling via Azure AI Foundry for compute-intensive operations. Given the lower complexity of building applications with Copilot Studio, they tend to result in early ROI, through Copilot Studio’s fast deployment. Azure AI Foundry’s robust and extensible infrastructure could lead to a longer-term value of ROI optimization. Technical Integration Capabilities HTTP Request Trigger One integration method involves using Copilot Studio’s HTTP Request feature to trigger Foundry Agents. This allows for Natural language prompts in Studio to initiate backend processes in Azure AI Foundry. This allows users to benefit from a seamless flow between conversational UI and enterprise logic, to consult business data, run data analytics or retrieve information across different enterprise application backends. Image: HTTP Request setup within Copilot Studio Topic MCP Protocol Azure AI Foundry now supports Model Context Protocol (MCP), an open standard enabling seamless interaction between large language models (LLMs) and external tools, systems or data sources. MCP provides a model-agnostic interface for tasks such as reading files, executing functions, and handling contextual prompts. Its primary goal is to simplify the integration of LLMs with third-party systems by addressing the complexity of building custom connectors for each tool or data source. MCP Tools can be integrated into your AI solutions using Azure AI Foundry Agent Service or through common development language SDKs or REST API. Check this Microsoft Learn module for more technical details on how to configure this or check out MCP-for beginners on YouTube https://aka.ms/MCP-for-beginners. Recently, the Model Context Protocol (MCP) Connector also became available as a new tool directly within Copilot Studio. Image: Model Context Protocol Connector Tool in Copilot Studio By integrating MCP Tools from within either Foundry Agent Service or through Copilot Studio, the organization can benefit from the standardized approach to allow connectivity to different enterprise systems, data endpoints or external applications. Simplifying the complexity and providing a smooth interaction irrespective of the AI platform used, provides major benefits to both business users and developer teams building these applications. Azure AI Foundry Models available to Copilot Studio (preview feature) Azure AI Foundry Models provides +11,000 models for you to choose from, offered by both Microsoft and an extensive range of model providers such as OpenAI, DeepSeek, Black Forest Labs, Meta and many more. On top of existing models offered, organizations can also create their own customized models by fine-tuning from within Azure AI Foundry. For example, imagine an organization building an IT support agent, which interacts with end-users using a chat interface and natural language. Users might be able to provide screenshots of errors, as well as describe technical issues in their own words. Traditional LLMs could struggle with recognizing specific screenshot details or business-specific terminology used by custom in-house developed applications, as they are not trained in this kind of information. That’s where fine-tuned models could be a solution. At the time of writing this article, a new preview feature became available to Copilot Studio customers, allowing them to use any Azure AI Foundry model, both catalog and fine-tuned ones, as the primary model for their Copilot Studio Agents. (FYI, follow this link for all details on the Copilot Studio Roadmap and features list) Image: Copilot Studio New Feature setting to enable AI Foundry model integration Conclusion Integrating Copilot Studio and Azure AI Foundry is not just a technical exercise, but rather a strategic move which aligns business goals, cost efficiency, and adoption readiness. By leveraging the strengths of both platforms, organizations can build AI solutions that are agile, scalable, and secure. Your business can focus on developing (or ‘making’ if not code-based) AI Agents, without facing bottlenecks or unneeded complexity or isolation of workloads. Instead of asking the question of which platform to use for building AI applications, organizations should invest in and benefit from a tight integration between both platforms, quickly enabling teams from both the business side as well as developers, to create AI-influenced applications that provide immediate business value, without compromise. #MicrosoftLearn #SkilledByMTT
Issue when connecting from SPFX to Entra-enabled Azure AI Foundry resource
We have been successfully connecting our chat bot from an SPFX to a chat completion model in Azure, using key authentication. We have a requirement now to disable key authentication. This is what we've done so far: disabled API authentication in the resource Gave to the SharePoint Client Extensibility Web Application Principal "Cognitive Services OpenAI User", "Cognitive Service User" and "Cognitive Data Reader" permission in the resource In the SPFX we have added the following in the package-solution.json (and we have approved it in the SharePoint admin site): "webApiPermissionRequests": [ { "resource": "Azure Machine Learning Services", "scope": "user_impersonation" } ] To connect to the chat completion API we're using fetchEventSource from '@microsoft/fetch-event-source', so we're getting a Bearer token using AadTokenProviderFactory from "@microsoft/sp-http", e.g.: // preceeded by some code to get the tokenProvider from aadTokenProviderFactory const token = await tokenProvider.getToken('https://ai.azure.com'); const url = "https://my-ai-resource.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2025-01-01-preview"; await fetchEventSource(url, { method: 'POST', headers: { Accept: 'text/event-stream', 'Content-type': 'application/json', Authorization: `Bearer ${token}` }, body: body, ...// truncated We added the users (let's say, email address removed for privacy reasons) in the resource as an Azure AI User. When we try to get this to work, we get the following error: The principal `email address removed for privacy reasons` lacks the required data action `Microsoft.CognitiveServices/accounts/OpenAI/deployments/chat/completions/action` to perform `POST /openai/deployments/{deployment-id}/chat/completions` operation. How can we make this work? Ideally we would prefer the SPFX principal to do the request to the chat completion API, without needed to have to add end users in the resource thorugh IAC, but my understanding is that AadTokenProviderFactory only issues delegated access tokens.Announcing GPT‑5‑Codex: Redefining Developer Experience in Azure AI Foundry
Today, we’re excited to announce OpenAI’s GPT‑5‑Codex is generally available in Azure AI Foundry, and in public preview for GitHub Copilot in Visual Studio Code. This release is the next step in our continuous commitment to empower developers with the latest model innovation, now building on the proven strengths of the earlier Codex generation along with the speed and CLI fluency many teams have adopted with the latest codex‑mini. Next-level features for developers Multimodal coding in a single flow: GPT-5-Codex accepts multimodal inputs including text and image. With this multimodal intelligence, developers are now empowered to tackle complex tasks, delivering context-aware, repository-scale solutions in one single workflow. Advanced tool use across various experiences: GPT-5-Codex is built for real-world developer experiences. Developers in Azure AI Foundry can get seamless automation and deep integration via the Response API, improving developers’ productivity and reducing development time. Code review expertise: GPT‑5‑Codex is specially trained to conduct code reviews and surface critical flows, helping developers catch issues early and improve code quality with AI-powered insights. It transforms code review from a manual bottleneck into an intelligent, adaptive and integrated process, empowering developers to deliver high-quality code experience. How GPT‑5‑Codex makes your life easier Stay in flow, not in friction: With GPT‑5‑Codex, move smoothly from reading issues to writing code and checking UI; all in one place. It keeps context, so developers stay focused and productive. No more jumping between tools or losing track of what they were doing. Refactor and migrate with confidence: Whether cleaning up code or moving to a new framework, GPT‑5‑Codex helps stage updates, run tests, and fix issues as you go. It’s like having a digital colleague for those tricky transitions. Hero use cases: real impact for developers Repo‑aware refactoring assistant: Feed repo and architecture diagrams to GPT‑5‑Codex. Get cohesive refactors, automated builds, and visual verification via screenshots. Flaky test hunter: Target failing test matrices. The model executes runs, polls status, inspects logs, and recommends fixes looping until stability. Cloud migration copilot: Edit IaC scripts, kick off CLI commands, and iterate on errors in a controlled loop, reducing manual toil. Pricing and Deployment available at GA Deployment Available Region Pricing ($/million tokens) Standard Global East US 2 Sweden Central Input Cached Input Output $1.25 $0.125 $10.00 GPT-5-Codex is bringing developers’ coding experience to a new level. Don’t just write code. Let’s redefine what’s possible. Start building with GPT-5-Codex today and turn your bold ideas into reality now powered by the latest innovation in Azure AI Foundry.
