Foundry IQ: Unlocking ubiquitous knowledge for agents
Introducing Foundry IQ by Azure AI Search in Microsoft Foundry. Foundry IQ is a centralized knowledge layer that connects agents to data with the next generation of retrieval-augmented generation (RAG). Foundry IQ includes the following features: Knowledge bases: Available directly in the new Foundry portal, knowledge bases are reusable, topic-centric collections that ground multiple agents and applications through a single API. Automated indexed and federated knowledge sources – Expand what data an agent can reach by connecting to both indexed and remote knowledge sources. For indexed sources, Foundry IQ delivers automatic indexing, vectorization, and enrichment for text, images, and complex documents. Agentic retrieval engine in knowledge bases – A self-reflective query engine that uses AI to plan, select sources, search, rank and synthesize answers across sources with configurable “retrieval reasoning effort.” Enterprise-grade security and governance – Support for document-level access control, alignment with existing permissions models, and options for both indexed and remote data. Foundry IQ is available in public preview through the new Foundry portal and Azure portal with Azure AI Search. Foundry IQ is part of Microsoft's intelligence layer with Fabric IQ and Work IQ.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.Introducing OpenAI's GPT-image-2 in Microsoft Foundry
Take a small design team running a global social campaign. They have the creative vision to produce localized imagery for every market, but not the resources to reshoot, reformat, or outsource that scale. Every asset needs to fit a different platform, a different dimension, a different cultural context, and they all need to ship at the same time. This is where flexible image generation comes in handy. OpenAI's GPT-image-2 is now generally available and rolling out today to Microsoft Foundry, introducing a step change in image generation. Developers and designers now get more control over image output, so a small team can execute with the reach and flexibility of a much larger one. What is new in GPT-image-2? GPT-image-2 brings real world intelligence, multilingual understanding, improved instruction following, increased resolution support, and an intelligent routing layer giving developers the tools to scale image generation for production workflows. Real world intelligence GPT-image-2 has a knowledge cut off of December 2025, meaning that it is able to give you more contextually relevant and accurate outputs. The model also comes with enhanced thinking capabilities that allow it to search the web, check its own outputs, and create multiple images from just one prompt. These enhancements shift image generation models away from being simple tools and runs them into creative sidekicks. Multilingual understanding GPT-image-2 includes increased language support across Japanese, Korean, Chinese, Hindi, and Bengali, as well as new thinking capabilities. This means the model can create images and render text that feels localized. Increased resolution support GPT-image-2 introduces 4K resolution support, giving developers the ability to generate rich, detailed, and photorealistic images at custom dimensions. Resolution guidelines to keep in mind: Constraint Detail Total pixel budget Maximum pixels in final image cannot exceed 8,294,400 Minimum pixels in final image cannot be less than 655,360 Requests exceeding this are automatically resized to fit. Resolutions 4K, 1024x1024, 1536x1024, and 1024x1536 Dimension alignment Each dimension must be a multiple of 16 Note: If your requested resolution exceeds the pixel budget, the service will automatically resize it down. Intelligent routing layer GPT-image-2 also includes an expanded routing layer with two distinct modes, allowing the service to intelligently select the right generation configuration for a request without requiring an explicitly set size value. Mode 1 — Legacy size selection In Mode 1, the routing layer selects one of the three legacy size tiers to use for generation: Size tier Description smimage Small image output image Standard image output xlimage Large image output This mode is useful for teams already familiar with the legacy size tiers who want to benefit from automatic selection without making any manual changes. Mode 2 — Token size bucket selection In Mode 2, the routing layer selects from six token size buckets — 16, 24, 36, 48, 64, 96 — which map roughly to the legacy size tiers: Token bucket Approximate legacy size 16, 24 smimage 36, 48 image 64, 96 xlimage This approach can allow for more flexibility in the number of tokens generated, which in turn helps to better optimize output quality and efficiency for a given prompt. See it in action GPT-image-2 shows improved image fidelity across visual styles, generating more detailed and refined images. But, don’t just take our word for it, let's see the model in action with a few prompts and edits. Here is the example we used: Prompt: Interior of an empty subway car (no people). Wide-angle view looking down the aisle. Clean, modern subway car with seats, poles, route map strip, and ad frames above the windows. Realistic lighting with a slight cool fluorescent tone, realistic materials (metal poles, vinyl seats, textured floor). As you can see, when using the same base prompt, the image quality and realism improved with each model. Now let’s take a look at adding incremental changes to the same image: Prompt: Populate the ad frames with a cohesive ad campaign for “Zava Flower Delivery” and use an array of flower types. And our subway is now full of ads for the new ZAVA flower delivery service. Let's ask for another small change: Prompt: In all Zava Flower Delivery advertisements, change the flowers shown to roses (red and pink roses). And in three simple prompts, we've created a mockup of a flower delivery ad. From marketing material to website creation to UX design, GPT-image-2 now allows developers to deliver production-grade assets for real business use cases. Image generation across industries These new capabilities open the door to richer, more production-ready image generation workflows across a range of enterprise scenarios: Retail & e-commerce: Generate product imagery at exact platform-required dimensions, from square thumbnails to wide banners, without post-processing. Marketing: Produce crisp, rich in color campaign visuals and social assets localized to different markets. Media & entertainment: Generate storyboard panels and scene at resolutions suited to production pipelines. Education & training: Create visual learning aids and course materials formatted to exact display requirements across devices. UI/UX design: Accelerate mockup and prototype workflows by generating interface assets at the precise dimensions your design system requires. Trust and safety At Microsoft, our mission to empower people and organizations remains constant. As part of this commitment, models made available through Foundry undergo internal reviews and are deployed with safeguards designed to support responsible use at scale. Learn more about responsible AI at Microsoft. For GPT-image-2, Microsoft applied an in-depth safety approach that addresses disallowed content and misuse while maintaining human oversight. The deployment combines OpenAI’s image generation safety mitigations with Azure AI Content Safety, including filters and classifiers for sensitive content. Pricing Model Offer type Pricing - Image Pricing - Text GPT-image-2 Standard Global Input Tokens: $8 Cached Input Tokens: $2 Output Tokens: $30 Input Tokens: $5 Cached Input Tokens: $1.25 Note: All prices are per 1M token. There is no billing for output tokens for the GPT-image-2 model. Getting started Whether you’re building a personalized retail experience, automating visual content pipelines or accelerating design workflows. GPT-image-2 gives your team the resolution control and intelligent routing to generate images that fit your exact needs. Try the GPT-image-2 in Microsoft Foundry today! Deploy the model in Microsoft Foundry Experiment with the model in the Image playground Read the documentation to learn moreIntroducing OpenAI’s GPT-5.4 mini and GPT-5.4 nano for low-latency AI
Imagine you’re a developer building a research assistant agent on top of GPT‑5.4. The agent retrieves documents, summarizes findings, and answers follow‑up questions across multiple turns. In early testing, the reasoning quality is strong, but as the agent chains together retrieval, tool calls, and generation, latency starts to add up. For interactive experiences, those delays matter—so many teams adopt a multi‑model approach, using a larger model to plan and smaller models to execute subtasks quickly at scale. This is where GPT‑5.4 mini and GPT‑5.4 nano come in. These smaller variants of GPT-5.4 are optimized for developer workloads where latency, cost savings, and agentic design are top of mind. GPT-5.4 mini and GPT-5.4 nano will be rolling out today in Microsoft Foundry, so you can evaluate them in the model catalog and deploy the right option for each workload. GPT-5.4 mini: efficient reasoning for production workflows GPT-5.4 mini distills GPT-5.4’s strengths into a smaller, more efficient model for developer workloads where responsiveness matters. It significantly improves over GPT-5 mini across coding, reasoning, multimodal understanding, and tool use while running about 2X faster. Text and image inputs: build multimodal experiences that combine prompts with screenshots or other images. Tool use and function calling: reliably invoke tools and APIs for agentic workflows. Web search and file search: ground responses in external or enterprise content as part of multi-step tasks. Computer use: support software-interaction loops where the model interprets UI state and takes well-scoped actions. Where GPT-5.4 mini thrives Developer copilots and coding assistants: latency-sensitive coding help, code review suggestions, and fast iteration loops where turnaround time matters. Multimodal developer workflows: applications that interpret screenshots, understand UI state, or process images as part of coding and debugging loops. Computer-use sub-agents: fast executors that take well-scoped actions in software (for example, navigating UIs or completing repetitive steps) within a larger agent loop coordinated by a planner model. GPT-5.4 nano: ultra-low latency automation at scale GPT-5.4 nano is the smallest and fastest model in the lineup, designed for low-latency and low-cost API usage at high throughput. It’s optimized for short-turn tasks like classification, extraction, and ranking, plus lightweight sub-agent work where speed and cost are the priority and extended multi-step reasoning isn’t required. Strong instruction following: consistent adherence to developer intent across short, well-defined interactions. Function and tool calling: dependable invocation of tools and APIs for lightweight agent and automation scenarios. Coding support: optimized performance for common coding tasks where fast turnaround is required. Image understanding: multimodal image input support for basic image interpretation alongside text. Low-latency, low-cost execution: designed to deliver responses quickly and efficiently at scale. Where GPT-5.4 nano thrives GPT-5.4 nano is a strong fit when you need predictable behavior at very high throughput and the task can be expressed as short, well-scoped instructions. Classification and intent detection: fast labeling and routing decisions for high-volume requests. Extraction and normalization: pull structured fields from text, validate formats, and standardize outputs. Ranking and triage: reorder candidates, prioritize tickets/leads, and select best-next actions under tight latency budgets. Guardrails and policy checks: lightweight safety and policy classification, prompt gating, and enforcement decisions before dispatching to tools or larger models. High-volume text processing pipelines: batch transformation, cleanup, deduping, and normalization steps where unit cost and throughput dominate. Routing and prioritization at the edge: select the right downstream workflow (template, queue, or model) for each request under tight latency budgets. Choosing the right GPT-5.4 model Microsoft Foundry makes it possible to deploy multiple GPT-5.4 variants side by side, so teams can route requests to the model that best fits each task. Here’s a practical way to think about the lineup: Model Best suited for Typical workloads GPT-5.4 Sustained, multi-step reasoning with reliable follow-through Agentic workflows, research assistants, document analysis, complex internal tools GPT-5.4 Pro Deeper, higher-reliability reasoning for complex production scenarios High-stakes agentic workflows, long-form analysis and synthesis, complex planning, advanced internal copilots GPT-5.4 mini Balanced reasoning with lower latency for interactive systems Real-time agents, developer tools, retrieval-augmented applications GPT-5.4 nano Ultra-low latency and high throughput High-volume request routing, real-time chat, lightweight automation Responsible AI in Microsoft Foundry At Microsoft, our mission to empower people and organizations remains constant. In the age of AI, trust is foundational to adoption, and earning that trust requires a commitment to transparency, safety, and accountability. Microsoft Foundry provides governance controls, monitoring, and evaluation capabilities to help organizations deploy GPT-5.4 models responsibly in production environments, aligned with Microsoft's Responsible AI principles. Pricing Model Deployment Input (USD $/M tokens) Cached input (USD $/M tokens) Output (USD $/M tokens) GPT-5.4 mini Standard Global $0.75 $0.075 $4.5 GPT-5.4 nano Standard Global $0.20 $0.02 $1.25 The models are also available in Data Zone US. It is rolling out to Data Zone EU. Getting started Explore the models in Microsoft Foundry. Sign in to the Foundry portal and browse the model catalog to evaluate GPT-5.4 mini and GPT-5.4 nano alongside other options, then deploy the right model for each workload.Introducing OpenAI's newest chat model in Microsoft Foundry
OpenAI's GPT-5.5 Instant (or Chat-latest in the API) begins rolling out in Microsoft Foundry today as GPT-chat-latest. Built on GPT-5.4 and GPT-5.3-chat, the new model delivers measurable gains in factual accuracy, tool calling, and response efficiency. These improvements translate directly into more reliable production deployments. GPT-chat-latest is designed for the workflows builders are actually shipping: multi-turn assistants, agentic systems that orchestrate tools, and retrieval-grounded applications where precision and grounding matter as much as conversational quality. Why the name is changing In Microsoft Foundry, we are introducing GPT-chat-latest as the product name for this release, while the model continues to follow the existing Preview lifecycle and standard notice periods. We are also evaluating ways to simplify how customers access continuously updated models over time, but current behavior remains unchanged as that work continue Smarter, more factually reliable GPT-chat-latest closes the factuality gap from prior iterations with significant reductions in hallucinations, especially in domains where accuracy matters most. According to OpenAI, the new model produces 52.5% fewer hallucinations and reduces hallucinated claims by 37.3% on conversations previously flagged for factual errors when compared to GPT-5.3-chat. These gains extend beyond text. GPT-chat-latest shows improvements in visual reasoning, expert multimodal understanding, and STEM tasks, with measurable lifts across standard benchmarks: Benchmark GPT-5.3-chat GPT-chat-latest CharXiv-reasoning Scientific Chart Reasoning 75.0 81.6 MMMU-Pro Expert multimodal reasoning 69.2 76.0 GPQA PhD-level science questions 78.5 85.6 AIME 2025 Competition math 65.4 81.2 *Data shown comes from OpenAI’s testing” For builders shipping into regulated workloads such as clinical decision support, legal research, financial advisory, and technical analysis, these improvements raise the bar on the kinds of applications GPT-chat-latest can assist with. More efficient outputs GPT-chat-latest produces responses that may be more to-the-point without losing substance. The model may reduce verbosity and over formatting, ask fewer follow-up questions, and avoid cluttered output patterns that often require post-processing in production UIs. For builders, this can translate to two concrete benefits: lower output token costs at scale, and cleaner responses that drop into product surfaces with less downstream cleanup. In comparative testing from OpenAI, GPT-chat-latest produced roughly 25–30% fewer words than GPT-5.3-chat across a range of common prompts while preserving response quality, and in many cases improving it. Improving intelligence and tool calling GPT-chat-latest introduces measurable improvements in how the model interacts with tools, including better judgment about when and how to invoke them. The model produces more structured and context-aware tool invocation outputs, which is particularly relevant for workflows that rely on function calling, retrieval-augmented generation, and multi-step reasoning. Equally important, the model is better at deciding whether a tool is needed in the first place, reducing unnecessary tool calls in scenarios where it already has the information to answer directly. Improved search and context handling GPT-chat-latest includes targeted improvements to how the model retrieves, interprets, and synthesizes information when search is involved, with enhancements to query formulation, result ranking, and filtering, plus more grounded synthesis of retrieved content into final responses. These changes improve handling of ambiguous or underspecified queries and reduce noise in answers that depend on retrieved content. The model also makes better use of the context developers pass in, including system prompts, conversation history, retrieved documents, and structured data. Applications that maintain long-running state or stitch together multiple retrieval steps produce more coherent, context-aware outputs without developers having to over-engineer prompt scaffolding. Use Cases: When to choose the chat model Developers typically choose a chat-optimized model like GPT-5.5-chat when the application needs to sustain multi-turn conversations while reliably following instructions and coordinating external tools. This is a fit for assistants and agentic workflows where the model must interpret user intent over time, decide when to retrieve additional context, and produce structured outputs for downstream systems rather than just generate free-form text. Customer support and contact centers: virtual agents that maintain conversational context across a case, retrieve policy or product documentation via search, and hand off to a ticketing or CRM system through tool calls when escalation is needed. Retail and e-commerce: shopping and service assistants that clarify preferences over multiple turns, reference catalogs and policies via retrieval, and generate structured actions such as returns, exchanges, and order lookups through integrated tools. Manufacturing and field service: technician-facing assistants that combine conversational guidance with retrieval of manuals and work instructions, plus structured task creation in maintenance systems. Use GPT-chat-latest Use GPT-5.5 Reasoning Multi-turn assistants and customer-facing chat experiences Harder problems that benefit from more deliberate, step-by-step thinking Agentic workflows that coordinate tools (search, retrieval, ticketing, CRM) and benefit from structured tool outputs Complex analysis, planning, or decision support where correctness matters more than conversational flow Interactive experiences where you want quick back-and-forth clarification and task completion Tasks involving multi-constraint reasoning (policy interpretation, detailed requirements, long-horizon plans) RAG-based apps where the model must decide when to retrieve and then synthesize grounded answers Offline or low-tool scenarios where the main value is deeper reasoning over provided context Pricing Model Input ($/1M tokens) Cached input ($/1M tokens) Output ($/1M tokens) GPT-chat-latest $5 $0.50 $30 Responsible AI in Microsoft Foundry At Microsoft, our mission to empower people and organizations remains constant. In the age of AI, trust is foundational to adoption, and earning that trust requires a commitment to transparency, safety, and accountability. Microsoft Foundry provides governance controls, monitoring, and evaluation capabilities to help organizations deploy models responsibly in production environments, aligned with Microsoft's Responsible AI principles. Getting started GPT-chat-latest is rolling out in Microsoft Foundry today.Building Production-Ready, Secure, Observable, AI Agents with Real-Time Voice with Microsoft Foundry
We're excited to announce the general availability of Foundry Agent Service, Observability in Foundry Control Plane, and the Microsoft Foundry portal — plus Voice Live integration with Agent Service in public preview — giving teams a production-ready platform to build, deploy, and operate intelligent AI agents with enterprise-grade security and observability.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 model 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.Foundry IQ: boost response relevance by 36% with agentic retrieval
The latest RAG performance evaluations and results for knowledge bases and built-in agentic retrieval engine. Foundry IQ by Azure AI Search is a unified knowledge layer for agents, designed to improve response performance, automate RAG workflows and enable enterprise-ready grounding. These evaluations tested RAG performance for knowledge bases and new features including retrieval reasoning effort and federated sources like web and SharePoint for M365. Foundry IQ and Azure AI Search are part of Microsoft Foundry.Announcing GPT‑5‑Codex: Redefining Developer Experience in Azure AI Foundry
Today, we’re excited to announce OpenAI’s GPT‑5‑Codex is generally available in Azure AI Foundry, and in public preview for GitHub Copilot in Visual Studio Code. This release is the next step in our continuous commitment to empower developers with the latest model innovation, now building on the proven strengths of the earlier Codex generation along with the speed and CLI fluency many teams have adopted with the latest codex‑mini. Next-level features for developers Multimodal coding in a single flow: GPT-5-Codex accepts multimodal inputs including text and image. With this multimodal intelligence, developers are now empowered to tackle complex tasks, delivering context-aware, repository-scale solutions in one single workflow. Advanced tool use across various experiences: GPT-5-Codex is built for real-world developer experiences. Developers in Azure AI Foundry can get seamless automation and deep integration via the Response API, improving developers’ productivity and reducing development time. Code review expertise: GPT‑5‑Codex is specially trained to conduct code reviews and surface critical flows, helping developers catch issues early and improve code quality with AI-powered insights. It transforms code review from a manual bottleneck into an intelligent, adaptive and integrated process, empowering developers to deliver high-quality code experience. How GPT‑5‑Codex makes your life easier Stay in flow, not in friction: With GPT‑5‑Codex, move smoothly from reading issues to writing code and checking UI; all in one place. It keeps context, so developers stay focused and productive. No more jumping between tools or losing track of what they were doing. Refactor and migrate with confidence: Whether cleaning up code or moving to a new framework, GPT‑5‑Codex helps stage updates, run tests, and fix issues as you go. It’s like having a digital colleague for those tricky transitions. Hero use cases: real impact for developers Repo‑aware refactoring assistant: Feed repo and architecture diagrams to GPT‑5‑Codex. Get cohesive refactors, automated builds, and visual verification via screenshots. Flaky test hunter: Target failing test matrices. The model executes runs, polls status, inspects logs, and recommends fixes looping until stability. Cloud migration copilot: Edit IaC scripts, kick off CLI commands, and iterate on errors in a controlled loop, reducing manual toil. Pricing and Deployment available at GA Deployment Available Region Pricing ($/million tokens) Standard Global East US 2 Sweden Central Input Cached Input Output $1.25 $0.125 $10.00 GPT-5-Codex is bringing developers’ coding experience to a new level. Don’t just write code. Let’s redefine what’s possible. Start building with GPT-5-Codex today and turn your bold ideas into reality now powered by the latest innovation in Azure AI Foundry.From Zero to Hero: AgentOps - End-to-End Lifecycle Management for Production AI Agents
The shift from proof-of-concept AI agents to production-ready systems isn't just about better models—it's about building robust infrastructure that can develop, deploy, and maintain intelligent agents at enterprise scale. As organizations move beyond simple chatbots to agentic systems that plan, reason, and act autonomously, the need for comprehensive Agent LLMOps becomes critical. This guide walks through the complete lifecycle for building production AI agents, from development through deployment to monitoring, with special focus on leveraging Azure AI Foundry's hosted agents infrastructure. The Evolution: From Single-Turn Prompts to Agentic Workflows Traditional AI applications operated on a simple request-response pattern. Modern AI agents, however, are fundamentally different. They maintain state across multiple interactions, orchestrate complex multi-step workflows, and dynamically adapt their approach based on intermediate results. According to recent analysis, agentic workflows represent systems where language models and tools are orchestrated through a combination of predefined logic and dynamic decision-making. Unlike monolithic systems where a single model attempts everything, production agents break down complex tasks into specialized components that collaborate effectively. The difference is profound. A simple customer service chatbot might answer questions from a knowledge base. An agentic customer service system, however, can search multiple data sources, escalate to specialized sub-agents for technical issues, draft response emails, schedule follow-up tasks, and learn from each interaction to improve future responses. Stage 1: Development with any agentic framework Why LangGraph for Agent Development? LangGraph has emerged as a leading framework for building stateful, multi-agent applications. Unlike traditional chain-based approaches, LangGraph uses a graph-based architecture where each node represents a unit of work and edges define the workflow paths between them. The key advantages include: Explicit State Management: LangGraph maintains persistent state across nodes, making it straightforward to track conversation history, intermediate results, and decision points. This is critical for debugging complex agent behaviors. Visual Workflow Design: The graph structure provides an intuitive way to visualize and understand agent logic. When an agent misbehaves, you can trace execution through the graph to identify where things went wrong. Flexible Control Flows: LangGraph supports diverse orchestration patterns—single agent, multi-agent, hierarchical, sequential—all within one framework. You can start simple and evolve as requirements grow. Built-in Memory: Agents automatically store conversation histories and maintain context over time, enabling rich personalized interactions across sessions. Core LangGraph Components Nodes: Individual units of logic or action. A node might call an AI model, query a database, invoke an external API, or perform data transformation. Each node is a Python function that receives the current state and returns updates. Edges: Define the workflow paths between nodes. These can be conditional (routing based on the node's output) or unconditional (always proceeding to the next step). State: The data structure passed between nodes and updated through reducers. Proper state design is crucial—it should contain all information needed for decision-making while remaining manageable in size. Checkpoints: LangGraph's checkpointing mechanism saves state at each node, enabling features like human-in-the-loop approval, retry logic, and debugging. Implementing the Agentic Workflow Pattern A robust production agent typically follows a cyclical pattern of planning, execution, reflection, and adaptation: Planning Phase: The agent analyzes the user's request and creates a structured plan, breaking complex problems into manageable steps. Execution Phase: The agent carries out planned actions using appropriate tools—search engines, calculators, code interpreters, database queries, or API calls. Reflection Phase: After each action, the agent evaluates results against expected outcomes. This critical thinking step determines whether to proceed, retry with a different approach, or seek additional information. Decision Phase: Based on reflection, the agent decides the next course of action—continue to the next step, loop back to refine the approach, or conclude with a final response. This pattern handles real-world complexity far better than simple linear workflows. When an agent encounters unexpected results, the reflection phase enables adaptive responses rather than brittle failure. Example: Building a Research Agent with LangGraph from langgraph.graph import StateGraph, END from langchain_openai import ChatOpenAI from typing import TypedDict, List class AgentState(TypedDict): query: str plan: List[str] current_step: int research_results: List[dict] final_answer: str def planning_node(state: AgentState): # Agent creates a research plan llm = ChatOpenAI(model="gpt-4") plan = llm.invoke(f"Create a research plan for: {state['query']}") return {"plan": plan, "current_step": 0} def research_node(state: AgentState): # Execute current research step step = state['plan'][state['current_step']] # Perform web search, database query, etc. results = perform_research(step) return {"research_results": state['research_results'] + [results]} def reflection_node(state: AgentState): # Evaluate if we have enough information if len(state['research_results']) >= len(state['plan']): return {"next": "synthesize"} return {"next": "research", "current_step": state['current_step'] + 1} def synthesize_node(state: AgentState): # Generate final answer from all research llm = ChatOpenAI(model="gpt-4") answer = llm.invoke(f"Synthesize research: {state['research_results']}") return {"final_answer": answer} # Build the graph workflow = StateGraph(AgentState) workflow.add_node("planning", planning_node) workflow.add_node("research", research_node) workflow.add_node("reflection", reflection_node) workflow.add_node("synthesize", synthesize_node) workflow.add_edge("planning", "research") workflow.add_edge("research", "reflection") workflow.add_conditional_edges( "reflection", lambda s: s["next"], {"research": "research", "synthesize": "synthesize"} ) workflow.add_edge("synthesize", END) agent = workflow.compile() This pattern scales from simple workflows to complex multi-agent systems with dozens of specialized nodes. Stage 2: CI/CD Pipeline for AI Agents Traditional software CI/CD focuses on code quality, security, and deployment automation. Agent CI/CD must additionally handle model versioning, evaluation against behavioral benchmarks, and non-deterministic behavior. Build Phase: Packaging Agent Dependencies Unlike traditional applications, AI agents have unique packaging requirements: Model artifacts: Fine-tuned models, embeddings, or model configurations Vector databases: Pre-computed embeddings for knowledge retrieval Tool configurations: API credentials, endpoint URLs, rate limits Prompt templates: Versioned prompt engineering assets Evaluation datasets: Test cases for agent behavior validation Best practice is to containerize everything. Docker provides reproducibility across environments and simplifies dependency management: FROM python:3.11-slim WORKDIR /app COPY . user_agent/ WORKDIR /app/user_agent RUN if [ -f requirements.txt ]; then \ pip install -r requirements.txt; \ else \ echo "No requirements.txt found"; \ fi EXPOSE 8088 CMD ["python", "main.py"] Register Phase: Version Control Beyond Git Code versioning is necessary but insufficient for AI agents. You need comprehensive artifact versioning: Container Registry: Azure Container Registry stores Docker images with semantic versioning. Each agent version becomes an immutable artifact that can be deployed or rolled back at any time. Prompt Registry: Version control your prompts separately from code. Prompt changes can dramatically impact agent behavior, so treating them as first-class artifacts enables A/B testing and rapid iteration. Configuration Management: Store agent configurations (model selection, temperature, token limits, tool permissions) in version-controlled files. This ensures reproducibility and enables easy rollback. Evaluate Phase: Testing Non-Deterministic Behavior The biggest challenge in agent CI/CD is evaluation. Unlike traditional software where unit tests verify exact outputs, agents produce variable responses that must be evaluated holistically. Behavioral Testing: Define test cases that specify desired agent behaviors rather than exact outputs. For example, "When asked about product pricing, the agent should query the pricing API, handle rate limits gracefully, and present information in a structured format." Evaluation Metrics: Track multiple dimensions: Task completion rate: Did the agent accomplish the goal? Tool usage accuracy: Did it call the right tools with correct parameters? Response quality: Measured via LLM-as-judge or human evaluation Latency: Time to first token and total response time Cost: Token usage and API call expenses Adversarial Testing: Intentionally test edge cases—ambiguous requests, tool failures, rate limiting, conflicting information. Production agents will encounter these scenarios. Recent research on CI/CD for AI agents emphasizes comprehensive instrumentation from day one. Track every input, output, API call, token usage, and decision point. After accumulating production data, patterns emerge showing which metrics actually predict failures versus noise. Deploy Phase: Safe Production Rollouts Never deploy agents directly to production. Implement progressive delivery: Staging Environment: Deploy to a staging environment that mirrors production. Run automated tests and manual QA against real data (appropriately anonymized). Canary Deployment: Route a small percentage of traffic (5-10%) to the new version. Monitor error rates, latency, user satisfaction, and cost metrics. Automatically rollback if any metric degrades beyond thresholds. Blue-Green Deployment: Maintain two production environments. Deploy to the inactive environment, verify it's healthy, then switch traffic. Enables instant rollback by switching back. Feature Flags: Deploy new agent capabilities behind feature flags. Gradually enable them for specific user segments, gather feedback, and iterate before full rollout. Now since we know how to create an agent using langgraph, the next step will be understand how can we use this langgraph agent to deploy in Azure AI Foundry. Stage 3: Azure AI Foundry Hosted Agents Hosted agents are containerized agentic AI applications that run on Microsoft's Foundry Agent Service. They represent a paradigm shift from traditional prompt-based agents to fully code-driven, production-ready AI systems. When to Use Hosted Agents: ✅ Complex agentic workflows - Multi-step reasoning, branching logic, conditional execution ✅ Custom tool integration - External APIs, databases, internal systems ✅ Framework-specific features - LangGraph graphs, multi-agent orchestration ✅ Production scale - Enterprise deployments requiring autoscaling ✅ Auth- Identity and authentication, Security and compliance controls ✅ CI/CD integration - Automated testing and deployment pipelines Why Hosted Agents Matter Hosted agents bridge the gap between experimental AI prototypes and production systems: For Developers: Full control over agent logic via code Use familiar frameworks and tools Local testing before deployment Version control for agent code For Enterprises: No infrastructure management overhead Built-in security and compliance Scalable pay-as-you-go pricing Integration with existing Azure ecosystem For AI Systems: Complex reasoning patterns beyond prompts Stateful conversations with persistence Custom tool integration and orchestration Multi-agent collaboration Before you get started with Foundry. Deploy Foundry project using the starter code using AZ CLI. # Initialize a new agent project azd init -t https://github.com/Azure-Samples/azd-ai-starter-basic # The template automatically provisions: # - Foundry resource and project # - Azure Container Registry # - Application Insights for monitoring # - Managed identities and RBAC # Deploy azd up The extension significantly reduces the operational burden. What previously required extensive Azure knowledge and infrastructure-as-code expertise now works with a few CLI commands. The extension significantly reduces the operational burden. What previously required extensive Azure knowledge and infrastructure-as-code expertise now works with a few CLI commands. Local Development to Production Workflow A streamlined workflow bridges development and production: Develop Locally: Build and test your LangGraph agent on your machine. Use the Foundry SDK to ensure compatibility with production APIs. Validate Locally: Run the agent locally against the Foundry Responses API to verify it works with production authentication and conversation management. Containerize: Package your agent in a Docker container with all dependencies. Deploy to Staging: Use azd deploy to push to a staging Foundry project. Run automated tests. Deploy to Production: Once validated, deploy to production. Foundry handles versioning, so you can maintain multiple agent versions and route traffic accordingly. Monitor and Iterate: Use Application Insights to monitor agent performance, identify issues, and plan improvements. Azure AI Toolkit for Visual Studio offer this great place to test your hosted agent. You can also test this using REST. FROM python:3.11-slim WORKDIR /app COPY . user_agent/ WORKDIR /app/user_agent RUN if [ -f requirements.txt ]; then \ pip install -r requirements.txt; \ else \ echo "No requirements.txt found"; \ fi EXPOSE 8088 CMD ["python", "main.py"] Once you are able to run agent and test in local playground. You want to move to the next step of registering, evaluating and deploying agent in Microsoft AI Foundry. CI/CD with GitHub Actions This repository includes a GitHub Actions workflow (`.github/workflows/mslearnagent-AutoDeployTrigger.yml`) that automatically builds and deploys the agent to Azure when changes are pushed to the main branch. 1. Set Up Service Principal # Create service principal az ad sp create-for-rbac \ --name "github-actions-agent-deploy" \ --role contributor \ --scopes /subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP # Output will include: # - appId (AZURE_CLIENT_ID) # - tenant (AZURE_TENANT_ID) 2. Configure Federated Credentials # For GitHub Actions OIDC az ad app federated-credential create \ --id $APP_ID \ --parameters '{ "name": "github-actions-deploy", "issuer": "https://token.actions.githubusercontent.com", "subject": "repo:YOUR_ORG/YOUR_REPO:ref:refs/heads/main", "audiences": ["api://AzureADTokenExchange"] }' 3. Set Required Permissions Critical: Service principal needs Azure AI User role on AI Services resource: # Get AI Services resource ID AI_SERVICES_ID=$(az cognitiveservices account show \ --name $AI_SERVICES_NAME \ --resource-group $RESOURCE_GROUP \ --query id -o tsv) # Assign Azure AI User role az role assignment create \ --assignee $SERVICE_PRINCIPAL_ID \ --role "Azure AI User" \ --scope $AI_SERVICES_ID 4. Configure GitHub Secrets Go to GitHub repository → Settings → Secrets and variables → Actions Add the following secrets: AZURE_CLIENT_ID=<from-service-principal> AZURE_TENANT_ID=<from-service-principal> AZURE_SUBSCRIPTION_ID=<your-subscription-id> AZURE_AI_PROJECT_ENDPOINT=<your-project-endpoint> ACR_NAME=<your-acr-name> IMAGE_NAME=calculator-agent AGENT_NAME=CalculatorAgent 5. Create GitHub Actions Workflow Create .github/workflows/deploy-agent.yml: name: Deploy Agent to Azure AI Foundry on: push: branches: - main paths: - 'main.py' - 'custom_state_converter.py' - 'requirements.txt' - 'Dockerfile' workflow_dispatch: inputs: version_tag: description: 'Version tag (leave empty for auto-increment)' required: false type: string permissions: id-token: write contents: read jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4 - name: Generate version tag id: version run: | if [ -n "${{ github.event.inputs.version_tag }}" ]; then echo "VERSION=${{ github.event.inputs.version_tag }}" >> $GITHUB_OUTPUT else # Auto-increment version VERSION="v$(date +%Y%m%d-%H%M%S)" echo "VERSION=$VERSION" >> $GITHUB_OUTPUT fi - name: Azure Login (OIDC) uses: azure/login@v1 with: client-id: ${{ secrets.AZURE_CLIENT_ID }} tenant-id: ${{ secrets.AZURE_TENANT_ID }} subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }} - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.11' - name: Install Azure AI SDK run: | pip install azure-ai-projects azure-identity - name: Build and push Docker image run: | az acr build \ --registry ${{ secrets.ACR_NAME }} \ --image ${{ secrets.IMAGE_NAME }}:${{ steps.version.outputs.VERSION }} \ --image ${{ secrets.IMAGE_NAME }}:latest \ --file Dockerfile \ . - name: Register agent version env: AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZURE_AI_PROJECT_ENDPOINT }} ACR_NAME: ${{ secrets.ACR_NAME }} IMAGE_NAME: ${{ secrets.IMAGE_NAME }} AGENT_NAME: ${{ secrets.AGENT_NAME }} VERSION: ${{ steps.version.outputs.VERSION }} run: | python - <<EOF import os from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.projects.models import ImageBasedHostedAgentDefinition project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"] credential = DefaultAzureCredential() project_client = AIProjectClient.from_connection_string( credential=credential, conn_str=project_endpoint, ) agent_name = os.environ["AGENT_NAME"] version = os.environ["VERSION"] image_uri = f"{os.environ['ACR_NAME']}.azurecr.io/{os.environ['IMAGE_NAME']}:{version}" agent_definition = ImageBasedHostedAgentDefinition( image=image_uri, cpu=1.0, memory="2Gi", ) agent = project_client.agents.create_or_update( agent_id=agent_name, body=agent_definition ) print(f"Agent version registered: {agent.version}") EOF - name: Start agent run: | echo "Agent deployed successfully with version ${{ steps.version.outputs.VERSION }}" - name: Deployment summary run: | echo "### Deployment Summary" >> $GITHUB_STEP_SUMMARY echo "- **Agent Name**: ${{ secrets.AGENT_NAME }}" >> $GITHUB_STEP_SUMMARY echo "- **Version**: ${{ steps.version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY echo "- **Image**: ${{ secrets.ACR_NAME }}.azurecr.io/${{ secrets.IMAGE_NAME }}:${{ steps.version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY echo "- **Status**: Deployed" >> $GITHUB_STEP_SUMMARY 6. Add Container Status Verification To ensure deployments are truly successful, add a script to verify container startup before marking the pipeline as complete. Create wait_for_container.py: """ Wait for agent container to be ready. This script polls the agent container status until it's running successfully or times out. Designed for use in CI/CD pipelines to verify deployment. """ import os import sys import time import requests from typing import Optional, Dict, Any from azure.identity import DefaultAzureCredential class ContainerStatusWaiter: """Polls agent container status until ready or timeout.""" def __init__( self, project_endpoint: str, agent_name: str, agent_version: str, timeout_seconds: int = 600, poll_interval: int = 10, ): """ Initialize the container status waiter. Args: project_endpoint: Azure AI Foundry project endpoint agent_name: Name of the agent agent_version: Version of the agent timeout_seconds: Maximum time to wait (default: 10 minutes) poll_interval: Seconds between status checks (default: 10s) """ self.project_endpoint = project_endpoint.rstrip("/") self.agent_name = agent_name self.agent_version = agent_version self.timeout_seconds = timeout_seconds self.poll_interval = poll_interval self.api_version = "2025-11-15-preview" # Get Azure AD token credential = DefaultAzureCredential() token = credential.get_token("https://ml.azure.com/.default") self.headers = { "Authorization": f"Bearer {token.token}", "Content-Type": "application/json", } def _get_container_url(self) -> str: """Build the container status URL.""" return ( f"{self.project_endpoint}/agents/{self.agent_name}" f"/versions/{self.agent_version}/containers/default" ) def get_container_status(self) -> Optional[Dict[str, Any]]: """Get current container status.""" url = f"{self._get_container_url()}?api-version={self.api_version}" try: response = requests.get(url, headers=self.headers, timeout=30) if response.status_code == 200: return response.json() elif response.status_code == 404: return None else: print(f"⚠️ Warning: GET container returned {response.status_code}") return None except Exception as e: print(f"⚠️ Warning: Error getting container status: {e}") return None def wait_for_container_running(self) -> bool: """ Wait for container to reach running state. Returns: True if container is running, False if timeout or error """ print(f"\n🔍 Checking container status for {self.agent_name} v{self.agent_version}") print(f"⏱️ Timeout: {self.timeout_seconds}s | Poll interval: {self.poll_interval}s") print("-" * 70) start_time = time.time() iteration = 0 while time.time() - start_time < self.timeout_seconds: iteration += 1 elapsed = int(time.time() - start_time) container = self.get_container_status() if not container: print(f"[{iteration}] ({elapsed}s) ⏳ Container not found yet, waiting...") time.sleep(self.poll_interval) continue # Extract status information status = ( container.get("status") or container.get("state") or container.get("provisioningState") or "Unknown" ) # Check for replicas information replicas = container.get("replicas", {}) ready_replicas = replicas.get("ready", 0) desired_replicas = replicas.get("desired", 0) print(f"[{iteration}] ({elapsed}s) 📊 Status: {status}") if replicas: print(f" 🔢 Replicas: {ready_replicas}/{desired_replicas} ready") # Check if container is running and ready if status.lower() in ["running", "succeeded", "ready"]: if desired_replicas == 0 or ready_replicas >= desired_replicas: print("\n" + "=" * 70) print("✅ Container is running and ready!") print("=" * 70) return True elif status.lower() in ["failed", "error", "cancelled"]: print("\n" + "=" * 70) print(f"❌ Container failed to start: {status}") print("=" * 70) return False time.sleep(self.poll_interval) # Timeout reached print("\n" + "=" * 70) print(f"⏱️ Timeout reached after {self.timeout_seconds}s") print("=" * 70) return False def main(): """Main entry point for CLI usage.""" project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT") agent_name = os.getenv("AGENT_NAME") agent_version = os.getenv("AGENT_VERSION") timeout = int(os.getenv("TIMEOUT_SECONDS", "600")) poll_interval = int(os.getenv("POLL_INTERVAL_SECONDS", "10")) if not all([project_endpoint, agent_name, agent_version]): print("❌ Error: Missing required environment variables") sys.exit(1) waiter = ContainerStatusWaiter( project_endpoint=project_endpoint, agent_name=agent_name, agent_version=agent_version, timeout_seconds=timeout, poll_interval=poll_interval, ) success = waiter.wait_for_container_running() sys.exit(0 if success else 1) if __name__ == "__main__": main() Key Features: REST API Polling: Uses Azure AI Foundry REST API to check container status Timeout Handling: Configurable timeout (default 10 minutes) Progress Tracking: Shows iteration count and elapsed time Replica Checking: Verifies all desired replicas are ready Clear Output: Emoji-enhanced status messages for easy reading Exit Codes: Returns 0 for success, 1 for failure (CI/CD friendly) Update workflow to include verification: Add this step after starting the agent: - name: Start the new agent version id: start_agent env: FOUNDRY_ACCOUNT: ${{ steps.foundry_details.outputs.FOUNDRY_ACCOUNT }} PROJECT_NAME: ${{ steps.foundry_details.outputs.PROJECT_NAME }} AGENT_NAME: ${{ secrets.AGENT_NAME }} run: | LATEST_VERSION=$(az cognitiveservices agent show \ --account-name "$FOUNDRY_ACCOUNT" \ --project-name "$PROJECT_NAME" \ --name "$AGENT_NAME" \ --query "versions.latest.version" -o tsv) echo "AGENT_VERSION=$LATEST_VERSION" >> $GITHUB_OUTPUT az cognitiveservices agent start \ --account-name "$FOUNDRY_ACCOUNT" \ --project-name "$PROJECT_NAME" \ --name "$AGENT_NAME" \ --agent-version $LATEST_VERSION - name: Wait for container to be ready env: AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZURE_AI_PROJECT_ENDPOINT }} AGENT_NAME: ${{ secrets.AGENT_NAME }} AGENT_VERSION: ${{ steps.start_agent.outputs.AGENT_VERSION }} TIMEOUT_SECONDS: 600 POLL_INTERVAL_SECONDS: 15 run: | echo "⏳ Waiting for container to be ready..." python wait_for_container.py - name: Deployment Summary if: success() run: | echo "## Deployment Complete! 🚀" >> $GITHUB_STEP_SUMMARY echo "" >> $GITHUB_STEP_SUMMARY echo "- **Agent**: ${{ secrets.AGENT_NAME }}" >> $GITHUB_STEP_SUMMARY echo "- **Version**: ${{ steps.version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY echo "- **Status**: ✅ Container running and ready" >> $GITHUB_STEP_SUMMARY echo "" >> $GITHUB_STEP_SUMMARY echo "### Deployment Timeline" >> $GITHUB_STEP_SUMMARY echo "1. ✅ Image built and pushed to ACR" >> $GITHUB_STEP_SUMMARY echo "2. ✅ Agent version registered" >> $GITHUB_STEP_SUMMARY echo "3. ✅ Container started" >> $GITHUB_STEP_SUMMARY echo "4. ✅ Container verified as running" >> $GITHUB_STEP_SUMMARY - name: Deployment Failed Summary if: failure() run: | echo "## Deployment Failed ❌" >> $GITHUB_STEP_SUMMARY echo "" >> $GITHUB_STEP_SUMMARY echo "Please check the logs above for error details." >> $GITHUB_STEP_SUMMARY Benefits of Container Status Verification: Deployment Confidence: Know for certain that the container started successfully Early Failure Detection: Catch startup errors before users are affected CI/CD Gate: Pipeline only succeeds when container is actually ready Debugging Aid: Clear logs show container startup progress Timeout Protection: Prevents infinite waits with configurable timeout REST API Endpoints Used: GET {endpoint}/agents/{agent_name}/versions/{agent_version}/containers/default?api-version=2025-11-15-preview Response includes: status or state: Container state (Running, Failed, etc.) replicas.ready: Number of ready replicas replicas.desired: Target number of replicas error: Error details if failed Container States: Running/Ready: Container is operational InProgress: Container is starting up Failed/Error: Container failed to start Stopped: Container was stopped 7. Trigger Deployment # Automatic trigger - push to main git add . git commit -m "Update agent implementation" git push origin main # Manual trigger - via GitHub UI # Go to Actions → Deploy Agent to Azure AI Foundry → Run workflow Now this will trigger the Workflow as soon as you checkin the implementation code. You can play with the Agent in Foundry UI: Evaluation is now part the workflow You can also visualize the Evaluation in AI Foundry Best Practices for Production Agent LLMOps 1. Start with Simple Workflows, Add Complexity Gradually Don't build a complex multi-agent system on day one. Start with a single agent that does one task well. Once that's stable in production, add additional capabilities: Single agent with basic tool calling Add memory/state for multi-turn conversations Introduce specialized sub-agents for complex tasks Implement multi-agent collaboration This incremental approach reduces risk and enables learning from real usage before investing in advanced features. 2. Instrument Everything from Day One The worst time to add observability is after you have a production incident. Comprehensive instrumentation should be part of your initial development: Log every LLM call with inputs, outputs, token usage Track all tool invocations Record decision points in agent reasoning Capture timing metrics for every operation Log errors with full context After accumulating production data, you'll identify which metrics matter most. But you can't retroactively add logging for incidents that already occurred. 3. Build Evaluation into the Development Process Don't wait until deployment to evaluate agent quality. Integrate evaluation throughout development: Maintain a growing set of test conversations Run evaluations on every code change Track metrics over time to identify regressions Include diverse scenarios—happy path, edge cases, adversarial inputs Use LLM-as-judge for scalable automated evaluation, supplemented with periodic human review of sample outputs. 4. Embrace Non-Determinism, But Set Boundaries Agents are inherently non-deterministic, but that doesn't mean anything goes: Set acceptable ranges for variability in testing Use temperature and sampling controls to manage randomness Implement retry logic with exponential backoff Add fallback behaviors for when primary approaches fail Use assertions to verify critical invariants (e.g., "agent must never perform destructive actions without confirmation") 5. Prioritize Security and Governance from Day One Security shouldn't be an afterthought: Use managed identities and RBAC for all resource access Implement least-privilege principles—agents get only necessary permissions Add content filtering for inputs and outputs Monitor for prompt injection and jailbreak attempts Maintain audit logs for compliance Regularly review and update security policies 6. Design for Failure Your agents will fail. Design systems that degrade gracefully: Implement retry logic for transient failures Provide clear error messages to users Include fallback behaviors (e.g., escalate to human support) Never leave users stuck—always provide a path forward Log failures with full context for post-incident analysis 7. Balance Automation with Human Oversight Fully autonomous agents are powerful but risky. Consider human-in-the-loop workflows for high-stakes decisions: Draft responses that require approval before sending Request confirmation before executing destructive actions Escalate ambiguous situations to human operators Provide clear audit trails of agent actions 8. Manage Costs Proactively LLM API costs can escalate quickly at scale: Monitor token usage per conversation Set per-conversation token limits Use caching for repeated queries Choose appropriate models (not always the largest) Consider local models for suitable use cases Alert on cost anomalies that indicate runaway loops 9. Plan for Continuous Learning Agents should improve over time: Collect feedback on agent responses (thumbs up/down) Analyze conversations that required escalation Identify common failure patterns Fine-tune models on production interaction data (with appropriate consent) Iterate on prompts based on real usage Share learnings across the team 10. Document Everything Comprehensive documentation is critical as teams scale: Agent architecture and design decisions Tool configurations and API contracts Deployment procedures and runbooks Incident response procedures Version migration guides Evaluation methodologies Conclusion You now have a complete, production-ready AI agent deployed to Azure AI Foundry with: LangGraph-based agent orchestration Tool-calling capabilities Multi-turn conversation support Containerized deployment CI/CD automation Evaluation framework Multiple client implementations Key Takeaway LangGraph provides flexible agent orchestration with state management Azure AI Agent Server SDK simplifies deployment to Azure AI Foundry Custom state converter is critical for production deployments with tool calls CI/CD automation enables rapid iteration and deployment Evaluation framework ensures agent quality and performance Resources Azure AI Foundry Documentation LangGraph Documentation Azure AI Agent Server SDK OpenAI Responses API Thanks Manoranjan Rajguru https://www.linkedin.com/in/manoranjan-rajguru/
