Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!New Azure Open AI models bring fast, expressive, and real‑time AI experiences in Microsoft Foundry
Modern AI applications, whether voice‑first experiences or building large software systems, rarely fit into a single prompt. Real work unfolds over time: maintaining context, following instructions, invoking tools, and adapting as requirements evolve. When these foundations break down through latency spikes, instruction drift, or unreliable tool calls, both user conversations and developer workflows are impacted. OpenAI’s latest models address this shared challenge by prioritizing continuity and reliability across real‑time interaction and long‑running engineering tasks. Starting today, GPT-Realtime-1.5, GPT-Audio-1.5, and GPT-5.3-Codex are rolling out into Microsoft Foundry. Together, these models reflect the growing needs of the modern developer and push the needle from short, stateless interactions toward AI systems that can reason, act, and collaborate over time. GPT-5.3-Codex at a glance GPT‑5.3‑Codex brings together advanced coding capability with broader reasoning and professional problem solving in a single model built for real engineering work. It unifies the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT5.2 in one system. This shifts the experience from optimizing isolated outputs to supporting longer running development efforts; where repositories are large, changes span multiple steps, and requirements aren’t always fully specified at the start. What’s improved Model experiences 25% faster execution time, according to Open AI, than its predecessors so developers can accelerate development of new applications. Built for long-running tasks that involve research, tool use, and complex, multi‑step execution while maintaining context. Midtask steerability and frequent updates allow developers to redirect and collaborate with the model as it works without losing context. Stronger computer-use capabilities allow developers to execute across the full spectrum of technical work. Common use cases Developers and teams can apply GPT‑5.3‑Codex across a wide range of scenarios, including: Refactoring and modernizing large or legacy applications Performing multi‑step migrations or upgrades Running agentic developer workflows that span analysis, implementation, testing, and remediation Automating code reviews, test generation, and defect detection Supporting development in security‑sensitive or regulated environments Pricing Model Input Price/1M Tokens Cached Input Price/1M Tokens Output Price/1M Tokens GPT-5.3-Codex $1.75 $0.175 $14.00 GPT-Realtime-1.5 and GPT-Audio-1.5 at a glance The models deliver measurable gains in reasoning and speech understanding for real‑time voice interactions on Microsoft Foundry. In OpenAI’s evaluations, it shows a +5% lift on Big Bench Audio (reasoning), a +10.23% improvement in alphanumeric transcription, and a +7% gain in instruction following, while maintaining low‑latency performance. Key improvements include: What's improved More natural‑sounding speech: Audio output is smoother and more conversational, with improved pacing and prosody. Higher audio quality: Clearer, more consistent audio output across supported voices. Improved instruction following: Better alignment with developer‑provided system and user instructions during live interactions. Function calling support: Enables structured, tool‑driven interactions within real‑time audio flows. Common use cases Developers are using GPT-Realtime-1.5 and GPT-Audio-1.5 for scenarios where low‑latency voice interaction is essential, including: Conversational voice agents for customer support or internal help desks Voice‑enabled assistants embedded in applications or devices Live voice interfaces for kiosks, demos, and interactive experiences Hands‑free workflows where audio input and output replace keyboard interaction Pricing Model Text Audio Image Input Cached Input Output Input Cached Input Output Input Cached Input Output GPT-Realtime-1.5 $4.00 $0.04 $16.0 $32.0 $0.40 $64.00 $4.00 $0.04 $16.0 GPT-Audio-1.5 $2.50 n/a $10.0 $32.00 n/a $64.00 $2.50 n/a $10.0 Getting started in Microsoft Foundry Start building in Microsoft Foundry, evaluate performance, and explore Azure Open AI models today. Foundry brings evaluation, deployment, and governance into a single workflow, helping teams progress from experiments to scalable applications while maintaining security and operational controls.Building Knowledge-Grounded Conversational AI Agents with Azure Speech Photo Avatars
From Chat to Presence: The Next Step in Conversational AI Chat agents are now embedded across nearly every industry, from customer support on websites to direct integrations inside business applications designed to boost efficiency and productivity. As these agents become more capable and more visible, user expectations are also rising: conversations should feel natural, trustworthy, and engaging. While text‑only chat agents work well for many scenarios, voice‑enabled agents take a meaningful step forward by introducing a clearer persona and a stronger sense of presence, making interactions feel more human and intuitive (see healow Genie success story). In domains such as Retail, Healthcare, Education, and Corporate Training, adding a visual dimension through AI avatars further elevates the experience. Pairing voice with a lifelike visual representation improves inclusiveness, reduces interaction friction, and helps users better contextualize conversations—especially in scenarios that rely on trust, guidance, or repeated engagement. To support these experiences, Microsoft offers two AI avatar options through Azure Speech: Video Avatars, which are generally available and provide full‑ or partial‑body immersive representations, and Photo Avatars, currently in public preview, which deliver a headshot‑style visual well suited for web‑based agents and digital twin scenarios. Both options support custom avatars, enabling organizations to reflect their brand identity rather than relying solely on generic representations (see W2M custom video avatar). Choosing between Video Avatars and Photo Avatars is less about preference and more about intent. Video Avatars offer higher visual fidelity and immersion but require more extensive onboarding, such as high-quality recorded video of an avatar talent. Photo Avatars, by contrast, can be created from a single image, enabling a lighter‑weight onboarding process while still delivering a human‑centered experience. The right choice depends on the desired interaction style, visual presence, and target deployment scenario. What this solution demonstrates In this post, I walk through how to integrate Azure Speech Photo Avatars — powered by Microsoft Research's VASA-1 model — into a knowledge‑grounded conversational AI agent built on Azure AI Search. The goal is to show how voice, visuals, and retrieval‑augmented generation (RAG) can come together to create a more natural and engaging agent experience. The solution exposes a web‑based interface where users can speak naturally to the AI agent using their voice. The agent responds in real time using synthesized speech, while live transcriptions of the conversation are displayed in the UI to improve clarity and accessibility. To help compare different interaction patterns, the sample application supports three modes: 1) Photo Avatar mode, which adds a lifelike visual presence. 2) Video Avatar mode, which provides a more immersive, full‑motion experience. 3) Voice‑only mode, which focuses purely on speech‑to‑speech interaction. Key architectural components An end‑to‑end architecture for the solution is shown in the diagram below. The solution is composed of the following core services and building blocks: Microsoft Foundry — provides the platform for deploying, managing, and accessing the foundation models used by the application. Azure OpenAI — provides the Realtime API for speech‑to‑speech interaction in the voice‑only mode and the Chat Completions API used by backend services for reasoning and conversational responses. gpt‑4.1 — LLM used for reasoning tasks such as deciding when to invoke tool calls and summarizing responses. gpt-realtime-mini — LLM used for speech-to-speech interaction in the Voice-only mode. text‑embedding‑3‑large — LLM used for generating vector embeddings used in retrieval‑augmented generation. Azure Speech — delivers the real‑time speech‑to‑text (STT), text‑to‑speech (TTS), and AI avatars capabilities for both Photo Avatar and Video Avatar experiences. Azure Document Intelligence — extracts structured text, layout, and key information from source documents used to build the knowledge base. Azure AI Search — provides vector‑based retrieval to ground the language model with relevant, context‑aware content. Azure Container Apps — hosts the web UI frontend, backend services, and MCP server within a managed container runtime. Azure Container Apps Environment — defines a secure and isolated boundary for networking, scaling, and observability of the containerized workloads. Azure Container Registry — stores and manages Docker images used by the container applications. How you can try it yourself The complete sample implementation is available in the LiveChat AI Voice Assistant repository, which includes instructions for deploying the solution into your Azure environment. The repository uses Infrastructure as Code (IaC) deployment via Azure Developer CLI (azd) to orchestrate Azure resource provisioning and application deployment. Prerequisites: An Azure subscription with appropriate services and models' quota is required to deploy the solution. Getting the solution up and running in just three simple steps: Clone the repository and navigate to the project git clone https://github.com/mardianto-msft/azure-speech-ai-avatars.git cd azure-speech-ai-avatars Authenticate with Azure azd auth login Initialize and deploy the solution azd up Once deployed, you can access the sample application by opening the frontend service URL in a web browser. To demonstrate knowledge grounding, the sample includes source documents derived from Microsoft’s 2025 Annual Report and Shareholder Letter. These grounding documents can optionally be replaced with your own data, allowing the same architecture to be reused for domain‑specific or enterprise scenarios. When using the provided sample documents, you can ask questions such as: “How much was Microsoft’s net income in 2025?”, “What are Microsoft’s priorities according to the shareholder letter?”, “Who is Microsoft’s CEO?” Bringing Conversational AI Agents to Life This implementation of Azure Speech Photo Avatars serves as a practical starting point for building more engaging, knowledge‑grounded conversational AI agents. By combining voice interaction, visual presence, and retrieval‑augmented generation, Photo Avatars offer a lightweight yet powerful way to make AI agents feel more approachable, trustworthy, and human‑centered — especially in web‑based and enterprise scenarios. From here, the solution can be extended over time with capabilities such as long‑term memory, richer personalization, or more advanced multi‑agent orchestration. Whether used as a reference architecture or as the foundation for a production system, this approach demonstrates how Azure Speech Photo Avatars can help bridge the gap between conversational intelligence and meaningful user experience. By emphasizing accessibility, trust, and human‑centered design, it reflects Microsoft’s broader mission to empower every person and every organization on the planet to achieve more.How to Set Up Claude Code with Microsoft Foundry Models on macOS
Introduction Building with AI isn't just about picking a smart model. It is about where that model lives. I chose to route my Claude Code setup through Microsoft Foundry because I needed more than just a raw API. I wanted the reliability, compliance, and structured management that comes with Microsoft's ecosystem. When you are moving from a prototype to something real, having that level of infrastructure backing your calls makes a significant difference. The challenge is that Foundry is designed for enterprise cloud environments, while my daily development work happens locally on a MacBook. Getting the two to communicate seamlessly involved navigating a maze of shell configurations and environment variables that weren't immediately obvious. I wrote this guide to document the exact steps for bridging that gap. Here is how you can set up Claude Code to run locally on macOS while leveraging the stability of models deployed on Microsoft Foundry. Requirements Before we open the terminal, let's make sure you have the necessary accounts and environments ready. Since we are bridging a local CLI with an enterprise cloud setup, having these credentials handy now will save you time later. Azure Subscription with Microsoft Foundry Setup - This is the most critical piece. You need an active Azure subscription where the Microsoft Foundry environment is initialized. Ensure that you have deployed the Claude model you intend to use and that the deployment status is active. You will need the specific endpoint URL and the associated API keys from this deployment to configure the connection. An Anthropic User Account - Even though the compute is happening on Azure, the interface requires an Anthropic account. You will need this to authenticate your session and manage your user profile settings within the Claude Code ecosystem. Claude Code Client on macOS - We will be running the commands locally, so you need the Claude Code CLI installed on your MacBook. Step 1: Install Claude Code on macOS The recommended installation method is via Homebrew or Curl, which sets it up for terminal access ("OS level"). Option A: Homebrew (Recommended) brew install --cask claude-code Option B: Curl curl -fsSL https://claude.ai/install.sh | bash Verify Installation: Run claude --version. Step 2: Set Up Microsoft Foundry to deploy Claude model Navigate to your Microsoft Foundry portal, and find the Claude model catalog, and deploy the selected Claude model. [Microsoft Foundry > My Assets > Models + endpoints > + Deploy Model > Deploy Base model > Search for "Claude"] In your Model Deployment dashboard, go to the deployed Claude Models and get the "Endpoints and keys". Store it somewhere safe, because we will need them to configure Claude Code later on. Configure Environment Variables in MacOS terminal: Now we need to tell your local Claude Code client to route requests through Microsoft Foundry instead of the default Anthropic endpoints. This is handled by setting specific environment variables that act as a bridge between your local machine and your Azure resources. You could run these commands manually every time you open a terminal, but it is much more efficient to save them permanently in your shell profile. For most modern macOS users, this file is .zshrc. Open your terminal and add the following lines to your profile, making sure to replace the placeholder text with your actual Azure credentials: export CLAUDE_CODE_USE_FOUNDRY=1 export ANTHROPIC_FOUNDRY_API_KEY="your-azure-api-key" export ANTHROPIC_FOUNDRY_RESOURCE="your-resource-name" # Specify the deployment name for Opus export CLAUDE_CODE_MODEL="your-opus-deployment-name" Once you have added these variables, you need to reload your shell configuration for the changes to take effect. Run the source command below to update your current session, and then verify the setup by launching Claude: source ~/.zshrc claude If everything is configured correctly, the Claude CLI will initialize using your Microsoft Foundry deployment as the backend. Once you execute the claude command, the CLI will prompt you to choose an authentication method. Select Option 2 (Antrophic Console account) to proceed. This action triggers your default web browser and redirects you to the Claude Console. Simply sign in using your standard Anthropic account credentials. After you have successfully signed in, you will be presented with a permissions screen. Click the Authorize button to link your web session back to your local terminal. Return to your terminal window, and you should see a notification confirming that the login process is complete. Press Enter to finalize the setup. You are now fully connected. You can start using Claude Code locally, powered entirely by the model deployment running in your Microsoft Foundry environment. Conclusion Setting up this environment might seem like a heavy lift just to run a CLI tool, but the payoff is significant. You now have a workflow that combines the immediate feedback of local development with the security and infrastructure benefits of Microsoft Foundry. One of the most practical upgrades is the removal of standard usage caps. You are no longer limited to the 5-hour API call limits, which gives you the freedom to iterate, test, and debug for as long as your project requires without hitting a wall. By bridging your local macOS terminal to Azure, you are no longer just hitting an API endpoint. You are leveraging a managed, compliance-ready environment that scales with your needs. The best part is that now the configuration is locked in, you don't need to think about the plumbing again. You can focus entirely on coding, knowing that the reliability of an enterprise platform is running quietly in the background supporting every command.Building with Azure OpenAI Sora: A Complete Guide to AI Video Generation
In this comprehensive guide, we'll explore how to integrate both Sora 1 and Sora 2 models from Azure OpenAI Service into a production web application. We'll cover API integration, request body parameters, cost analysis, limitations, and the key differences between using Azure AI Foundry endpoints versus OpenAI's native API. Table of Contents Introduction to Sora Models Azure AI Foundry vs. OpenAI API Structure API Integration: Request Body Parameters Video Generation Modes Cost Analysis per Generation Technical Limitations & Constraints Resolution & Duration Support Implementation Best Practices Introduction to Sora Models Sora is OpenAI's groundbreaking text-to-video model that generates realistic videos from natural language descriptions. Azure AI Foundry provides access to two versions: Sora 1: The original model focused primarily on text-to-video generation with extensive resolution options (480p to 1080p) and flexible duration (1-20 seconds) Sora 2: The enhanced version with native audio generation, multiple generation modes (text-to-video, image-to-video, video-to-video remix), but more constrained resolution options (720p only in public preview) Azure AI Foundry vs. OpenAI API Structure Key Architectural Differences Sora 1 uses Azure's traditional deployment-based API structure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/... Parameters: Uses Azure-specific naming like n_seconds, n_variants, separate width/height fields Job Management: Uses /jobs/{id} for status polling Content Download: Uses /video/generations/{generation_id}/content/video Sora 2 adapts OpenAI's v1 API format while still being hosted on Azure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/videos Parameters: Uses OpenAI-style naming like seconds (string), size (combined dimension string like "1280x720") Job Management: Uses /videos/{video_id} for status polling Content Download: Uses /videos/{video_id}/content Why This Matters? This architectural difference requires conditional request formatting in your code: const isSora2 = deployment.toLowerCase().includes('sora-2'); if (isSora2) { requestBody = { model: deployment, prompt, size: `${width}x${height}`, // Combined format seconds: duration.toString(), // String type }; } else { requestBody = { model: deployment, prompt, height, // Separate dimensions width, n_seconds: duration.toString(), // Azure naming n_variants: variants, }; } API Integration: Request Body Parameters Sora 1 API Parameters Standard Text-to-Video Request: { "model": "sora-1", "prompt": "Wide shot of a child flying a red kite in a grassy park, golden hour sunlight, camera slowly pans upward.", "height": "720", "width": "1280", "n_seconds": "12", "n_variants": "2" } Parameter Details: model (String, Required): Your Azure deployment name prompt (String, Required): Natural language description of the video (max 32000 chars) height (String, Required): Video height in pixels width (String, Required): Video width in pixels n_seconds (String, Required): Duration (1-20 seconds) n_variants (String, Optional): Number of variations to generate (1-4, constrained by resolution) Sora 2 API Parameters Text-to-Video Request: { "model": "sora-2", "prompt": "A serene mountain landscape with cascading waterfalls, cinematic drone shot", "size": "1280x720", "seconds": "12" } Image-to-Video Request (uses FormData): const formData = new FormData(); formData.append('model', 'sora-2'); formData.append('prompt', 'Animate this image with gentle wind movement'); formData.append('size', '1280x720'); formData.append('seconds', '8'); formData.append('input_reference', imageFile); // JPEG/PNG/WebP Video-to-Video Remix Request: Endpoint: POST .../videos/{video_id}/remix Body: Only { "prompt": "your new description" } The original video's structure, motion, and framing are reused while applying the new prompt Parameter Details: model (String, Optional): Your deployment name prompt (String, Required): Video description size (String, Optional): Either "720x1280" or "1280x720" (defaults to "720x1280") seconds (String, Optional): "4", "8", or "12" (defaults to "4") input_reference (File, Optional): Reference image for image-to-video mode remix_video_id (String, URL parameter): ID of video to remix Video Generation Modes 1. Text-to-Video (Both Models) The foundational mode where you provide a text prompt describing the desired video. Implementation: const response = await fetch(endpoint, { method: 'POST', headers: { 'Content-Type': 'application/json', 'api-key': apiKey, }, body: JSON.stringify({ model: deployment, prompt: "A train journey through mountains with dramatic lighting", size: "1280x720", seconds: "12", }), }); Best Practices: Include shot type (wide, close-up, aerial) Describe subject, action, and environment Specify lighting conditions (golden hour, dramatic, soft) Add camera movement if desired (pans, tilts, tracking shots) 2. Image-to-Video (Sora 2 Only) Generate a video anchored to or starting from a reference image. Key Requirements: Supported formats: JPEG, PNG, WebP Image dimensions must exactly match the selected video resolution Our implementation automatically resizes uploaded images to match Implementation Detail: // Resize image to match video dimensions const targetWidth = parseInt(width); const targetHeight = parseInt(height); const resizedImage = await resizeImage(inputReference, targetWidth, targetHeight); // Send as multipart/form-data formData.append('input_reference', resizedImage); 3. Video-to-Video Remix (Sora 2 Only) Create variations of existing videos while preserving their structure and motion. Use Cases: Change weather conditions in the same scene Modify time of day while keeping camera movement Swap subjects while maintaining composition Adjust artistic style or color grading Endpoint Structure: POST {base_url}/videos/{original_video_id}/remix?api-version=2024-08-01-preview Implementation: let requestEndpoint = endpoint; if (isSora2 && remixVideoId) { const [baseUrl, queryParams] = endpoint.split('?'); const root = baseUrl.replace(/\/videos$/, ''); requestEndpoint = `${root}/videos/${remixVideoId}/remix${queryParams ? '?' + queryParams : ''}`; } Cost Analysis per Generation Sora 1 Pricing Model Base Rate: ~$0.05 per second per variant at 720p Resolution Scaling: Cost scales linearly with pixel count Formula: const basePrice = 0.05; const basePixels = 1280 * 720; // Reference resolution const currentPixels = width * height; const resolutionMultiplier = currentPixels / basePixels; const totalCost = basePrice * duration * variants * resolutionMultiplier; Examples: 720p (1280×720), 12 seconds, 1 variant: $0.60 1080p (1920×1080), 12 seconds, 1 variant: $1.35 720p, 12 seconds, 2 variants: $1.20 Sora 2 Pricing Model Flat Rate: $0.10 per second per variant (no resolution scaling in public preview) Formula: const totalCost = 0.10 * duration * variants; Examples: 720p (1280×720), 4 seconds: $0.40 720p (1280×720), 12 seconds: $1.20 720p (720×1280), 8 seconds: $0.80 Note: Since Sora 2 currently only supports 720p in public preview, resolution doesn't affect cost, only duration matters. Cost Comparison Scenario Sora 1 (720p) Sora 2 (720p) Winner 4s video $0.20 $0.40 Sora 1 12s video $0.60 $1.20 Sora 1 12s + audio N/A (no audio) $1.20 Sora 2 (unique) Image-to-video N/A $0.40-$1.20 Sora 2 (unique) Recommendation: Use Sora 1 for cost-effective silent videos at various resolutions. Use Sora 2 when you need audio, image/video inputs, or remix capabilities. Technical Limitations & Constraints Sora 1 Limitations Resolution Options: 9 supported resolutions from 480×480 to 1920×1080 Includes square, portrait, and landscape formats Full list: 480×480, 480×854, 854×480, 720×720, 720×1280, 1280×720, 1080×1080, 1080×1920, 1920×1080 Duration: Flexible: 1 to 20 seconds Any integer value within range Variants: Depends on resolution: 1080p: Variants disabled (n_variants must be 1) 720p: Max 2 variants Other resolutions: Max 4 variants Concurrent Jobs: Maximum 2 jobs running simultaneously Job Expiration: Videos expire 24 hours after generation Audio: No audio generation (silent videos only) Sora 2 Limitations Resolution Options (Public Preview): Only 2 options: 720×1280 (portrait) or 1280×720 (landscape) No square formats No 1080p support in current preview Duration: Fixed options only: 4, 8, or 12 seconds No custom durations Defaults to 4 seconds if not specified Variants: Not prominently supported in current API documentation Focus is on single high-quality generations with audio Concurrent Jobs: Maximum 2 jobs (same as Sora 1) Job Expiration: 24 hours (same as Sora 1) Audio: Native audio generation included (dialogue, sound effects, ambience) Shared Constraints Concurrent Processing: Both models enforce a limit of 2 concurrent video jobs per Azure resource. You must wait for one job to complete before starting a third. Job Lifecycle: queued → preprocessing → processing/running → completed Download Window: Videos are available for 24 hours after completion. After expiration, you must regenerate the video. Generation Time: Typical: 1-5 minutes depending on resolution, duration, and API load Can occasionally take longer during high demand Resolution & Duration Support Matrix Sora 1 Support Matrix Resolution Aspect Ratio Max Variants Duration Range Use Case 480×480 Square 4 1-20s Social thumbnails 480×854 Portrait 4 1-20s Mobile stories 854×480 Landscape 4 1-20s Quick previews 720×720 Square 4 1-20s Instagram posts 720×1280 Portrait 2 1-20s TikTok/Reels 1280×720 Landscape 2 1-20s YouTube shorts 1080×1080 Square 1 1-20s Premium social 1080×1920 Portrait 1 1-20s Premium vertical 1920×1080 Landscape 1 1-20s Full HD content Sora 2 Support Matrix Resolution Aspect Ratio Duration Options Audio Generation Modes 720×1280 Portrait 4s, 8s, 12s ✅ Yes Text, Image, Video Remix 1280×720 Landscape 4s, 8s, 12s ✅ Yes Text, Image, Video Remix Note: Sora 2's limited resolution options in public preview are expected to expand in future releases. Implementation Best Practices 1. Job Status Polling Strategy Implement adaptive backoff to avoid overwhelming the API: const maxAttempts = 180; // 15 minutes max let attempts = 0; const baseDelayMs = 3000; // Start with 3 seconds while (attempts < maxAttempts) { const response = await fetch(statusUrl, { headers: { 'api-key': apiKey }, }); if (response.status === 404) { // Job not ready yet, wait longer const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; continue; } const job = await response.json(); // Check completion (different status values for Sora 1 vs 2) const isCompleted = isSora2 ? job.status === 'completed' : job.status === 'succeeded'; if (isCompleted) break; // Adaptive backoff const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; } 2. Handling Different Response Structures Sora 1 Video Download: const generations = Array.isArray(job.generations) ? job.generations : []; const genId = generations[0]?.id; const videoUrl = `${root}/${genId}/content/video`; Sora 2 Video Download: const videoUrl = `${root}/videos/${jobId}/content`; 3. Error Handling try { const response = await fetch(endpoint, fetchOptions); if (!response.ok) { const error = await response.text(); throw new Error(`Video generation failed: ${error}`); } // ... handle successful response } catch (error) { console.error('[VideoGen] Error:', error); // Implement retry logic or user notification } 4. Image Preprocessing for Image-to-Video Always resize images to match the target video resolution: async function resizeImage(file: File, targetWidth: number, targetHeight: number): Promise<File> { return new Promise((resolve, reject) => { const img = new Image(); const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); img.onload = () => { canvas.width = targetWidth; canvas.height = targetHeight; ctx.drawImage(img, 0, 0, targetWidth, targetHeight); canvas.toBlob((blob) => { if (blob) { const resizedFile = new File([blob], file.name, { type: file.type }); resolve(resizedFile); } else { reject(new Error('Failed to create resized image blob')); } }, file.type); }; img.onerror = () => reject(new Error('Failed to load image')); img.src = URL.createObjectURL(file); }); } 5. Cost Tracking Implement cost estimation before generation and tracking after: // Pre-generation estimate const estimatedCost = calculateCost(width, height, duration, variants, soraVersion); // Save generation record await saveGenerationRecord({ prompt, soraModel: soraVersion, duration: parseInt(duration), resolution: `${width}x${height}`, variants: parseInt(variants), generationMode: mode, estimatedCost, status: 'queued', jobId: job.id, }); // Update after completion await updateGenerationStatus(jobId, 'completed', { videoId: finalVideoId }); 6. Progressive User Feedback Provide detailed status updates during the generation process: const statusMessages: Record<string, string> = { 'preprocessing': 'Preprocessing your request...', 'running': 'Generating video...', 'processing': 'Processing video...', 'queued': 'Job queued...', 'in_progress': 'Generating video...', }; onProgress?.(statusMessages[job.status] || `Status: ${job.status}`); Conclusion Building with Azure OpenAI's Sora models requires understanding the nuanced differences between Sora 1 and Sora 2, both in API structure and capabilities. Key takeaways: Choose the right model: Sora 1 for resolution flexibility and cost-effectiveness; Sora 2 for audio, image inputs, and remix capabilities Handle API differences: Implement conditional logic for parameter formatting and status polling based on model version Respect limitations: Plan around concurrent job limits, resolution constraints, and 24-hour expiration windows Optimize costs: Calculate estimates upfront and track actual usage for better budget management Provide great UX: Implement adaptive polling, progressive status updates, and clear error messages The future of AI video generation is exciting, and Azure AI Foundry provides production-ready access to these powerful models. As Sora 2 matures and limitations are lifted (especially resolution options), we'll see even more creative applications emerge. Resources: Azure AI Foundry Sora Documentation OpenAI Sora API Reference Azure OpenAI Service Pricing This blog post is based on real-world implementation experience building LemonGrab, my AI video generation platform that integrates both Sora 1 and Sora 2 through Azure AI Foundry. The code examples are extracted from production usage.Announcing GPT‑5.2‑Codex in Microsoft Foundry: Enterprise‑Grade AI for Secure Software Engineering
Enterprise developers know the grind: wrestling with legacy code, navigating complex dependency challenges, and waiting on security reviews that stall releases. OpenAI’s GPT‑5.2‑Codex flips that equation and helps engineers ship faster without cutting corners. It’s not just autocomplete; it’s a reasoning engine for real-world software engineering. Generally available starting today through Azure OpenAI in Microsoft Foundry Models, GPT‑5.2‑Codex is built for the realities of enterprise codebases, large repos, evolving requirements, and security constraints that can’t be overlooked. As OpenAI’s most advanced agentic coding model, it brings sustained reasoning, and security-aware assistance directly into the workflows enterprise developers already rely on with Microsoft’s secure and reliable infrastructure. GPT-5.2-Codex at a Glance GPT‑5.2‑Codex is designed for how software gets built in enterprise teams. You start with imperfect inputs including legacy code, partial docs, screenshots, diagrams, and work through multi‑step changes, reviews, and fixes. The model helps keep context, intent, and standards intact across that entire lifecycle, so teams can move faster without sacrificing quality or security. What it enables Work across code and artifacts: Reason over source code alongside screenshots, architecture diagrams, and UI mocks — so implementation stays aligned with design intent. Stay productive in long‑running tasks: Maintain context across migrations, refactors, and investigations, even as requirements evolve. Build and review with security in mind: Get practical support for secure coding patterns, remediation, reviews, and vulnerability analysis — where correctness matters as much as speed. Feature Specs (quick reference) Context window: 400K tokens (approximately 100K lines of code) Supported languages: 50+ including Python, JavaScript/TypeScript, C#, Java, Go, Rust Multimodal inputs: Code, images (UI mocks, diagrams), and natural language API compatibility: Drop-in replacement for existing Codex API calls Use cases where it really pops Legacy modernization with guardrails: Safely migrate and refactor “untouchable” systems by preserving behavior, improving structure, and minimizing regression risk. Large‑scale refactors that don’t lose intent: Execute cross‑module updates and consistency improvements without the typical “one step forward, two steps back” churn. AI‑assisted code review that raises the floor: Catch risky patterns, propose safer alternatives, and improve consistency, especially across large teams and long‑lived codebases. Defensive security workflows at scale: Accelerate vulnerability triage, dependency/path analysis, and remediation when speed matters, but precision matters more. Lower cognitive load in long, multi‑step builds: Keep momentum across multi‑hour sessions: planning, implementing, validating, and iterating with context intact. Pricing Model Input Price/1M Tokens Cached Input Price/1M Tokens Output Price/1M Tokens GPT-5.2-Codex $1.75 $0.175 $14.00 Security Aware by Design, not as an Afterthought For many organizations, AI adoption hinges on one nonnegotiable question: Can this be trusted in security sensitive workflows? GPT-5.2-Codex meaningfully advances the Codex lineage in this area. As models grow more capable, we’ve seen that general reasoning improvements naturally translate into stronger performance in specialized domains — including defensive cybersecurity. With GPT‑5.2‑Codex, this shows up in practical ways: Improved ability to analyze unfamiliar code paths and dependencies Stronger assistance with secure coding patterns and remediation More dependable support during code reviews, vulnerability investigations, and incident response At the same time, Microsoft continues to deploy these capabilities thoughtfully balancing access, safeguards, and platform level controls so enterprises can adopt AI responsibly as capabilities evolve. Why Run GPT-5.2-Codex on Microsoft Foundry? Powerful models matter — but where and how they run matters just as much for enterprise. Organizations choose Microsoft Foundry because it combines Foundry frontier AI with Azure enterprise grade fundamentals: Integrated security, compliance, and governance Deploy GPT-5.2-Codex within existing Azure security boundaries, identity systems, and compliance frameworks — without reinventing controls. Enterprise ready orchestration and tooling Build, evaluate, monitor, and scale AI powered developer experiences using the same platform teams already rely on for production workloads. A unified path from experimentation to scale Foundry makes it easier to move from proof of concept to real deployment —without changing platforms, vendors, or operating assumptions. Trust at the platform level For teams working in regulated or security critical environments, Foundry and Azure provide assurances that go beyond the model itself. Together with GitHub Copilot, Microsoft Foundry provides a unified developer experience — from in‑IDE assistance to production‑grade AI workflows — backed by Azure’s security, compliance, and global scale. This is where GPT-5.2-Codex becomes not just impressive but adoptable. Get Started Today Explore GPT‑5.2‑Codex in Microsoft today. Start where you already work: Try GPT‑5.2‑Codex in GitHub Copilot for everyday coding and scale the same model to larger workflows using Azure OpenAI in Microsoft Foundry. Let’s build what’s next with speed and security.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.
How to Reliably Gauge LLM Confidence?
a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } I’m trying to estimate an LLM’s confidence in its answers in a way that correlates with correctness. Self-reported confidence is often misleading, and raw token probabilities mostly reflect fluency rather than truth. I don’t have grounding options like RAG, human feedback, or online search, so I’m looking for approaches that work in this constraint. What techniques have you found effective—entropy-based signals, calibration (temperature scaling), self-evaluation, or others? Any best practices for making confidence scores actionable?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.Staying in the flow: SleekFlow and Azure turn customer conversations into conversions
A customer adds three items to their cart but never checks out. Another asks about shipping, gets stuck waiting eight minutes, only to drop the call. A lead responds to an offer but is never followed up with in time. Each of these moments represents lost revenue, and they happen to businesses every day. SleekFlow was founded in 2019 to help companies turn those almost-lost-customer moments into connection, retention, and growth. Today we serve more than 2,000 mid-market and enterprise organizations across industries including retail and e-commerce, financial services, healthcare, travel and hospitality, telecommunications, real estate, and professional services. In total, those customers rely on SleekFlow to orchestrate more than 600,000 daily customer interactions across WhatsApp, Instagram, web chat, email, and more. Our name reflects what makes us different. Sleek is about unified, polished experiences—consolidating conversations into one intelligent, enterprise-ready platform. Flow is about orchestration—AI and human agents working together to move each conversation forward, from first inquiry to purchase to renewal. The drive for enterprise-ready agentic AI Enterprises today expect always-on, intelligent conversations—but delivering that at scale proved daunting. When we set out to build AgentFlow, our agentic AI platform, we quickly ran into familiar roadblocks: downtime that disrupted peak-hour interactions, vector search delays that hurt accuracy, and costs that ballooned under multi-tenant workloads. Development slowed from limited compatibility with other technologies, while customer onboarding stalled without clear compliance assurances. To move past these barriers, we needed a foundation that could deliver the performance, trust, and global scale enterprises demand. The platform behind the flow: How Azure powers AgentFlow We chose Azure because building AgentFlow required more than raw compute power. Chatbots built on a single-agent model often stall out. They struggle to retrieve the right context, they miss critical handoffs, and they return answers too slowly to keep a customer engaged. To fix that, we needed an ecosystem capable of supporting a team of specialized AI agents working together at enterprise scale. Azure Cosmos DB provides the backbone for memory and context, managing short-term interactions, long-term histories, and vector embeddings in containers that respond in 15–20 milliseconds. Powered by Azure AI Foundry, our agents use Azure OpenAI models within Azure AI Foundry to understand and generate responses natively in multiple languages. Whether in English, Chinese, or Portuguese, the responses feel natural and aligned with the brand. Semantic Kernel acts as the conductor, orchestrating multiple agents, each of which retrieves the necessary knowledge and context, including chat histories, transactional data, and vector embeddings, directly from Azure Cosmos DB. For example, one agent could be retrieving pricing data, another summarizing it, and a third preparing it for a human handoff. The result is not just responsiveness but accuracy. A telecom provider can resolve a billing question while surfacing an upsell opportunity in the same dialogue. A financial advisor can walk into a call with a complete dossier prepared in seconds rather than hours. A retailer can save a purchase by offering an in-stock substitute before the shopper abandons the cart. Each of these conversations is different, yet the foundation is consistent on AgentFlow. Fast, fluent, and focused: Azure keeps conversations moving Speed is the heartbeat of a good conversation. A delayed answer feels like a dropped call, and an irrelevant one breaks trust. For AgentFlow to keep customers engaged, every operation behind the scenes has to happen in milliseconds. A single interaction can involve dozens of steps. One agent pulls product information from embeddings, another checks it against structured policy data, and a third generates a concise, brand-aligned response. If any of these steps lag, the dialogue falters. On Azure, they don’t. Azure Cosmos DB manages conversational memory and agent state across dedicated containers for short-term exchanges, long-term history, and vector search. Sharded DiskANN indexing powers semantic lookups that resolve in the 15–20 millisecond range—fast enough that the customer never feels a pause. Microsoft Phi’s model Phi-4 as well as Azure OpenAI in Foundry Models like o3-mini and o4-mini, provide the reasoning, and Azure Container Apps scale elastically, so performance holds steady during event-driven bursts, such as campaign broadcasts that can push the platform from a few to thousands of conversations per minute, and during daily peak-hour surges. To support that level of responsiveness, we run Azure Container Apps on the Pay-As-You-Go consumption plan, using KEDA-based autoscaling to expand from five idle containers to more than 160 within seconds. Meanwhile, Microsoft Orleans coordinates lightweight in-memory clustering to keep conversations sleek and flowing. The results are tangible. Retrieval-augmented generation recall improved from 50 to 70 percent. Execution speed is about 50 percent faster. For SleekFlow’s customers, that means carts are recovered before they’re abandoned, leads are qualified in real time, and support inquiries move forward instead of stalling out. With Azure handling the complexity under the hood, conversations flow naturally on the surface—and that’s what keeps customers engaged. Secure enough for enterprises, human enough for customers AgentFlow was built with security-by-design as a first principle, giving businesses confidence that every interaction is private, compliant, and reliable. On Azure, every AI agent operates inside guardrails enterprises can depend on. Azure Cosmos DB enforces strict per-tenant isolation through logical partitioning, encryption, and role-based access control, ensuring chat histories, knowledge bases, and embeddings remain auditable and contained. Models deployed through Azure AI Foundry, including Azure OpenAI and Microsoft Phi, process data entirely within SleekFlow’s Azure environment and guarantees it is never used to train public models, with activity logged for transparency. And Azure’s certifications—including ISO 27001, SOC 2, and GDPR—are backed by continuous monitoring and regional data residency options, proving compliance at a global scale. But trust is more than a checklist of certifications. AgentFlow brings human-like fluency and empathy to every interaction, powered by Azure OpenAI running with high token-per-second throughput so responses feel natural in real time. Quality control isn’t left to chance. Human override workflows are orchestrated through Azure Container Apps and Azure App Service, ensuring AI agents can carry conversations confidently until they’re ready for human agents. Enterprises gain the confidence to let AI handle revenue-critical moments, knowing Azure provides the foundation and SleekFlow provides the human-centered design. Shaping the next era of conversational AI on Azure The benefits of Azure show up not only in customer conversations but also in the way our own teams work. Faster processing speeds and high token-per-second throughput reduce latency, so we spend less time debugging and more time building. Stable infrastructure minimizes downtime and troubleshooting, lowering operational costs. That same reliability and scalability have transformed the way we engineer AgentFlow. AgentFlow started as part of our monolithic system. Shipping new features used to take about a month of development and another week of heavy testing to make sure everything held together. After moving AgentFlow to a microservices architecture on Azure Container Apps, we can now deploy updates almost daily with no down time or customer impact. And this is all thanks to native support for rolling updates and blue-green deployments. This agility is what excites us most about what's ahead. With Azure as our foundation, SleekFlow is not simply keeping pace with the evolution of conversational AI—we are shaping what comes next. Every interaction we refine, every second we save, and every workflow we streamline brings us closer to our mission: keeping conversations sleek, flowing, and valuable for enterprises everywhere.
