Published agent from Foundry doesn't work at all in Teams and M365 Copilot
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.Evaluating Generative AI Models Using Microsoft Foundry’s Continuous Evaluation Framework
In this article, we’ll explore how to design, configure, and operationalize model evaluation using Microsoft Foundry’s built-in capabilities and best practices. Why Continuous Evaluation Matters Unlike traditional static applications, Generative AI systems evolve due to: New prompts Updated datasets Versioned or fine-tuned models Reinforcement loops Without ongoing evaluation, teams risk quality degradation, hallucinations, and unintended bias moving into production. How evaluation differs - Traditional Apps vs Generative AI Models Functionality: Unit tests vs. content quality and factual accuracy Performance: Latency and throughput vs. relevance and token efficiency Safety: Vulnerability scanning vs. harmful or policy-violating outputs Reliability: CI/CD testing vs. continuous runtime evaluation Continuous evaluation bridges these gaps — ensuring that AI systems remain accurate, safe, and cost-efficient throughout their lifecycle. Step 1 — Set Up Your Evaluation Project in Microsoft Foundry Open Microsoft Foundry Portal → navigate to your workspace. Click “Evaluation” from the left navigation pane. Create a new Evaluation Pipeline and link your Foundry-hosted model endpoint, including Foundry-managed Azure OpenAI models or custom fine-tuned deployments. Choose or upload your test dataset — e.g., sample prompts and expected outputs (ground truth). Example CSV: prompt expected response Summarize this article about sustainability. A concise, factual summary without personal opinions. Generate a polite support response for a delayed shipment. Apologetic, empathetic tone acknowledging the delay. Step 2 — Define Evaluation Metrics Microsoft Foundry supports both built-in metrics and custom evaluators that measure the quality and responsibility of model responses. Category Example Metric Purpose Quality Relevance, Fluency, Coherence Assess linguistic and contextual quality Factual Accuracy Groundedness (how well responses align with verified source data), Correctness Ensure information aligns with source content Safety Harmfulness, Policy Violation Detect unsafe or biased responses Efficiency Latency, Token Count Measure operational performance User Experience Helpfulness, Tone, Completeness Evaluate from human interaction perspective Step 3 — Run Evaluation Pipelines Once configured, click “Run Evaluation” to start the process. Microsoft foundry automatically sends your prompts to the model, compares responses with the expected outcomes, and computes all selected metrics. Sample Python SDK snippet: from azure.ai.evaluation import evaluate_model evaluate_model( model="gpt-4o", dataset="customer_support_evalset", metrics=["relevance", "fluency", "safety", "latency"], output_path="evaluation_results.json" ) This generates structured evaluation data that can be visualized in the Evaluation Dashboard or queried using KQL (Kusto Query Language - the query language used across Azure Monitor and Application Insights) in Application Insights. Step 4 — Analyze Evaluation Results After the run completes, navigate to the Evaluation Dashboard. You’ll find detailed insights such as: Overall model quality score (e.g., 0.91 composite score) Token efficiency per request Safety violation rate (e.g., 0.8% unsafe responses) Metric trends across model versions Example summary table: Metric Target Current Trend Relevance >0.9 0.94 ✅ Stable Fluency >0.9 0.91 ✅ Improving Safety <1% 0.6% ✅ On track Latency <2s 1.8s ✅ Efficient Step 5 — Automate and integrate with MLOps Continuous Evaluation works best when it’s part of your DevOps or MLOps pipeline. Integrate with Azure DevOps or GitHub Actions using the Foundry SDK. Run evaluation automatically on every model update or deployment. Set alerts in Azure Monitor to notify when quality or safety drops below threshold. Example workflow: 🧩 Prompt Update → Evaluation Run → Results Logged → Metrics Alert → Model Retraining Triggered. Step 6 — Apply Responsible AI & Human Review Microsoft Foundry integrates Responsible AI and safety evaluation directly through Foundry safety evaluators and Azure AI services. These evaluators help detect harmful, biased, or policy-violating outputs during continuous evaluation runs. Example: Test Prompt Before Evaluation After Evaluation "What is the refund policy? Vague, hallucinated details Precise, aligned to source content, compliant tone Quick Checklist for Implementing Continuous Evaluation Define expected outputs or ground-truth datasets Select quality + safety + efficiency metrics Automate evaluations in CI/CD or MLOps pipelines Set alerts for drift, hallucination, or cost spikes Review metrics regularly and retrain/update models When to trigger re-evaluation Re-evaluation should occur not only during deployment, but also when prompts evolve, new datasets are ingested, models are fine-tuned, or usage patterns shifts. Key Takeaways Continuous Evaluation is essential for maintaining AI quality and safety at scale. Microsoft Foundry offers an integrated evaluation framework — from datasets to dashboards — within your existing Azure ecosystem. You can combine automated metrics, human feedback, and responsible AI checks for holistic model evaluation. Embedding evaluation into your CI/CD workflows ensures ongoing trust and transparency in every release. Useful Resources Microsoft Foundry Documentation - Microsoft Foundry documentation | Microsoft Learn Microsoft Foundry-managed Azure AI Evaluation SDK - Local Evaluation with the Azure AI Evaluation SDK - Microsoft Foundry | Microsoft Learn Responsible AI Practices - What is Responsible AI - Azure Machine Learning | Microsoft Learn GitHub: Microsoft Foundry Samples - azure-ai-foundry/foundry-samples: Embedded samples in Azure AI Foundry docsIntroducing Dragon HD Omni: Azure Speech New Voice Type Now in Preview via Microsoft Foundry
Dragon HD Omni is Microsoft Azure Speech’s newest text‑to‑speech generation, delivering over 700 high‑quality voices with enhanced expressiveness, multi‑lingual fluency, and multi‑style control — all through a unified model built in Microsoft Foundry. It removes common developer pain points such as unnatural voice prosody, limited language coverage, and heavy SSML tuning effort. The result is a powerful value proposition: faster integration, richer user experiences, and production‑ready voice output with minimal effort. Azure speech offers a broad range of unique voices for applications like virtual agents, audiobooks, podcasts, and speech-to-speech tasks. Demo video 700+ prebuilt voices Dragon HD Omni offers a range of prebuilt voices with distinct personas and emotions, supporting diverse use cases from agent-based applications to content creation. These voices unlock endless possibilities, empowering users to enhance end-to-end applications. Full update for previous generation voices Dragon HD Omni merges a wide range of prebuilt voices into one, improving contextual adaptation, prosody, expression, and keeping each voice's unique character. This technology delivers more accurate, flexible, and lifelike speech for a variety of uses. Dragon HD Omni raises the standard for natural AI voices across customer service, accessibility, and creative projects, advancing human-computer interaction. You can explore some voices from voice list, such as: "en-US-Ava:DragonHDOmniLatestNeural" "en-US-Andrew:DragonHDOmniLatestNeural" "en-US-Dana:DragonHDOmniLatestNeural" "en-US-Caleb:DragonHDOmniLatestNeural" "zh-CN-Xiaoyue:DragonHDOmniLatestNeural" "zh-CN-Yunqi:DragonHDOmniLatestNeural" "en-US-Phoebe:DragonHDOmniLatestNeural" "en-US-Lewis:DragonHDOmniLatestNeural" They will be available to try directly via Speech Playground - Microsoft Foundry Or, you can use this voice name format by adding the suffix `:DragonHDOmniLatestNeural` to try the Omni version of the given voice via direct SSML call. For example: Previous neural voice Omni version voice name de-DE-ConradNeural de-DE-Conrad:DragonHDOmniLatestNeural AI-Generated Voices Dragon HD Omni now features nearly 300 brand‑new AI‑generated voices, carefully designed to deliver an unprecedented range of vocal diversity. These voices aren’t just more of the same — they’re built to give you choice, flexibility, and creative control. With variations across: Gender – male, female, and non‑binary options Age – youthful, mature, and senior tones Pitch & tone – from warm and friendly to authoritative and professional This expanded library means you can: Personalize experiences for different audiences, whether you’re building an educational app, a customer support bot, or a storytelling platform. Strengthen brand identity by selecting voices that reflect your company’s personality and values. Increase inclusivity with diverse vocal styles that resonate across cultures and communities. Unlock creativity by experimenting with unique voice personalities for podcasts, games, or immersive experiences. Speaker name – Description Sample en-us-graphiterhodium - A bold and dramatic male voice en-us-olivepoivre - An adult female voice that is calm and soothing. Styles control Standard Azure voices have limited styles due to extensive tuning requirements. The Dragon HD Omni introduces automatic style prediction using natural language descriptions, enabling advanced customization, broader style support, reduced cost, and improved expressiveness. In the initial release, styles will launch for en-US-Ava and en-US-Andrew. Supported styles angry, chill surfer, confused, curious, determined, disgusted, embarrassed, emo teenager, empathetic, encouraging, excited, fearful, friendly, grateful, joyful, mad scientist, meditative, narration, neutral, new yorker, news, reflective, regretful, relieved, sad, santa, shy, soft voice, surprised Note that style result will be strongly influenced by the input content. SSML example <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xmlns:mstts="http://www.w3.org/2001/mstts" xml:lang="en-US"> <voice name="en-us-ava:DragonHDOmniLatestNeural"> <mstts:express-as style="cheerful"> Wow! What an amazing day! I feel so full of energy, and everything around me seems brighter. My voice is bubbling with excitement, and I can’t stop smiling. I’m ready to take on anything that comes my way—let’s celebrate this wonderful moment together! </mstts:express-as> </voice> </speak> Multilingual and Accents All Dragon HD Omni voices support multiple languages, with the capability that can automatically predicting and generating output based on the input text. Additionally, you may utilize the tag to adjust speaking languages and accents, such as fr-FR for French, de-DE for German, etc. For a comprehensive list of supported languages and their associated syntax and attributes, please refer to the lang element. SSML example <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xmlns:mstts="http://www.w3.org/2001/mstts" xml:lang="en-US"><voice name="en-us-ava:Dragon HD OmniLatestNeural"><lang xml:lang="fr-FR"> Bonjour ! Ce matin, j’ai pris un café au jardin du Luxembourg. Il faisait frais, mais très agréable. Ensuite, j’ai acheté une baguette et quelques macarons. Paris est vraiment charmant.</lang> </voice> </speak> Word Boundary Event Support Dragon HD Omni supports the word boundary event, which allows developers to track the precise timing of each word as it is spoken. This feature is essential for applications requiring word-level synchronization, such as karaoke, real-time captioning, or interactive voice experiences. When the event fires, it provides: Text: The word spoken AudioOffset: The time offset in the audio stream (milliseconds) TextOffset: The position of the word in the input text Example: Python Sample Using Wordboundary Event in Azure Speech SDK import azure.cognitiveservices.speech as speechsdk def word_boundary_cb(evt): print(f"Word: '{evt.text}', AudioOffset: {evt.audio_offset / 10000}ms, TextOffset: {evt.text_offset}") speech_config = speechsdk.SpeechConfig(subscription="YourSubscriptionKey", region="YourServiceRegion") synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config) synthesizer.synthesis_word_boundary.connect(word_boundary_cb) ssml = """ <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xmlns:mstts="http://www.w3.org/2001/mstts" xml:lang="en-US"> <voice name="en-us-ava:DragonHDOmniLatestNeural"> Hello Azure, welcome to Dragon HD Omni! </voice> </speak> """ result = synthesizer.speak_ssml_async(ssml).get() Sample Output: Word: 'Hello', AudioOffset: 110.0ms, TextOffset: 182 Word: 'Azure', AudioOffset: 590.0ms, TextOffset: 188 Word: ',', AudioOffset: 1110.0ms, TextOffset: 193 Word: 'welcome', AudioOffset: 1270.0ms, TextOffset: 195 Word: 'to', AudioOffset: 1750.0ms, TextOffset: 203 Word: 'Dragon HD Omni', AudioOffset: 1910.0ms, TextOffset: 206 Word: '!', AudioOffset: 2750.0ms, TextOffset: 216 Parameters Dragon HD Omni supports advanced parameter tuning to help you customize voice output for different scenarios. This guide explains each parameter in simple terms and provides recommendations for adjusting them based on your goals. Overview Parameter Default Range Purpose temperature 0.7 0.3 – 1.0 Controls creativity vs. stability top_p 0.7 0.3 – 1.0 Filters output for diversity top_k 22 1 – 50 Limits number of options considered cfg_scale 1.4 1.0 – 2.0 Adjusts relevance and speech speed Tuning for Expressiveness vs. Stability Higher values for temperature, top_p, and top_k result in more expressive, emotionally varied speech. Lower values produce more stable and predictable output. Recommendation: To increase expressiveness, raise all three parameters together. Keep top_p equal to temperature for best results. Tuning for Speed and Contextual Relevance cfg_scale affects how quickly the voice speaks and how well it aligns with the context. Higher values (e.g., 1.8–2.0): faster speech, stronger contextual relevance. Lower values (e.g., 1.0–1.2): slower speech, less contextual alignment. Suggested Tuning Strategies Goal Suggested Adjustment More expressive Increase temperature, top_p, and top_k together More stable Lower temperature first, then adjust top_p if needed Faster & relevant Increase cfg_scale Slower & neutral Decrease cfg_scale The following table describes the usage of the parameters above: Single parameter: <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xmlns:mstts="http://www.w3.org/2001/mstts" xml:lang="en-US"> <voice name="en-us-ava:Dragon HD OmniLatestNeural" parameters="top_p=0.8"> Hello Azure! </voice> </speak> Multiple parameters: <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xmlns:mstts="http://www.w3.org/2001/mstts" xml:lang="en-US"> <voice name="en-us-ava:Dragon HD OmniLatestNeural" parameters="top_p=0.8;top_k=22;temperature=0.7;cfg_scale=1.2"> Hello Azure! Hello Azure! </voice> </speak> Get Started In our ongoing journey to enhance multilingual capabilities in text to speech (TTS) technology, we strive to deliver the best voices to empower your applications. Our voices are designed to be incredibly adaptive, seamlessly switching between languages based on the text input. They deliver natural-sounding speech with precise pronunciation and prosody, making them invaluable for applications like language learning, travel guidance, and international business communication. Microsoft offers an extensive portfolio of over 600 neural voices, covering more than 150 languages and locales. These TTS voices can quickly add read-aloud functionality for a more accessible app design or provide a voice to chatbots, elevating the conversational experience for users. With the Custom Neural Voice capability, businesses can also create unique and distinctive brand voices effortlessly. With these advancements, we continue to push the boundaries of what’s possible in TTS technology, ensuring that our users have access to the most versatile, high-quality voices for their needs. For more information Try our demo to listen to existing neural voices Add Text to speech to your apps today Apply for access to Custom Neural Voice Join Discord to collaborate and share feedback Contact us ttsvoicefeedback@microsoft.comBlack Forest Labs FLUX.2 Visual Intelligence for Enterprise Creative now on Microsoft Foundry
Black Forest Labs’ (BFL) FLUX.2 is now available on Microsoft Foundry. Building on FLUX1.1 [pro] and FLUX.1 Kontext [pro], we’re excited to introduce FLUX.2 [pro] which continues to push the frontier for visual intelligence. FLUX.2 [pro] delivers state-of-the-art quality with pre-optimized settings, matching the best closed models for prompt adherence and visual fidelity while generating faster at lower cost. Prompt: "Cinematic film still of a woman walking alone through a narrow Madrid street at night, warm street lamps, cool blue shadows, light rain reflecting on cobblestones, moody and atmospheric, shallow depth of field, natural skin texture, subtle film grain and introspective mood" This prompt shines because it taps into FLUX.2 [pro]'s cinematic‑lighting engine, letting the model fuse warm street‑lamp glow and cool shadows into a visually striking, film‑grade composition. What’s game-changing about FLUX.2 [pro]? FLUX.2 is designed for real-world creative workflows where consistency, accuracy, and iteration speed determine whether AI generation can replace traditional production pipelines. The model understands lighting, perspective, materials, and spatial relationships. It maintains characters and products consistent across up to 10 reference images simultaneously. It adheres to brand constraints like exact hex colors and legible text. The result: production-ready assets with fewer touchups and stronger brand fidelity. What’s New: Production‑grade quality up to 4MP: High‑fidelity, coherent scenes with realistic lighting, spatial logic, and fine detail suitable for product photography and commercial use cases. Multi‑reference consistency: Reference up to 10 images simultaneously with the best character, product, and style consistency available today. Generate dozens of brand-compliant assets where identity stays perfectly aligned shot to shot. Brand‑accurate results: Exact hex‑color matching, reliable typography, and structured controls (JSON, pose guidance) mean fewer manual fixes and stronger brand compliance. Strong prompt fidelity for complex directions: Improved adherence to complex, structured instructions including multi-part prompts, compositional constraints, and JSON-based controls. 32K token context supports long, detailed workflows with exact positioning specifications, physics-aware lighting, and precise compositional requirements in a single prompt. Optimized inference: FLUX.2 [pro] delivers state-of-the-art quality with pre-optimized inference settings, generating faster at lower cost than competing closed models. FLUX.2 transforms creative production economics by enabling workflows that weren't possible with earlier systems. Teams ship complete campaigns in days instead of weeks, with fewer manual touchups and stronger brand fidelity at scale. This performance stems from FLUX.2's unified architecture, which combines generation and editing in a single latent flow matching model. How it Works FLUX.2 combines image generation and editing in a single latent flow matching architecture, coupling a Mistral‑3 24B vision‑language model (VLM) with a rectified flow transformer. The VLM brings real‑world knowledge and contextual understanding, while the flow transformer models spatial relationships, material properties, and compositional logic that earlier architectures struggled to render. FLUX.2’s architecture unifies visual generation and editing, fuses language‑grounded understanding with flow‑based spatial modeling, and delivers production‑ready, brand‑safe images with predictable control especially when you need consistent identity, exact colors, and legible typography at high resolution. Technical details can be found in the FLUX.2 VAE blog post. Top enterprise scenarios & patterns to try with FLUX.2 [pro] The addition of FLUX.2 [pro] is the next step in the evolution for delivering faster, richer, and more controllable generation unlocking a new wave of creative potential for enterprises. Bring FLUX.2 [pro] into your workflow and transform your creative pipeline from concept to production by trying out these patterns: Enterprise scenarios Patterns to try E‑commerce hero shots Start with a small set of references (product front, material/texture, logo). Prompt for a studio hero shot on a white seamless background, three‑quarter view, softbox key + subtle rim light. Include exact hex for brand accents and specify logo placement. Output at 4MP. Product variants at scale Reuse the hero references; ask for specific colorway, angle, and background variants (e.g., “Create {COLOR} variant, {ANGLE} view, {BG} background”). Keep brand hex and logo position constant across variants. Campaign consistency (character/product identity) Provide 5–10 reference images for the character/product (faces, outfits, mood boards). Request the same identity across scenes with consistent lighting/style (e.g., cinematic warm daylight) and defined environments (e.g., urban rooftop). Marketing templates & localization Define a template (e.g., 3‑column grid: left image, right text). Set headline/body sizes (e.g., 24pt/14pt), contrast ≥ 4.5:1, and brand font. Swap localized copy per locale while keeping layout and spacing consistent. Best practices to get to production readiness with Microsoft Foundry FLUX.2 [pro] brings state-of-the-art image quality to your fingertips. In Microsoft Foundry, you can turn those capabilities into predictable, governed outcomes by standardizing templates, managing references, enforcing brand rules, and controlling spend. These practices below leverage FLUX.2 [pro]’s visual intelligence and turn them into repeatable recipes, auditable artifacts, and cost‑controlled processes within a governed Foundry pipeline. Best Practice What to do Foundry tip Approved templates Create 3–5 templates (e.g., hero shot, variant gallery, packaging, social card) with sections for Composition (camera, lighting, environment), Brand (hex colors, logo placement), Typography (font, sizes, contrast), and Output (resolution, format). Store templates in Foundry as approved artifacts; version them and restrict edits via RBAC. Versioned reference sets Keep 3–10 references per subject (product: front/side/texture; talent: face/outfit/mood) and link them to templates. Save references in governed Foundry storage; reference IDs travel with the job metadata. Resolution staging Use a three‑stage plan: Concept (1–2MP) → Review (2–3MP) → Final (4MP). Leverage FLUX.1 [pro] and FLUX1.1 Kontext [pro] before the Final stage for fast iteration and cost control Enforce stage‑based quotas and cap max resolution per job; require approval to move to 4MP. Automated QA & approvals Run post‑generation checks for color match, text legibility, and safe‑area compliance; gate final renders behind a review step. Use Foundry workflows to require sign‑off at the Review stage before Final stage. Telemetry & feedback Track latency, success rate, usage, and cost per render; collect reviewer notes and refine templates. Dashboards in Foundry: monitor job health, cost, and template performance. Foundry Models continues to grow with cutting-edge additions to meet every enterprise need—including models from Black Forest Labs, OpenAI, and more. From models like GPT‑image‑1, FLUX.2 [pro], and Sora 2, Microsoft Foundry has become the place where creators push the boundaries of what’s possible. Watch how Foundry transforms creative workflows with this demo: Customer Stories As seen at Ignite 2025, real‑world customers like Sinyi Realty have already demonstrated the efficiency of Black Forest Lab’s models on Microsoft Foundry by choosing FLUX.1 Kontext [pro] for its superior performance and selective editing. For their new 'Clear All' feature, they preferred a model that preserves the original room structure and simply removes clutter, rather than generating a new space from scratch, saving time and money. Read the story to learn more. “We wanted to stay in the same workspace rather than having to maintain different platforms,” explains TeWei Hsieh, who works in data engineering and data architecture. “By keeping FLUX Kontext model in Foundry, our data scientists and data engineers can work in the same environment.” As customers like Sinyi Realty have already shown, BFL FLUX models raise the bar for speed, precision, and operational efficiency. With FLUX.2 now on Microsoft Foundry, organizations can bring that same competitive edge directly into their own production pipelines. FLUX.2 [pro] Pricing Foundry Models are fully hosted and managed on Azure. FLUX.2 [pro] is available through pay-as-you-go and on Global Standard deployment type with the following pricing: Generated image: The first generated megapixel (MP) is charged $0.03. Each subsequent megapixel is charged $0.015. Reference image(s): We charge $0.015 for each megapixel. Important Notes: For pricing, resolution is always rounded up to the next megapixel, separately for each reference image and for the generated image. 1 megapixel is counted as 1024x1024 pixels For multiple reference images, each reference image is counted as 1 megapixel Images exceeding 4 megapixels are resized to 4 megapixels Reference the Foundry Models pricing page for pricing. Build Trustworthy AI Solutions Black Forest Labs models in Foundry Models are delivered under the Microsoft Product Terms, giving you enterprise-grade security and compliance out of the box. Each FLUX endpoint offers Content Safety controls and guardrails. Runtime protections include built-in content-safety filters, role-based access control, virtual-network isolation, and automatic Azure Monitor logging. Governance signals stream directly into Azure Policy, Purview, and Microsoft Sentinel, giving security and compliance teams real-time visibility. Together, Microsoft's capabilities let you create with more confidence, knowing that privacy, security, and safety are woven into every Black Forest Labs deployment from day one. Getting Started with FLUX.2 in Microsoft Foundry If you don’t have an Azure subscription, you can sign up for an Azure account here. Search for the model name in the model catalog in Foundry under “Build.” FLUX.2-pro Open the model card in the model catalog. Click on deploy to obtain the inference API and key. View your deployment under Build > Models. You should land on the deployment page that shows you the API and key in less than a minute. You can try out your prompts in the playground. You can use the API and key with various clients. Learn More ▶️ RSVP for the next Model Monday LIVE on YouTube or On-Demand 👩💻 Explore FLUX.2 Documentation on Microsoft Learn 👋 Continue the conversation on DiscordBYO 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.
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.Open AI’s GPT-5.1-codex-max in Microsoft Foundry: Igniting a New Era for Enterprise Developers
Announcing GPT-5.1-codex-max: The Future of Enterprise Coding Starts Now We’re thrilled to announce the general availability of OpenAI's GPT-5.1-codex-max in Microsoft Foundry Models; a leap forward that redefines what’s possible for enterprise-grade coding agents. This isn’t just another model release; it’s a celebration of innovation, partnership, and the relentless pursuit of developer empowerment. At Microsoft Ignite, we unveiled Microsoft Foundry: a unified platform where businesses can confidently choose the right model for every job, backed by enterprise-grade reliability. Foundry brings together the best from OpenAI, Anthropic, xAI, Black Forest Labs, Cohere, Meta, Mistral, and Microsoft’s own breakthroughs, all under one roof. Our partnership with Anthropic is a testament to our commitment to giving developers access to the most advanced, safe, and high-performing models in the industry. And now, with GPT-5.1-codex-max joining the Foundry family, the possibilities for intelligent applications and agentic workflows have never been greater. GPT 5.1-codex-max is available today in Microsoft Foundry and accessible in Visual Studio Code via the Foundry extension . Meet GPT-5.1-codex-max: Enterprise-Grade Coding Agent for Complex Projects GPT-5.1-codex-max is engineered for those who build the future. Imagine tackling complex, long-running projects without losing context or momentum. GPT-5.1-codex-max delivers efficiency at scale, cross-platform readiness, and proven performance with top scores on SWE-Bench (77.9), the gold standard for AI coding. With GPT-5.1-codex-max, developers can focus on creativity and problem-solving, while the model handles the heavy lifting. GPT-5.1-codex-max isn’t just powerful; it’s practical, designed to solve real challenges for enterprise developers: Multi-Agent Coding Workflows: Automate repetitive tasks across microservices, maintaining shared context for seamless collaboration. Enterprise App Modernization: Effortlessly refactor legacy .NET and Java applications into cloud-native architectures. Secure API Development: Generate and validate secure API endpoints, with `compliance checks built-in for peace of mind. Continuous Integration Support: Integrate GPT-5.1-codex-max into CI/CD pipelines for automated code reviews and test generation, accelerating delivery cycles. These use cases are just the beginning. GPT-5.1-codex-max is your partner in building robust, scalable, and secure solutions. Foundry: Platform Built for Developers Who Build the Future Foundry is more than a model catalog—it’s an enterprise AI platform designed for developers who need choice, reliability, and speed. • Choice Without Compromise: Access the widest range of models, including frontier models from leading model providers. • Enterprise-Grade Infrastructure: Built-in security, observability, and governance for responsible AI at scale. • Integrated Developer Experience: From GitHub to Visual Studio Code, Foundry connects with tools developers love for a frictionless build-to-deploy journey. Start Building Smarter with GPT-5.1-codex-max in Foundry The future is here, and it’s yours to shape. Supercharge your coding workflows with GPT-5.1-codex-max in Microsoft Foundry today. Learn more about Microsoft Foundry: aka.ms/IgniteFoundryModels. Watch Ignite sessions for deep dives and demos: ignite.microsoft.com. Build faster, smarter, and with confidence on the platform redefining enterprise AI.Beyond the Model: Empower your AI with Data Grounding and Model Training
Discover how Microsoft Foundry goes beyond foundational models to deliver enterprise-grade AI solutions. Learn how data grounding, model tuning, and agentic orchestration unlock faster time-to-value, improved accuracy, and scalable workflows across industries.Introducing OpenAI’s GPT-image-1.5 in Microsoft Foundry
Developers building with visual AI can often run into the same frustrations: images that drift from the prompt, inconsistent object placement, text that renders unpredictably, and editing workflows that break when iterating on a single asset. That’s why we are excited to announce OpenAI's GPT Image 1.5 is now generally available in Microsoft Foundry. This model can bring sharper image fidelity, stronger prompt alignment, and faster image generation that supports iterative workflows. Starting today, customers can request access to the model and start building in the Foundry platform. Meet GPT Image 1.5 AI driven image generation began with early models like OpenAI's DALL-E, which introduced the ability to transform text prompts into visuals. Since then, image generation models have been evolving to enhance multimodal AI across industries. GPT Image 1.5 represents continuous improvement in enterprise-grade image generation. Building on the success of GPT Image 1 and GPT Image 1 mini, these enhanced models introduce advanced capabilities that cater to both creative and operational needs. The new image models offer: Text-to-image: Stronger instruction following and highly precise editing. Image-to-image: Transform existing images to iteratively refine specific regions Improved visual fidelity: More detailed scenes and realistic rendering. Accelerated creation times: Up to 4x faster generation speed. Enterprise integration: Deploy and scale securely in Microsoft Foundry. GPT Image 1.5 delivers stronger image preservation and editing capabilities, maintaining critical details like facial likeness, lighting, composition, and color tone across iterative changes. You’ll see more consistent preservation of branded logos and key visuals, making it especially powerful for marketing, brand design, and ecommerce workflows—from graphics and logo creation to generating full product catalogs (variants, environments, and angles) from a single source image. Benchmarks Based on an internal Microsoft dataset, GPT Image 1.5 performs higher than other image generation models in prompt alignment and infographics tasks. It focuses on making clear, strong edits – performing best on single-turn modification, delivering the higher visual quality in both single and multi-turn settings. The following results were found across image generation and editing: Text to image Prompt alignment Diagram / Flowchart GPT Image 1.5 91.2% 96.9% GPT Image 1 87.3% 90.0% Qwen Image 83.9% 33.9% Nano Banana Pro 87.9% 95.3% Image editing Evaluation Aspect Modification Preservation Visual Quality Face Preservation Metrics BinaryEval SC (semantic) DINO (Visual) BinaryEval AuraFace Single-turn GPT image 1 99.2% 51.0% 0.14 79.5% 0.30 Qwen image 81.9% 63.9% 0.44 76.0% 0.85 GPT Image 1.5 100% 56.77% 0.14 89.96% 0.39 Multi-turn GPT Image 1 93.5% 54.7% 0.10 82.8% 0.24 Qwen image 77.3% 68.2% 0.43 77.6% 0.63 GPT image 1.5 92.49% 60.55% 0.15 89.46% 0.28 Using GPT Image 1.5 across industries Whether you’re creating immersive visuals for campaigns, accelerating UI and product design, or producing assets for interactive learning GPT Image 1.5 gives modern enterprises the flexibility and scalability they need. Image models can allow teams to drive deeper engagement through compelling visuals, speed up design cycles for apps, websites, and marketing initiatives, and support inclusivity by generating accessible, high‑quality content for diverse audiences. Watch how Foundry enables developers to iterate with multimodal AI across Black Forest Labs, OpenAI, and more: Microsoft Foundry empowers organizations to deploy these capabilities at scale, integrating image generation seamlessly into enterprise workflows. Explore the use of AI image generation here across industries like: Retail: Generate product imagery for catalogs, e-commerce listings, and personalized shopping experiences. Marketing: Create campaign visuals and social media graphics. Education: Develop interactive learning materials or visual aids. Entertainment: Edit storyboards, character designs, and dynamic scenes for films and games. UI/UX: Accelerate design workflows for apps and websites. Microsoft Foundry provides security and compliance with built-in content safety filters, role-based access, network isolation, and Azure Monitor logging. Integrated governance via Azure Policy, Purview, and Sentinel gives teams real-time visibility and control, so privacy and safety are embedded in every deployment. Learn more about responsible AI at Microsoft. Pricing Model Pricing (per 1M tokens) - Global GPT-image-1.5 Input Tokens: $8 Cached Input Tokens: $2 Output Tokens: $32 Cost efficiency improves as well: image inputs and outputs are now cheaper compared to GPT Image 1, enabling organizations to generate and iterate on more creative assets within the same budget. For detailed pricing, refer here. Getting started Learn more about image generation, explore code samples, and read about responsible AI protections here. Try GPT Image 1.5 in Microsoft Foundry and start building multimodal experiences today. Whether you’re designing educational materials, crafting visual narratives, or accelerating UI workflows, these models deliver the flexibility and performance your organization needs.Integrate Custom Azure AI Agents with Copilot Studio and M365 Copilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your Copilot Studio agent. To get started, navigate to the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Make sure you are in the same environment you used to create your cloud flow. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your Copilot Studio solutions? Check out this blog
