Kategorie: Microsoft Azure

Agent Factory: The new era of agentic AI—common use cases and design patterns

This blog post is the first out of a six-part blog series called Agent Factory which will share best practices, design patterns, and tools to help guide you through adopting and building agentic AI.

Beyond knowledge: Why enterprises need agentic AI

Retrieval-augmented generation (RAG) marked a breakthrough for enterprise AI—helping teams surface insights and answer questions at unprecedented speed. For many, it was a launchpad: copilots and chatbots that streamlined support and reduced the time spent searching for information.

However, answers alone rarely drive real business impact. Most enterprise workflows demand action: submitting forms, updating records, or orchestrating multi-step processes across diverse systems. Traditional automation tools—scripts, Robotic Process Automation (RPA) bots, manual handoffs—often struggle with change and scale, leaving teams frustrated by gaps and inefficiencies.

This is where agentic AI emerges as a game-changer. Instead of simply delivering information, agents reason, act, and collaborate—bridging the gap between knowledge and outcomes and enabling a new era of enterprise automation.

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Patterns of agentic AI: Building blocks for enterprise automation

While the shift from retrieval to real-world action often begins with agents that can use tools, enterprise needs don’t stop there. Reliable automation requires agents that reflect on their work, plan multi-step processes, collaborate across specialties, and adapt in real time—not just execute single calls.

The five patterns below are foundational building blocks seen in production today. They’re designed to be combined and together unlock transformative automation.

1. Tool use pattern—from advisor to operator

Modern agents stand out by driving real outcomes. Today’s agents interact directly with enterprise systems—retrieving data, calling Application Programming Interface (APIs), triggering workflows, and executing transactions. Agents now surface answers and also complete tasks, update records, and orchestrate workflows end-to-end.

Fujitsu transformed its sales proposal process using specialized agents for data analysis, market research, and document creation—each invoking specific APIs and tools. Instead of simply answering “what should we pitch,” agents built and assembled entire proposal packages, reducing production time by 67%.

2. Reflection pattern—self-improvement for reliability

Once agents can act, the next step is reflection—the ability to assess and improve their own outputs. Reflection lets agents catch errors and iterate for quality without always depending on humans.

In high-stakes fields like compliance and finance, a single error can be costly. With self-checks and review loops, agents can auto-correct missing details, double-check calculations, or ensure messages meet standards. Even code assistants, like GitHub Copilot, rely on internal testing and refinement before sharing outputs. This self-improving loop reduces errors and gives enterprises confidence that AI-driven processes are safe, consistent, and auditable.

3. Planning pattern—decomposing complexity for robustness

Most real business processes aren’t single steps—they’re complex journeys with dependencies and branching paths. Planning agents address this by breaking high-level goals into actionable tasks, tracking progress, and adapting as requirements shift.

ContraForce’s Agentic Security Delivery Platform (ASDP) automated its partner’s security service delivery with security service agents using planning agents that break down incidents into intake, impact assessment, playbook execution, and escalation. As each phase completes, the agent checks for next steps, ensuring nothing gets missed. The result: 80% of incident investigation and response is now automated and full incident investigation can be processed for less than $1 per incident.

Planning often combines tool use and reflection, showing how these patterns reinforce each other. A key strength is flexibility: plans can be generated dynamically by an LLM or follow a predefined sequence, whichever fits the need.

4. Multi-agent pattern—collaboration at machine speed

No single agent can do it all. Enterprises create value through teams of specialists, and the multi-agent pattern mirrors this by connecting networks of specialized agents—each focused on different workflow stages—under an orchestrator. This modular design enables agility, scalability, and easy evolution, while keeping responsibilities and governance clear.

Modern multi-agent solutions use several orchestration patterns—often in combination—to address real enterprise needs. These can be LLM-driven or deterministic: sequential orchestration (such as agents refine a document step by step), concurrent orchestration (agents run in parallel and merge results), group chat/maker-checker (agents debate and validate outputs together), dynamic handoff (real-time triage or routing), and magentic orchestration (a manager agent coordinates all subtasks until completion).

JM Family adopted this approach with business analyst/quality assurance (BAQA) Genie, deploying agents for requirements, story writing, coding, documentation, and Quality Assurance (QA). Coordinated by an orchestrator, their development cycles became standardized and automated—cutting requirements and test design from weeks to days and saving up to 60% of QA time.

5. ReAct (Reason + Act) pattern—adaptive problem solving in real time

The ReAct pattern enables agents to solve problems in real time, especially when static plans fall short. Instead of a fixed script, ReAct agents alternate between reasoning and action—taking a step, observing results, and deciding what to do next. This allows agents to adapt to ambiguity, evolving requirements, and situations where the best path forward isn’t clear.

For example, in enterprise IT support, a virtual agent powered by the ReAct pattern can diagnose issues in real time: it asks clarifying questions, checks system logs, tests possible solutions, and adjusts its strategy as new information becomes available. If the issue grows more complex or falls outside its scope, the agent can escalate the case to a human specialist with a detailed summary of what’s been attempted.

These patterns are meant to be combined. The most effective agentic solutions weave together tool use, reflection, planning, multi-agent collaboration, and adaptive reasoning—enabling automation that is faster, smarter, safer, and ready for the real world.

Why a unified agent platform is essential

Building intelligent agents goes far beyond prompting a language model. When moving from demo to real-world use, teams quickly encounter challenges:

How do I chain multiple steps together reliably?

How do I give agents access to business data—securely and responsibly?

How do I monitor, evaluate, and improve agent behavior?

How do I ensure security and identity across different agent components?

How do I scale from a single agent to a team of agents—or connect to others?

Many teams end up building custom scaffolding—DIY orchestrators, logging, tool managers, and access controls. This slows time-to-value, creates risks, and leads to fragile solutions.

This is where Azure AI Foundry comes in—not just as a set of tools, but as a cohesive platform designed to take agents from idea to enterprise-grade implementation.

Azure AI Foundry: Unified, scalable, and built for the real world

Azure AI Foundry is designed from the ground up for this new era of agentic automation. Azure AI Foundry delivers a single, end-to-end platform that meets the needs of both developers and enterprises, combining rapid innovation with robust, enterprise-grade controls.

With Azure AI Foundry, teams can:

Prototype locally, deploy at scale: Develop and test agents locally, then seamlessly move to cloud runtime—no rewrites needed. Check out how to get started with Azure AI Foundry SDK.

Flexible model choice: Choose from Azure OpenAI, xAI Grok, Mistral, Meta, and over 10,000 open-source models—all via a unified API. A Model Router and Leaderboard help select the optimal model, balancing performance, cost, and specialization. Check out the Azure AI Foundry Models catalog.

Compose modular multi-agent architectures: Connect specialized agents and workflows, reusing patterns across teams. Check out how to use connected agents in Azure AI Foundry Agent Service.

Integrate instantly with enterprise systems: Leverage over 1,400+ built-in connectors for SharePoint, Bing, SaaS, and business apps, with native security and policy support. Check out what are tools in Azure AI Foundry Agent Service.

Enable openness and interoperability: Built-in support for open protocols like Agent-to-Agent (A2A) and Model Context Protocol (MCP) lets your agents work across clouds, platforms, and partner ecosystems. Check out how to connect to a Model Context Protocol Server Endpoint in Azure AI Foundry Agent Service.

Enterprise-grade security: Every agent gets a managed Entra Agent ID, robust Role-based Access Control (RBAC), On Behalf Of authentication, and policy enforcement—ensuring only the right agents access the right resources. Check out how to use a virtual network with the Azure AI Foundry Agent Service.

Comprehensive observability: Gain deep visibility with step-level tracing, automated evaluation, and Azure Monitor integration—supporting compliance and continuous improvement at scale. Check out how to monitor Azure AI Foundry Agent Service.

Azure AI Foundry isn’t just a toolkit—it’s the foundation for orchestrating secure, scalable, and intelligent agents across the modern enterprise.It’s how organizations move from siloed automation to true, end-to-end business transformation.

Stay tuned: In upcoming posts in our Agent Factory blog series, we’ll show you how to bring these pillars to life—demonstrating how to build secure, orchestrated, and interoperable agents with Azure AI Foundry, from local development to enterprise deployment.

Azure AI Foundry
Design, customize, and manage AI apps and agents at scale.

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The post Agent Factory: The new era of agentic AI—common use cases and design patterns appeared first on Microsoft Azure Blog.
Quelle: Azure

GPT-5 in Azure AI Foundry: The future of AI apps and agents starts here

For business leaders building with AI, the conversation has moved beyond chat. The bar is higher: can your AI generate, reason, and deliver measurable outcomes—safely and at scale?

Today, we’re announcing general availability of OpenAI’s new flagship model, GPT-5, in Azure AI Foundry. This is more than a new model release; it is the most powerful LLM ever released across key benchmarks. GPT-5 in Azure AI Foundry pairs frontier reasoning with high-performance generation and cost efficiency, delivered on Microsoft Azure’s enterprise-grade platform so organizations can move from pilots to production with confidence. 

Enhance customer experiences with Azure AI Foundry

GPT-5 in Azure AI Foundry: Built for real-world workloads

In Azure AI Foundry, the GPT-5 models are available via API and orchestrated by themodel router. The GPT-5 series spans complementary strengths:

GPT-5, a full reasoning model provides deep, richer reasoning for analytics and complex tasks, like code generation, with a 272k token context.

GPT-5 mini powers real-time experiences for apps and agents that require reasoning, tool calling to solve customer problems.

GPT-5 nano is a new class of reasoning model which focuses on ultra-low-latency and speed with rich Q&A capabilities.

GPT-5 chat enables natural, multimodal, multi-turn conversations that remain context-aware throughout agentic workflows, with 128k token context.

Together, the suite delivers a seamless continuum from rigorous agentic coding tasks, to relatively simple Q&A—all delivered with the same Azure AI Foundry endpoint using model router in Foundry Models.

Under the hood, GPT-5 unifies advanced reasoning, code generation, and natural language interaction. It combines analytical depth with intuitive dialogue to solve end-to-end problems and explain its approach. Agentic capabilities allow multi-step tool use and long action chains with transparent, auditable decisions. As a frontier-level coding model, GPT-5 can plan complex agentic workflow, build migrations, and refactor code, as well as produce tests and documentation with clear rationale. Developer controls—including parameters like reasoning_effort and verbosity—let teams tune depth, speed, and detail, while new freeform tool-calling features enable broadens tool compatibility without rigid schemas.

Orchestrate with the model router—then scale with agents

Introducing GPT-5 to Azure AI Foundry is more than a model drop: it’s a leap forward for the platform. Starting today, developers can use the model router in Foundry Models to maximize the capabilities of the GPT-5 family models (and other models in Foundry Models) while saving up to 60% on inferencing cost with no loss in fidelity. Powered by a fine-tuned SLM under the hood, the model router evaluates each prompt and decides the optimal model based on the complexity, performance needs, and cost efficiency of each task. Let the model router pick the right model so that you can build your AI-powered applications with ease.

And orchestration doesn’t stop at routing—Foundry carries the same intelligence into agents. Coming soon, GPT-5 will be available in the Foundry Agent Service, pairing frontier models with built-in tools including new browser automation and Model Context Protocol (MCP) integrations. The result: policy-governed, tool-using agents that can search, act in web apps, and complete end-to-end tasks—instrumented with Foundry telemetry and aligned to Microsoft Responsible AI.

Accelerating business impact with GPT-5

These capabilities map directly to business impact.

In research and knowledge work, GPT-5 accelerates financial and legal analysis, market intelligence, and due diligence—reading at scale and producing decision-ready output with traceability. In operations and decisioning, it strengthens logistics support, risk assessment, and claims processing by pairing robust reasoning with policy adherence. Copilots and customer experience teams benefit from multi-turn, multimodal agents that reason in real time, call tools, resolve tasks, and revert to humans with more helpful context.

In software engineering, GPT-5 excels at code generation, application modernization, and quality engineering—improving code style and explanations to compress review cycles.

And for use cases which are cost or latency sensitive, GPT-5-nano’s ultra‑low‑latency architecture delivers rapid, high‑accuracy responses, making it the ideal target for fine‑tuning and the go‑to model for high‑volume, straightforward requests.

GPT-5 customer spotlight

Customers are unleashing GPT-5 across complex, mission-critical workloads—accelerating decision-making, supercharging coding, and catalyzing product innovation.

SAP

SAP is excited to be among the first to leverage the power of GPT-5 in Azure AI Foundry within our generative AI hub in AI Foundation. GPT-5 in Azure AI Foundry will enable our product team and our developer community to deliver impactful business innovations to our customers.
—Dr. Walter Sun, SVP and Global Head of AI, SAP SE

Relativity

The GPT-5 in Azure AI Foundry raises the bar for putting legal data intelligence into action… This next-generation AI will empower legal teams to uncover deeper insights, accelerate decision-making, and drive stronger strategies across the entire legal process.
—Dr. Aron Ahmadia, Senior Director, Applied Science, Relativity

Hebbia

The partnership between Hebbia and Azure AI Foundry gives financial professionals an unprecedented edge. With GPT-5’s advanced reasoning in Hebbia, they can pinpoint critical figures across thousands of documents and structuring complex financial analysis with speed and accuracy.
—Danny Wheller, VP of Business and Strategy

Building with AI in GitHub Copilot and Visual Studio Code

GPT-5 begins rolling out today to millions of developers using GitHub Copilot and Visual Studio Code, applying the flagship model’s advanced reasoning capabilities to increasingly complex problems—from sophisticated refactoring to navigating large codebases more effectively. GPT-5 helps developers write, test, and deploy code faster, while supporting agentic coding tasks with significant improvements to coding style and overall code quality. With GPT-5, developers not only code faster, but code better.

With today’s VS Code release, developers also gain a more powerful agentic coding experience directly within the editor: GitHub Copilot’s coding agent has an improved experience for autonomously tackling tasks in the background. Additionally, the GitHub Copilot chat experience brings increased productivity, including support beyond 128 tools for a single chat request and chat checkpoints allowing users to restore workspace changes to a prior point. Today, we are also announcing an updated extension to develop agents using the Azure AI Foundry extension all within VS Code environment.

These announcements extend Microsoft’s strategy to transform software development with AI, bringing advanced AI capabilities to the entire software lifecycle.

Security, safety, and governance by design

In all domains, security and safety is a layer cake of protections, which together provide protection for risk scenarios—and AI is no different. For AI, we think about layers with the model as the core. With GPT-5, the core is safer than before:

The Microsoft AI Red Team found GPT-5 to have one of the strongest safety profiles of any OpenAI model, performing on par with—or better than—o3.
—Dr. Sarah Bird, Chief Product Officer of Responsible AI, Microsoft

As we think about the safety, security, and governance layers around this core—Azure AI Foundry provides a number of additional controls:

Azure AI Content Safety protections are applied to every prompt and completion, such as prompt shields, which help to detect and mitigate prompt-injection attempts before they reach the model.

Built-in agent evaluators work with the AI Red Teaming Agent to run alignment, bias, and security tests throughout development and production, while continuous evaluation streams real-time metrics—latency, quality, safety, and fairness—stream into Azure Monitor and Application Insights for single-pane visibility.

Finally, security signals integrate directly with Microsoft Defender for Cloud, and runtime metadata and evaluation results are integrated to Microsoft Purview for audit, data-loss prevention, and regulatory reporting, extending protection and governance across the entire GPT-5 lifecycle.

Bringing AI into every workflow with GitHub Copilot and Visual Studio Code

Starting today, GPT-5 begins rolling out to millions of developers who use GitHub Copilot and Visual Studio Code who will be able to select GPT-5 to write, test, and deploy code—and develop agents using the Azure AI Foundry extension all within VS Code environment. GPT-5 supports complex agentic coding tasks with significant improvements to coding personality, front-end aesthetics, and code quality, highly desired improvements for the developer community.

Our evaluations show OpenAI GPT-5’s reasoning capabilities and contextual awareness exceed o3, enabling developers to tackle more complex problems—from refactoring to navigating large codebases. With GPT-5, users in the Visual Studio family can not only code faster, but code better.

VS Code and our recent decision to open-source GitHub Copilot, represents our commitment to open tools and standards and demonstrates our ability to meet the rapid pace of model innovations while keeping the developer experience at the forefront. In today’s release of VS Code, developers can. In today’s VS Code release, developers have even more control over their experience in chat—with improvements to the reliability of terminal tools, updates to the tool picker and limits, new checkpoints, and more.

Today’s announcement extends Microsoft’s strategy to transform software development with AI, bringing advanced AI capabilities to the entire software lifecycle.

Start building today

GPT-5 is available via our Standard offering in Azure AI Foundry, with deployment choices optimized for cost-efficiency and governance needs, including Global and Data Zone (United States, European Union) deployment options for data residency and compliance.1

With Azure AI Foundry’s first-class reliability, realtime evaluations, built-in observability, and secure deployment options, you can confidently move from pilot to production—all aided while unique tools like Model Router optimizes quality, latency, and cost across workloads.

Azure AI Foundry
Design, customize, and manage powerful, adaptable AI agents to get started today.

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1Pricing is accurate as of August 2025
The post GPT-5 in Azure AI Foundry: The future of AI apps and agents starts here appeared first on Microsoft Azure Blog.
Quelle: Azure

Introducing Azure Storage Discovery: Transform data management with storage insights

We are excited to announce the public preview of Azure Storage Discovery, a fully managed service that provides you enterprise-wide visibility into your Azure Blob Storage data estate. It provides a single pane of glass to understand and analyze how your data estate has evolved over time, optimize costs, enhance security, and drive operational efficiency. Azure Storage Discovery integrates with the Azure Copilot enabling you to use natural language to unlock insights and accelerate decision-making without utilizing any query language.

As your organization expands its digital footprint in the cloud, managing vast and globally distributed datasets across various business units and workloads becomes increasingly challenging. Insights aggregated across the entire Azure Blob Storage data estate can simplify the detection of outliers, enable long-term trend analysis, and support deep dives into specific resources using filters and pivots. Currently, customers rely on disparate tools and PowerShell scripts to generate, maintain and view such insights. This requires constant development, deployment, and management of infrastructure at scale. Azure Storage Discovery automates and scales this process by aggregating insights across all the subscriptions in your Microsoft Entra tenant and delivering them to you directly within the Azure portal.

Learn more about Azure Storage Discovery

Whether you’re a cloud architect, storage administrator, or data governance lead, Azure Storage Discovery helps you quickly answer key questions about your enterprise data estate in Azure Blob Storage:

How much data do we store across all our storage accounts?

Which regions are experiencing the highest growth?

Can I reduce our costs by finding data that is not being frequently used?

Are our storage configurations aligned with security and compliance best practices?

With Azure Storage Discovery, you can now explore such insights—and many more—with just a few clicks and with a Copilot by your side.

From insight to action with Azure Storage Discovery

Azure Storage Discovery simplifies the process of uncovering and analyzing insights from thousands of storage accounts, transforming complexity into clarity with just a few clicks.

Some of the key capabilities are:

Tap into Azure Copilot to get answers to the most critical storage questions for your business, without needing to learn a new query language or writing a single line of code. You can use Copilot to go beyond the pre-built reports and bring together insights across capacity, activity, errors and configurations.

Gain advanced storage insights that help you analyze how the data estate in Azure Blob Storage is growing, identify opportunities for cost optimization, discover data that is under-utilized, pinpoint workloads that could be getting throttled and find ways to strengthen the security of your storage accounts. These insights are powered by metrics related to storage capacity (object size and object count), activity on the data estate (transactions, ingress, egress), aggregation of transaction errors and detailed configurations for data protection, cost optimization and security.

Interactive reports in the Azure Portal make it simple to analyze trends over time, drill into top storage accounts, and instantly navigate to the specific resources represented in each chart. The reports can be filtered to focus on specific parts of the data estate based on Storage account configurations like regions, redundancy, performance type, encryption type, and others. Organization-wide visibility with flexible scoping to gather insights for multiple business groups or workloads. Analyze up to 1 million storage accounts spread across different subscriptions, resource groups and regions within a single workspace. The ability to drill down and filter data allows you to quickly obtain actionable insights for optimizing your data estate.

Fully managed service available right in the Azure Portal, with no additional infrastructure deployment or impact on business-critical workloads.

Up to 30 days of historical data will automatically be added within hours of deploying Azure Storage Discovery and all insights will be retained for up to 18 months.

Customer stories

Several customers have already started exploring Azure Storage Discovery during the preview to analyze their enterprise Azure Blob Storage data estate. Here are a few customers who found immediate value during the preview.

Getting a 360-degree view of the data estate in Azure Blob Storage

Tesco, one of the world’s largest and most innovative retailers, has been leveraging Storage Discovery in preview to gain an “effortless 360 View” of its data estate in Azure Blob Storage. To boost agility in development, the cloud deployment at Tesco is operated in a highly democratized manner, giving departments and teams autonomy over their subscriptions and storage accounts. However, to manage their cloud spend, ensure their deployment is configured correctly and optimize their data estate, each team is looking for detailed insights in a timely manner. The Cloud Platform Engineering (CPE) team works with each team providing them centralized data for cost analysis, security, and operational reporting. Currently, gathering and reporting on these insights to each team is a highly manual and operationally challenging task. As early adopters they have been using Azure Storage Discovery to provide a centralized, tenant-wide dashboard—to enable a “single-pane-of-glass” for key metrics and baselines. This helps them reduce the resources and time associated with answering simple questions such as “how much data do we have, and where?” or “what’s our baseline trends?”

As our data estate in Azure Storage continues to grow, it has become time consuming to gather the insights required to drive decisions around ‘How’ and ‘What’ we do—especially at the pace which is often demanded by stakeholders. Today, a lot of this is done using PowerShell scripts which even with parallelism, take a significant time to run, due to our overall scale. Anything which reduces the time it takes me to gather valuable insights is super valuable. On the other side, if I were to put my Ops hat on, the data presented is compelling for conversations with application teams; allowing us to focus on what really matters and addressing our top consumers, as opposed to being ‘snowed in’ under a mountain of data.
—Rhyan Waine, Lead Engineer, Cloud Platform Engineering, Tesco

Manage budget by identifying Storage Accounts that are growing rapidly

Willis Towers Watson (WTW) is at the forefront of using generative AI to enhance their offering for Human Resources and Insurance services while also balancing their costs. With Azure Storage Discovery, the team was able to quickly identify storage accounts where data was growing rapidly and increasing costs. With the knowledge of which storage accounts to focus on, they were able to identify usage patterns, roll out optimizations and control their costs.

As soon as my team started using Storage Discovery, they were immediately impressed by the insights it provided. Their reaction was, ‘Great—let’s dive in and see what we can uncover.’ Very quickly, they identified several storage accounts that were growing at an unusual rate. With that visibility, we were able to zero in on those Storage Accounts. We also discovered data that hadn’t been accessed in a long time, so we implemented automatic cleanups using Blob Lifecycle Management to efficiently manage and delete unused data.
—Darren Gipson, Lead DevOps Engineer, Willis Towers Watson

How Storage Discovery works

To get started with Azure Storage Discovery, follow these two simple steps: first, configure a Discovery workspace which contains the definition of the resource, and then define the Scopes that represent your business groups or workloads. Once these steps are completed, Azure Storage Discovery will start aggregating the relevant insights and make them available to you in detailed dashboards that can be found in the Reports page.

Deploying a Discovery workspace enables you to select which part of your data estate in Azure Blob Storage you want to analyze. You can do this by selecting all the subscriptions and resource groups of interest within your Microsoft Entra tenant. Upon successful verification of your access credentials, Azure Storage Discovery will advance to the next step.

Once the workspace is configured, you can create up to 5 scopes, each representing a business group, a workload, or any other logical grouping of storage accounts that has business value to you. This filtering can be done by selecting ARM resource tags that were previously applied to your storage accounts.

After the deployment is successful, Azure Storage Discovery provides reports right within the Azure portal with no additional setup.

Pricing and availability

Storage Discovery is available in select Azure regions during public preview. The service offers a Free pricing plan with insights related to capacity and configurations retained for up to 15 days and a Standard pricing plan that also includes advanced insights related to activity, errors and security configurations retained for up to 18 months to analyze annual trends and cycles in your business workloads. Pricing is based on the number of storage accounts and objects analyzed, with tiered rates to support all sizes of data estates in Azure Blob Storage.

The Free and Standard pricing plans will be offered for free, with no additional cost until September 30th, 2025. Learn more about pricing in the Azure Storage Discovery documentation.

Get started with Azure Storage Discovery

You can get started using Azure Storage Discovery to unlock the full potential of your storage within minutes. We invite you to preview Azure Storage Discovery for data management of your object storage. To get started, refer to the quick start guide to configure your first workspace. To learn more, check out the documentation.

We’d love to hear your feedback. What insights are most valuable to you? What would make Storage Discovery more compelling for your business? Let us know at StorageDiscoveryFeedback@service.microsoft.com.

Discover more about Azure Storage Discovery

The post Introducing Azure Storage Discovery: Transform data management with storage insights appeared first on Microsoft Azure Blog.
Quelle: Azure

OpenAI’s open‑source model: gpt‑oss on Azure AI Foundry and Windows AI Foundry 

AI is no longer a layer in the stack—it’s becoming the stack. This new era calls for tools that are open, adaptable, and ready to run wherever your ideas live—from cloud to edge, from first experiment to scaled deployment. At Microsoft, we’re building a full-stack AI app and agent factory that empowers every developer not just to use AI, but to create with it.

That’s the vision behind our AI platform spanning cloud to edge. Azure AI Foundry provides a unified platform for building, fine-tuning, and deploying intelligent agents with confidence while Foundry Local brings open-source models to the edge—enabling flexible, on-device inferencing across billions of devices. Windows AI Foundry builds on this foundation, integrating Foundry Local into Windows 11 to support a secure, low-latency local AI development lifecycle deeply aligned with the Windows platform. 

With the launch of OpenAI’s gpt‑oss models—its first open-weight release since GPT‑2—we’re giving developers and enterprises unprecedented ability to run, adapt, and deploy OpenAI models entirely on their own terms. 

For the first time, you can run OpenAI models like gpt‑oss‑120b on a single enterprise GPU—or run gpt‑oss‑20b locally. It’s notable that these aren’t stripped-down replicas—they’re fast, capable, and designed with real-world deployment in mind: reasoning at scale in the cloud, or agentic tasks at the edge. 

And because they’re open-weight, these models are also easy to fine-tune, distill, and optimize. Whether you’re adapting for a domain-specific copilot, compressing for offline inference, or prototyping locally before scaling in production, Azure AI Foundry and Foundry Local give you the tooling to do it all—securely, efficiently, and without compromise. 

Create intelligent applications with Azure AI Foundry

Open models, real momentum 

Open models have moved from the margins to the mainstream. Today, they’re powering everything from autonomous agents to domain-specific copilots—and redefining how AI gets built and deployed. And with Azure AI Foundry, we’re giving you the infrastructure to move with that momentum: 

With open weights teams can fine-tune using parameter-efficient methods (LoRA, QLoRA, PEFT), splice in proprietary data, and ship new checkpoints in hours—not weeks.

You can distill or quantize models, trim context length, or apply structured sparsity to hit strict memory envelopes for edge GPUs and even high-end laptops.

Full weight access also means you can inspect attention patterns for security audits, inject domain adapters, retrain specific layers, or export to ONNX/Triton for containerized inference on Azure Kubernetes Service (AKS) or Foundry Local.

In short, open models aren’t just feature-parity replacements—they’re programmable substrates. And Azure AI Foundry provides training pipelines, weight management, and low-latency serving backplane so you can exploit every one of those levers and push the envelope of AI customization. 

Meet gpt‑oss: Two models, infinite possibilities

Today, gpt‑oss-120b and gpt‑oss-20b are available on Azure AI Foundry. gpt‑oss-20b is also available on Windows AI Foundry and will be coming soon on MacOS via Foundry Local. Whether you’re optimizing for sovereignty, performance, or portability, these models unlock a new level of control. 

gpt‑oss-120b is a reasoning powerhouse. With 120 billion parameters and architectural sparsity, it delivers o4-mini level performance at a fraction of the size, excelling at complex tasks like math, code, and domain-specific Q&A—yet it’s efficient enough to run on a single datacenter-class GPU. Ideal for secure, high-performance deployments where latency or cost matter.

gpt‑oss-20b is tool-savvy and lightweight. Optimized for agentic tasks like code execution and tool use, it runs efficiently on a range of Windows hardware, including discrete GPUs with16GB+ VRAM, with support for more devices coming soon. It’s perfect for building autonomous assistants or embedding AI into real-world workflows, even in bandwidth-constrained environments. 

Both models will soon be API-compatible with the now ubiquitous responses API. That means you can swap them into existing apps with minimal changes—and maximum flexibility. 

Bringing gpt‑oss to Cloud and Edge 

Azure AI Foundry is more than a model catalog—it’s a platform for AI builders. With more than 11,000 models and growing, it gives developers a unified space to evaluate, fine-tune, and productionize models with enterprise-grade reliability and security. 

Today, with gpt‑oss in the catalog, you can: 

Spin up inference endpoints using gpt‑oss in the cloud with just a few CLI commands.

Fine-tune and distill the models using your own data and deploy with confidence.

Mix open and proprietary models to match task-specific needs.

For organizations developing scenarios only possible on client devices, Foundry Local brings prominent open-source models to Windows AI Foundry, pre-optimized for inference on your own hardware, supporting CPUs, GPUs, and NPUs, through a simple CLI, API, and SDK.

Whether you’re working in an offline setting, building in a secure network, or running at the edge—Foundry Local and Windows AI Foundry lets you go fully cloud-optional. With the capability to deploy gpt‑oss-20b on modern high-performance Windows PCs, your data stays where you want it—and the power of frontier-class models comes to you. 

This is hybrid AI in action: the ability to mix and match models, optimize performance and cost, and meet your data where it lives. 

Empowering builders and decision makers 

The availability of gpt‑oss on Azure and Windows unlocks powerful new possibilities for both builders and business leaders. 

For developers, open weights mean full transparency. Inspect the model, customize, fine-tune, and deploy on your own terms. With gpt‑oss, you can build with confidence, understanding exactly how your model works and how to improve it for your use case. 

For decision makers, it’s about control and flexibility. With gpt‑oss, you get competitive performance—with no black boxes, fewer trade-offs, and more options across deployment, compliance, and cost. 

A vision for the future: Open and responsible AI, together 

The release of gpt‑oss and its integration into Azure and Windows is part of a bigger story. We envision a future where AI is ubiquitous—and we are committed to being an open platform to bring these innovative technologies to our customers, across all our data centers and devices. 

By offering gpt‑oss through a variety of entry points, we’re doubling down on our commitment to democratize AI. We recognize that our customers will benefit from a diverse portfolio of models—proprietary and open—and we’re here to support whichever path unlocks value for you. Whether you are working with open-source models or proprietary ones, Foundry’s built-in safety and security tools ensure consistent governance, compliance, and trust—so customers can innovate confidently across all model types. 

Finally, our support of gpt-oss is just the latest in our commitment to open tools and standards. In June we announced that GitHub Copilot Chat extension is now open source on GitHub under the MIT license—the first step to make VS Code an open source AI editor. We seek to accelerate innovation with the open-source community and drive greater value to our market leading developer tools. This is what it looks like when research, product, and platform come together. The very breakthroughs we’ve enabled with our cloud at OpenAI are now open tools that anyone can build on—and Azure is the bridge that brings them to life. 

Next steps and resources for navigating gpt‑oss

Deploy gpt‑oss in the cloud today with a few CLI commands using Azure AI Foundry. Browse the Azure AI Model Catalog to spin up an endpoint. 

Deploy gpt‑oss-20b on your Windows device today (and soon on MacOS) via Foundry Local. Follow the QuickStart guide to learn more.

Pricing1 for these models is as follows:

*See Managed Compute pricing page here.

1Pricing is accurate as of August 2025.

The post OpenAI’s open‑source model: gpt‑oss on Azure AI Foundry and Windows AI Foundry  appeared first on Microsoft Azure Blog.
Quelle: Azure

Scaling generative AI in the cloud: Enterprise use cases for driving secure innovation 

Generative AI was made for the cloud. Only when you bring AI and the cloud together can you unlock the full potential of AI for business. For organizations looking to level up their generative AI capabilities, the cloud provides the flexibility, scalability and tools needed to accelerate AI innovation. Migration clears the roadblocks that inhibit AI adoption, making it faster and easier to not only adopt AI, but to move from experimentation to driving real business value.

Whether you are interested in tapping into real-time insights, delivering hyper-personalized customer experiences, optimizing supply chains with predictive analytics, or streamlining strategic decision-making, AI is reshaping how companies operate. Organizations relying on legacy or on-premises infrastructure are approaching an inflection point. Migration is not just a technical upgrade, it is a business imperative for realizing generative AI at scale. Without the flexibility the cloud provides, companies face higher costs, slower innovation cycles, and limited access to the data that AI models need to deliver meaningful results. 

Deploy trusted AI quickly with Azure AI services.

For IT and digital transformation leaders, choosing the right cloud platform is key to successfully deploying and managing AI. With best-in-class infrastructure, high-performance compute capabilities, enterprise-grade security, and advanced data integration tools, Azure offers a comprehensive cloud ecosystem that forward-thinking businesses can count on when bringing generative AI initiatives to bear. 

In our technical guide, “Accelerating Generative AI Innovation with Cloud Migration” we outline how IT and digital transformation leaders can tap into the power and flexibility of Azure to unlock the full potential of generative AI. Let us explore a few real-world business scenarios where generative AI in the cloud is driving tangible impact, helping companies move faster, innovate, and activate new ways of working.

Use case 1: Driving smarter, more adaptive AI solutions with real-time data

One of the biggest challenges in AI adoption? Disconnected or outdated data. Ensuring that AI models have access to the most current and relevant data is where Retrieval-augmented generation (RAG) shines. RAG makes generative AI more accurate and reliable by pulling in real-time, trusted data, reducing the chance of errors and hallucinations. 

How does deploying RAG impact businesses? 

Unlike traditional AI models that rely on historical data, RAG-powered AI is dynamic, staying up to date by pulling in the latest information from sources like SQL databases, APIs, and internal documents. This makes it more accurate in fast-changing environments. RAG models help teams: 

Automate live data retrieval, improving efficiency by reducing the need for manual updates. 

Make smarter, more informed decisions by granting access to the latest domain specific information. 

Boost accuracy and speed in interactive apps. 

Lower operational costs by reducing the need for human intervention. 

Tap into proprietary data to create differentiated outcomes and competitive advantages. 

Companies are turning to RAG models to generate more accurate, up-to-date insights by pulling in live data. This is especially valuable in fast-moving industries like finance, healthcare, and retail, where decisions rely on the latest market trends, access to sensitive data, regulatory updates, and personalized customer interactions. 

The Azure advantage:

Cloud-based RAG apps help businesses move beyond static AI by enabling more adaptive, intelligent solutions. When RAG runs in the cloud, enterprises can benefit from reduced latency, high-speed data transfers, built-in security controls, and simplified data governance. 

Azure’s cloud services, including Azure AI Search, Azure OpenAI Service, and Azure Machine Learning, provide the necessary tools to support responsive and secure RAG applications. Together, these services help businesses stay responsive in rapidly changing environments so they are ready for whatever comes next. 

Use case 2: Embedding generative AI into enterprise workflows

Enterprise systems like enterprise resource planning (ERP) software, customer relationship management (CRM), and content management platforms are the backbone of daily operations and crucial to the success of an organization. However, they often rely on repetitive tasks and manual oversight. By integrating generative AI directly into these workflows, businesses can streamline tasks, unlock faster insights, and deliver more personalized, contextually relevant recommendations, all within the existing systems that teams are already using.

What is the business impact of embedding generative AI into enterprise application workflows? 

With AI built into core business applications, teams can work smarter and faster. With embedded generative AI in enterprise apps, industry leaders can: 

Optimize their operations by analyzing supply chain data on the fly, flagging anomalies and recommending actionable insights and proactive adjustments. 

Enrich customer experiences with personalized recommendations and faster response times. 

Automate routine tasks like data entry, report generation, and content management to reduce manual effort and expedite workflows. 

For organizations running on-premises ERP and CRM systems, the ability to integrate AI presents a compelling reason to move to the cloud.

The Azure advantage:

With Azure, companies can bring GenAI into everyday business operations without disrupting them, gaining scalable compute power, secure data access, and modernization while maintaining operational continuity. Migrating these systems to the cloud also simplifies AI integration by eliminating silos and enabling secure, real-time access to business-critical data. Cloud migration lays the foundation for continuous innovation, allowing teams to quickly deploy updates, integrate new AI capabilities, and scale across the enterprise without disruption. 

Azure services like Azure OpenAI Service, Azure Logic Apps, and Azure API Management facilitate seamless integration, amplifying ERP and CRM systems with minimal disruption. 

Microsoft’s collaborations with platforms like SAP showcase how cloud-powered AI delivers current intelligence, streamlined operations, and advanced security—capabilities that are difficult to achieve with on-premises infrastructure. 

When generative AI is embedded into core applications, it goes beyond supporting operations. It transforms them.

Use case 3: Generative search for contextually aware responses

As enterprise data continues to grow, finding the right information at the right time has become a major challenge. Generative search transforms how organizations access and use information. With generative search, employees are empowered to make smarter decisions faster. As data volume grows, generative search helps cut through the noise by combining hybrid search with advanced AI models to deliver context-aware, tailored responses based on real-time data.

How can businesses use generative search to achieve real impact? 

With generative search, companies are better equipped to put their data to work. This approach is ideal knowledge discovery, customer support, and document retrieval, where the goal is to provide meaningful insights, summaries, or recommendations. With generative search, enterprises can: 

Improve customer support by delivering relevant, real-time responses based on customer data. 

Surface critical insights by quickly navigating unstructured and proprietary data. 

Summarize and extract key information from dense documents in less time. 

Across industries, generative search expands access to critical information, helping businesses move faster and smarter.

The Azure advantage:

Cloud-based generative search leverages the processing power and model options available in cloud environments.

Azure services like Azure AI Search, Azure OpenAI Service, and Azure Machine Learning enable productive integration of generative search into workflows, heightening context-aware search. Azure AI Search combines vector and keyword search to retrieve the most relevant data, while Azure OpenAI Service leverages models like GPT-4 to generate summaries and recommendations.

Azure Machine Learning ensures search outcomes remain precise through fine-tuning, and Azure Cognitive Search builds comprehensive indexes for improved retrieval.

Additional components, such as Azure Functions for dynamic model activation and Azure Monitor for performance tracking, further refine generative search capabilities, empowering organizations to harness AI-driven insights with confidence. 

Use case 4: Smart automation with generative AI agents 

There has been plenty of chatter around agentic AI this year, and for good reason. Unlike traditional chatbots, generative AI agents autonomously perform tasks to achieve specific goals, adapting to user interactions and continuously improving over time without needing explicit programming for every situation.

How can AI agents impact a business’s bottom line? 

By optimizing their actions for the best possible outcomes, AI agents help teams streamline workflows, respond to dynamic needs, and amplify overall effectiveness. With intelligent agents in place, companies can:

Automate repetitive, routine tasks, boosting efficiency and freeing teams to focus on higher-value workflows.

Cut operational costs, thanks to reduced manual effort and increased process efficiency.

Scale effortlessly, handling increased workloads without additional headcount. 

Improve service delivery by enabling consistent and personalized customer experiences. 

As demand rises, they scale effortlessly, enabling businesses to manage higher workloads without additional resources. This adaptability is especially valuable in industries with rapidly fluctuating customer demands, including e-commerce, financial services, manufacturing, communications, professional services, and healthcare.

The Azure advantage:

Cloud-based generative AI enables agents to access and process complex, distributed data sources in real time, sharpening their adaptability and accuracy. Microsoft Azure provides a comprehensive suite of tools to deploy and manage generative AI agents successfully: 

Azure AI Foundry Agent Service simplifies the enablement of agents capable of automating complex business processes from development to deployment. 

Azure OpenAI Service powers content generation and data analysis, while Azure Machine Learning enables fine-tuning and predictive analytics. 

Azure Cognitive Services polishes natural language understanding and Azure Databricks facilitates scalable AI model development.

For capable deployment and monitoring, Azure Kubernetes Service (AKS) streamlines containerized workloads, while Azure Monitor tracks live performance, ensuring AI agents operate optimally.

With these capabilities, Azure equips enterprises to harness the full potential of generative AI automation. 

The Azure advantage for generative AI innovation

Migrating to the cloud isn’t just a technical upgrade, it’s a strategic move for companies that want to lead in 2025 and beyond. By partnering with Azure, organizations can seamlessly connect AI models to critical data sources, applications, and workflows, integrating generative AI to drive tangible business outcomes. Azure’s infrastructure gives IT teams the tools to move fast and stay secure at scale. By shifting to a cloud-enabled AI environment, companies are positioning themselves to fully harness the power of AI and thrive in the era of intelligent automation. 

Accelerating Generative AI Innovation with Cloud Migration
Access the whitepaper to learn more about how enterprise can realize the promise of generative AI with Azure. 

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Quelle: Azure

Project Flash update: Advancing Azure Virtual Machine availability monitoring

Previously, we shared an update on Project Flash as part of our Advancing Reliability blog series, reaffirming our commitment to helping Azure customers detect and diagnose virtual machine (VM) availability issues with speed and precision. This year, we’re excited to unveil the latest innovations that take VM availability monitoring to the next level—enabling customers to operate their workloads on Azure with even greater confidence. I’ve asked Yingqi (Halley) Ding, Technical Program Manager from the Azure Core Compute team, to walk us through the newest investments powering the next phase of Project Flash.
— Mark Russinovich, CTO, Deputy CISO, and Technical Fellow, Microsoft Azure.

Project Flash is a cross-division initiative at Microsoft. Its vision is to deliver precise telemetry, real-time alerts, and scalable monitoring—all within a unified, user-friendly experience designed to meet the diverse observability needs of virtual machine (VM) availability.

Flash addresses both platform-level and user-level challenges. It enables rapid detection of issues originating from the Azure platform, helping teams respond quickly to infrastructure-related disruptions. At the same time, it equips you with actionable insights to diagnose and resolve problems within your own environment. This dual capability supports high availability and helps ensure your business Service-Level Agreements are consistently met. It’s our mission to ensure you can:

Gain clear visibility into disruptions, such as VM reboots and restarts, application freezes due to network driver updates, and 30-second host OS updates—with detailed insights into what happened, why it occurred, and whether it was planned or unexpected.

Analyze trends and set alerts to speed up debugging and track availability over time.

Monitor at scale and build custom dashboards to stay on top of the health of all resources.

Receive automated root cause analyses (RCAs) that explain which VMs were affected, what caused the issue, how long it lasted, and what was done to fix it.

Receive real-time notifications for critical events, such as degraded nodes requiring VM redeployment, platform-initiated service healing, or in-place reboots triggered by hardware issues—empowering your teams to respond swiftly and minimize user impact.

Adapt recovery policies dynamically to meet changing workload needs and business priorities.

During our team’s journey with Flash, it has garnered widespread adoption from some of the world’s leading companies spanning from e-commerce, gaming, finance, hedge funds, and many other sectors. Their extensive utilization of Flash underscores its effectiveness and value in meeting the diverse needs of high-profile organizations.

At BlackRock, VM reliability is critical to our operations. If a VM is running on degraded hardware, we want to be alerted quickly so we have the maximum opportunity to mitigate the issue before it impacts users. With Project Flash, we receive a resource health event integrated into our alerting processes the moment an underlying node in Azure infrastructure is marked unallocatable, typically due to health degradation. Our infrastructure team then schedules a migration of the affected resource to healthy hardware at an optimal time. This ability to predictively avoid abrupt VM failures has reduced our VM interruption rate and improved the overall reliability of our investment platform.
— Eli Hamburger, Head of Infrastructure Hosting, BlackRock.

Learn more about Project Flash

Suite of solutions available today

The Flash initiative has evolved into a robust, scalable monitoring framework designed to meet the diverse needs of modern infrastructure—whether you’re managing a handful of VMs or operating at massive scale. Built with reliability at its core, Flash empowers you to monitor what matters most, using the tools and telemetry that align with your architecture and operational model.

Flash publishes VM availability states and resource health annotations for detailed failure attribution and downtime analysis. The guide below outlines your options so you can choose the right Flash monitoring solution for your scenario.

SolutionDescriptionAzure Resource Graph (general availability)For investigations at scale, centralized resource repositories, and historical lookups, you can periodically consume resource availability telemetry across all workloads at once using Azure Resource Graph (ARG).Event Grid system topic (public preview)To trigger time-sensitive and critical mitigations, such as redeploying or restarting VMs to prevent end-user impact, you can receive alerts within seconds of critical changes in resource availability via Event Handlers in Event Grid.Azure Monitor – Metrics (public preview)To track trends, aggregate platform metrics (e.g., CPU, disk), and configure precise threshold-based alerts, you can consume an out-of-the-box VM availability metric via Azure Monitor.Resource Health (general availability)To perform instantaneous and convenient per-resource health checks in the Portal UI, you can quickly view the RHC blade. You can also access a 30-day historical view of health checks for that resource to support fast and effective troubleshooting.

Figure 1: Flash endpoints

What’s new?

Public preview: User vs platform dimension introduced for VM availability metric

Many customers have emphasized the need for user-friendly monitoring solutions that provide real-time, scalable access to compute resource availability data. This information is essential for triggering timely mitigation actions in response to availability changes.

Designed to satisfy this critical need, the VM availability metric is well-suited for tracking trends, aggregating platform metrics (such as CPU and disk usage), and configuring precise threshold-based alerts. You can utilize this out-of-the-box VM availability metric in Azure Monitor.

Figure 2: VM availability metric

Now you can use the Context dimension to identify whether VM availability was influenced by Azure or user-orchestrated activity. This dimension indicates, during any disruption or when the metric drops to zero, whether the cause was platform-triggered or user-driven. It can assume values of Platform, Customer, or Unknown.

Figure 3: Context dimension

The new dimension is also supported in Azure Monitor alert rules as part of the filtering process.

Figure 4: Azure Monitor alert rule

Public preview: Enable sending health resources events to Azure Monitor alerts in Event Grid

Azure Event Grid is a highly scalable, fully managed Pub/Sub message distribution service that offers flexible message consumption patterns. Event Grid enables you to publish and subscribe to messages to support Internet of Things (IoT) solutions. Through HTTP, Event Grid enables you to build event-driven solutions, where a publisher service (such as Project Flash) announces its system state changes (events) to subscriber applications.

Figure 5: Event Grid system topics

With the integration of Azure Monitor alerts as a new event handler, you can now receive low-latency notifications—such as VM availability changes and detailed annotations—via SMS, email, push notifications, and more. This combines Event Grid’s near real-time delivery with Azure Monitor’s direct alerting capabilities.

Figure 6: Event Grid subscription

To get started, simply follow the step-by-step instructions and begin receiving real-time alerts with Flash’s new offering.

What’s next?

Looking ahead, we plan to broaden our focus to include scenarios such as inoperable top-of-rack switches, failures in accelerated networking, and new classes of hardware failure prediction. In addition, we aim to continue enhancing data quality and consistency across all Flash endpoints—enabling more accurate downtime attribution and deeper visibility into VM availability.

For comprehensive monitoring of VM availability—including scenarios such as routine maintenance, live migration, service healing, and degradation—we recommend leveraging both Flash Health events and Scheduled Events (SE).

Flash Health events offer real-time insights into ongoing and historical availability disruptions, including VM degradation. This facilitates effective downtime management, supports automated mitigation strategies, and enhances root cause analysis.

Scheduled Events, in contrast, provide up to 15 minutes of advance notice prior to planned maintenance, enabling proactive decision-making and preparation. During this window, you may choose to acknowledge the event or defer actions based on your operational readiness.

For upcoming updates on the Flash initiative, we encourage you to follow the advancing reliability series!
The post Project Flash update: Advancing Azure Virtual Machine availability monitoring appeared first on Microsoft Azure Blog.
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Microsoft Azure AI Foundry Models and Microsoft Security Copilot achieve ISO/IEC 42001:2023 certification

Microsoft has achieved ISO/IEC 42001:2023 certification—a globally recognized standard for Artificial Intelligence Management Systems (AIMS) for both Azure AI Foundry Models and Microsoft Security Copilot. This certification underscores Microsoft’s commitment to building and operating AI systems responsibly, securely, and transparently. As responsible AI is rapidly becoming a business and regulatory imperative, this certification reflects how Microsoft enables customers to innovate with confidence.

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Raising the bar for responsible AI with ISO/IEC 42001

ISO/IEC 42001, developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), establishes a globally recognized framework for the management of AI systems. It addresses a broad range of requirements, from risk management and bias mitigation to transparency, human oversight, and organizational accountability. This international standard provides a certifiable framework for establishing, implementing, maintaining, and improving an AI management system, supporting organizations in addressing risks and opportunities throughout the AI lifecycle.

By achieving this certification, Microsoft demonstrates that Azure AI Foundry Models, including Azure OpenAI models, and Microsoft Security Copilot prioritize responsible innovation and are validated by an independent third party. It provides our customers with added assurance that Microsoft Azure’s application of robust governance, risk management, and compliance practices across Azure AI Foundry Models and Microsoft Security Copilot are developed and operated in alignment with Microsoft’s Responsible AI Standard.

Supporting customers across industries

Whether you are deploying AI in regulated industries, embedding generative AI into products, or exploring new AI use cases, this certification helps customers:

Accelerate their own compliance journey by leveraging certified AI services and inheriting governance controls aligned with emerging regulations.

Build trust with their own users, partners, and regulators through transparent, auditable governance evidenced with the AIMS certification for these services.

Gain transparency into how Microsoft manages AI risks and governs responsible AI development, giving users greater confidence in the services they build on.

Engineering trust and responsible AI into the Azure platform

Microsoft’s Responsible AI (RAI) program is the backbone of our approach to trustworthy AI and includes four core pillars—Govern, Map, Measure, and Manage—which guides how we design, customize, and manage AI applications and agents. These principles are embedded into both Azure AI Foundry Models and Microsoft Security Copilot, resulting in services designed to be innovative, safe and accountable.

We are committed to delivering on our Responsible AI promise and continue to build on our existing work which includes:

Our AI Customer Commitments to assist our customers on their responsible AI journey.

Our inaugural Responsible AI Transparency Report that enables us to record and share our maturing practices, reflect on what we have learned, chart our goals, hold ourselves accountable, and earn the public’s trust.

Our Transparency Notes for Azure AI Foundry Models and Microsoft Security Copilot help customers understand how our AI technology works, its capabilities and limitations, and the choices system owners can make that influence system performance and behavior.

Our Responsible AI resources site which provides tools, practices, templates and information we believe will help many of our customers establish their responsible AI practices.

Supporting your responsible AI journey with trust

We recognize that responsible AI requires more than technology; it requires operational processes, risk management, and clear accountability. Microsoft supports customers in these efforts by providing both the platform and the expertise to operational trust and compliance. Microsoft remains steadfast in our commitment to the following:

Continually improving our AI management system.

Understanding the needs and expectations of our customers.

Building onto the Microsoft RAI program and AI risk management.

Identifying and actioning upon opportunities that allow us to build and maintain trust in our AI products and services. 

Collaborating with the growing community of responsible AI practitioners, regulators, and researchers on advancing our responsible AI approach.  

ISO/IEC 42001:2023 joins Microsoft’s extensive portfolio of compliance certifications, reflecting our dedication to operational rigor and transparency, helping customers build responsibly on a cloud platform designed for trust. From a healthcare organization striving for fairness to a financial institution overseeing AI risk, or a government agency advancing ethical AI practices, Microsoft’s certifications enable the adoption of AI at scale while aligning compliance with evolving global standards for security, privacy, and responsible AI governance.

Microsoft’s foundation in security and data privacy and our investments in operational resilience and responsible AI shows our dedication to earning and preserving trust at every layer. Azure is engineered for trust, powering innovation on a secure, resilient, and transparent foundation that gives customers the confidence to scale AI responsibly, navigate evolving compliance needs, and stay in control of their data and operations.

Learn more with Microsoft

As AI regulations and expectations continue to evolve, Microsoft remains focused on delivering a trusted platform for AI innovation, built with resiliency, security, and transparency at its core. ISO/IEC 42001:2023 certification is a critical step on that path, and Microsoft will continue investing in exceeding global standards and driving responsible innovations to help customers stay ahead—securely, ethically, and at scale.

Explore how we put trust at the core of cloud innovation with our approach to security, privacy, and compliance at the Microsoft Trust Center. View this certification and report, as well as other compliance documents on the Microsoft Service Trust Portal.

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The ISO/IEC 42001:2023 certification for Azure AI Foundry: Azure AI Foundry Models and Microsoft Security Copilot was issued by Mastermind, an ISO-accredited certification body by the International Accreditation Service (IAS). 

The post Microsoft Azure AI Foundry Models and Microsoft Security Copilot achieve ISO/IEC 42001:2023 certification appeared first on Microsoft Azure Blog.
Quelle: Azure

Databricks runs best on Azure

Azure Databricks has clear advantages over other cloud service providers

This blog is a supplement to the Azure Databricks: Differentiated Synergy blog post and continues to define the differentiation for Azure Databricks in the cloud data analytics and AI landscape.

Azure Databricks: Powering analytics for the data-driven enterprise

In today’s data-driven world, organizations are seeking analytics platforms that simplify management, offer seamless scalability, and deliver consistent performance. While Databricks is available across major cloud service providers (CSPs), not all implementations are equal. Azure Databricks is a first party Microsoft offering co-engineered by Microsoft and Databricks, which stands out for its superior integration, performance, and governance capabilities. It not only delivers strong performance for workloads like decision support systems (DSSs), but it also seamlessly integrates with the Microsoft ecosystem, including solutions such as Azure AI Foundry, Microsoft Power BI, Microsoft Purview, Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Entra ID, Microsoft Fabric, and much more. Choosing Azure Databricks can streamline your entire data lifecycle—from data engineering and Extract Transform Load (ETL) workloads to machine learning (ML), AI, and business intelligence (BI)—within a single, scalable environment.

Maximize the value of your data assets for all analytics and AI use cases

Performance that matters

Principled Technologies (PT), a third-party technology assessment firm, recently analyzed the performance of Azure Databricks and Databricks on Amazon Web Services (AWS). PT stated that Azure Databricks, the Microsoft first-party Databricks service, outperformed Databricks on AWS—it was up to 21.1% faster for single query streams and saved over 9 minutes on four concurrent query streams.

Faster execution for a single query stream demonstrates the better experience a lone user would have. For example, data engineers, scientists, and analysts, and other key users could save time when running multiple detailed reports, tasking the system to handle heavy analytical queries without resource competition.

Faster concurrent query performance demonstrates the better experience multiple users would have while running analyses at the same time. For example, your analysts from different departments can save time when running reports or dashboards simultaneously, sharing cluster resources.

With or without autoscale?1, 2

If cost is a top priority, we recommend autoscaling your Azure Databricks cluster. When certain parts of your data pipeline are more computationally intensive, autoscale enables Azure Databricks to add compute resources and then remove them when the intensity cools down. This can help reduce your costs compared to static compute sizing. Considering the total cost of ownership (TCO) for data and AI platforms is essential, in addition to their integration and optimization capabilities combined with data gravity. An autoscaling cluster is often the most cost-effective option, though it may not be the fastest. If consistent performance is a top priority, consider disabling autoScale.

Key differences: Azure Databricks versus Databricks on other clouds deployed as third party

While all three CSPs offer Databricks, several factors distinguish Azure Databricks:

Underlying infrastructure: Azure Databricks is deeply optimized for Azure Data Lake Storage (ADLS), while AWS uses S3 and Google Cloud uses its own storage solution.

Control plane: Management layers differ, affecting billing, access control, and resource management.

Ecosystem integrations: Azure Databricks natively integrates with Microsoft services like Power BI, Microsoft Fabric, Microsoft Purview, Azure AI Foundry, Power Platform, Copilot Studio, Entra ID, and more.

Pricing: Each CSP has different pricing models, so it’s important to calculate projected costs based on your needs.

Azure-Native features: Anchoring data and AI

Azure Databricks delivers a range of Azure-native features that streamline analytics, governance, and security:

Centralized billing and support: Manage everything through the Azure portal, with unified support from Microsoft and Databricks.

Identity and access management: Use Microsoft Entra ID for seamless authentication and Azure role-based access control (RBAC) for fine-grained access control.

Azure DevOps integration: Native support for Git (Azure Repos) and continuous integration and continuous delivery/deployment (CI/CD) (Azure Pipelines) simplifies deployment and collaboration.

Credential passthrough: Enforces user-specific permissions when accessing ADLS.

Azure Key Vault: Securely manage secrets directly within Databricks notebooks.

ML integration: Deep integration with Azure Machine Learning for experiment tracking, model registry, and one-click deployment from Databricks to Azure ML endpoints.

Azure confidential computing: Protect data in use with hardware-based Trusted Execution Environments, preventing unauthorized access—even by cloud operators.

Azure Monitor: After signing on with Microsoft Entra ID, users can access Azure Databricks, Azure Data Lake Storage, and Azure Monitor from a single pane of glass for an efficient, cohesive, and secure analytics ecosystem in Azure.

Cross-cloud governance: One platform, multiple clouds

Azure Databricks now supports cross-cloud data governance, allowing direct access and management of AWS S3 data via Unity Catalog—without the need for data migration or duplication. This unified approach means you can standardize policies, access controls, and auditing across both Azure and AWS, simplifying operations and enhancing security in hybrid and multicloud environments.

Seamless integration with the Microsoft ecosystem

Azure Databricks is the only Databricks offering that is deeply integrated with the Microsoft ecosystem and some latest integrations are as follows:

Mirrored Azure Databricks Catalog in Microsoft Fabric: This feature enables access to Databricks Unity Catalog metadata and tables directly from Microsoft Fabric, enabling unified governed analytics and eliminating the need for data movement or duplication, especially for serving to Power BI via Direct Lake mode. 

Power Platform Connector: Instantly connect Power Apps, Power Automate, and Copilot Studio to Azure Databricks, enabling real-time, governed access to enterprise data and empowering users to build intelligent, data-driven applications without custom configuration or data duplication.

Azure AI Foundry data connection: Native connector that allow organizations to leverage real-time Azure Databricks data for building responsible, governed AI solutions.

What it means to you

Azure Databricks offers exceptional performance, cost efficiency, and deep integration with Microsoft’s trusted cloud ecosystem and solutions. With features like centralized management, advanced security, cross-cloud governance, and performance advantages, organizations can scale their analytics and AI workloads, unlock faster insights, and drive operational efficiency with Azure Databricks.

Get started with Azure Databricks today and experience why it’s the best home for your data and AI workloads.

 Check out the full Principled Technologies report for more information on Azure Databricks performance.

Explore how Azure Databricks functions and find additional information about the service via Databricks.com.

Learn more about why Databricks runs best on Azure:

Azure Databricks: Differentiated synergy.

5 Reasons Why Azure Databricks is the Best Data + AI Platform on Azure.

Explore and get started with the Azure Databricks Skilling Plan.

Azure Databricks Essentials—Virtual Workshop.

E-Book: Experimentation and AI with Azure Databricks.

E-Book: Modernize Your Data Estate by Migrating to Azure Databricks.

What’s New with Azure Databricks: Unified Governance, Open Formats, and AI-Native Workloads | Databricks Blog.

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1Azure, “Best practices for cost optimization,” June 6, 2025, https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/cost-optimization/best-practices.

2Azure, “Best practices for performance efficiency,” June 6, 2025, https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/performance-efficiency/best-practices.

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Quelle: Azure

Reasoning reimagined: Introducing Phi-4-mini-flash-reasoning

State of the art architecture redefines speed for reasoning models

Microsoft is excited to unveil a new edition to the Phi model family: Phi-4-mini-flash-reasoning. Purpose-built for scenarios where compute, memory, and latency are tightly constrained, this new model is engineered to bring advanced reasoning capabilities to edge devices, mobile applications, and other resource-constrained environments. This new model follows Phi-4-mini, but is built on a new hybrid architecture, that achieves up to 10 times higher throughput and a 2 to 3 times average reduction in latency, enabling significantly faster inference without sacrificing reasoning performance. Ready to power real world solutions that demand efficiency and flexibility, Phi-4-mini-flash-reasoning is available on Azure AI Foundry, NVIDIA API Catalog, and Hugging Face today.

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Efficiency without compromise 

Phi-4-mini-flash-reasoning balances math reasoning ability with efficiency, making it potentially suitable for educational applications, real-time logic-based applications, and more. 

Similar to its predecessor, Phi-4-mini-flash-reasoning is a 3.8 billion parameter open model optimized for advanced math reasoning. It supports a 64K token context length and is fine-tuned on high-quality synthetic data to deliver reliable, logic-intensive performance deployment.  

What’s new?

At the core of Phi-4-mini-flash-reasoning is the newly introduced decoder-hybrid-decoder architecture, SambaY, whose central innovation is the Gated Memory Unit (GMU), a simple yet effective mechanism for sharing representations between layers.  The architecture includes a self-decoder that combines Mamba (a State Space Model) and Sliding Window Attention (SWA), along with a single layer of full attention. The architecture also involves a cross-decoder that interleaves expensive cross-attention layers with the new, efficient GMUs. This new architecture with GMU modules drastically improves decoding efficiency, boosts long-context retrieval performance and enables the architecture to deliver exceptional performance across a wide range of tasks. 

Key benefits of the SambaY architecture include: 

Enhanced decoding efficiency.

Preserves linear prefiling time complexity.

Increased scalability and enhanced long context performance.

Up to 10 times higher throughput.

Our decoder-hybrid-decoder architecture taking Samba [RLL+25] as the self-decoder. Gated Memory Units (GMUs) are interleaved with the cross-attention layers in the cross-decoder to reduce the decoding computation complexity. As in YOCO [SDZ+24], the full attention layer only computes the KV cache during the prefilling with the self-decoder, leading to linear computation complexity for the prefill stage.

Phi-4-mini-flash-reasoning benchmarks 

Like all models in the Phi family, Phi-4-mini-flash-reasoning is deployable on a single GPU, making it accessible for a broad range of use cases. However, what sets it apart is its architectural advantage. This new model achieves significantly lower latency and higher throughput compared to Phi-4-mini-reasoning, particularly in long-context generation and latency-sensitive reasoning tasks. 

This makes Phi-4-mini-flash-reasoning a compelling option for developers and enterprises looking to deploy intelligent systems that require fast, scalable, and efficient reasoning—whether on premises or on-device. 

The top plot shows inference latency as a function of generation length, while the bottom plot illustrates how inference latency varies with throughput. Both experiments were conducted using the vLLM inference framework on a single A100-80GB GPU with tensor parallelism (TP) set to 1.

A more accurate evaluation was used where Pass@1 accuracy is averaged over 64 samples for AIME24/25 and 8 samples for Math500 and GPQA Diamond. In this graph, Phi-4-mini-flash-reasoning outperforms Phi-4-mini-reasoning and is better than models twice its size.

What are the potential use cases? 

Thanks to its reduced latency, improved throughput, and focus on math reasoning, the model is ideal for: 

Adaptive learning platforms, where real-time feedback loops are essential.

On-device reasoning assistants, such as mobile study aids or edge-based logic agents.

Interactive tutoring systems that dynamically adjust content difficulty based on a learner’s performance.

Its strength in math and structured reasoning makes it especially valuable for education technology, lightweight simulations, and automated assessment tools that require reliable logic inference with fast response times. 

Developers are encouraged to connect with peers and Microsoft engineers through the Microsoft Developer Discord community to ask questions, share feedback, and explore real-world use cases together. 

Microsoft’s commitment to trustworthy AI 

Organizations across industries are leveraging Azure AI and Microsoft 365 Copilot capabilities to drive growth, increase productivity, and create value-added experiences. 

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our responsible AI principles, with our product capabilities to unlock AI transformation with confidence.  

Phi models are developed in accordance with Microsoft AI principles: accountability, transparency, fairness, reliability and safety, privacy and security, and inclusiveness.  

The Phi model family, including Phi-4-mini-flash-reasoning, employs a robust safety post-training strategy that integrates Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Human Feedback (RLHF). These techniques are applied using a combination of open-source and proprietary datasets, with a strong emphasis on ensuring helpfulness, minimizing harmful outputs, and addressing a broad range of safety categories. Developers are encouraged to apply responsible AI best practices tailored to their specific use cases and cultural contexts. 

Read the model card to learn more about any risk and mitigation strategies.  

Learn more about the new model 

Try out the new model on Azure AI Foundry.

Find code samples and more in the Phi Cookbook.

Read the Phi-4-mini-flash-reasoning technical paper on Arxiv.

If you have questions, sign up for the Microsoft Developer “Ask Me Anything”. 

Create with Azure AI Foundry

Get started with Azure AI Foundry, and jump directly into Visual Studio Code.

Download the Azure AI Foundry SDK .

Take the Azure AI Foundry learn courses.

Review the Azure AI Foundry documentation.

Keep the conversation going in GitHub and Discord.

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Quelle: Azure