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New Azure Cobalt 200 VMs deliver 50% performance improvement, fully optimized for modern agentic AI workloads

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Building on the success of Cobalt 100 VMsWhat’s new in Cobalt 200 Arm-based VMsIndustry partners and customer adoptionDeveloper ecosystem and Arm compatibilityMicrosoft services powered by Cobalt 200 VMsVM families and specificationsThe next chapter in Azure’s custom silicon journeyAdditional resources

As organizations increasingly embrace the agentic era for AI, customer demands for compute are reshaping the architecture of cloud infrastructure as we know it. Today at Microsoft Build 2026, we are announcing the early access preview for Azure Cobalt 200 Arm-based Virtual Machines (VMs), designed from the ground up for scale-out, cloud-native, and Linux-based agentic AI workloads, with up to 50% better generational performance over Cobalt 100.

Cobalt 200 is purpose-built from silicon to servers to services—integrating Microsoft’s latest innovations in security, networking, storage, and offload to outperform traditional Arm-based compute. That hardware-software co-optimization lets us push the boundaries of scale, security, and cost for AI inferencing, data pipelines, and the web and API tiers that power modern services. Agents are unique from traditional workloads: they reason, make sequential decisions, and run continuously at scale—demanding a fundamentally different computational profile. Cobalt 200 is built for exactly this environment, delivering 50% performance gains for the workloads defining the next era of enterprise AI, making agents faster, more capable, and economically viable at scale.

Get early access to Azure Cobalt 200 VMs

Building on the success of Cobalt 100 VMs

Microsoft Azure Cobalt 100, our first custom-built processor for cloud-native workloads is now deployed in 32 Azure datacenter regions around the world, with cloud analytics leaders like Databricks and Snowflake adopting Cobalt 100 to optimize their cloud footprint, while customers like Amadeus, OneTrust, Siemens, Sprinklr, and Temenos have achieved significant real-world performance and efficiency gains.

Within Microsoft’s own cloud services, Azure Cobalt 100 VMs are delivering up to 45% better performance while using 35% fewer compute cores compared to its previous compute platform. Microsoft Defender for Endpoint (MDE) saw 40% better performance in its cyber data curator, enabling faster threat response at massive scale.

With Cobalt 200 VMs, we are applying everything we learned towards supporting these demanding services to raise the bar even further.

What’s new in Cobalt 200 Arm-based VMs

The Cobalt 200 CPU represents a significant leap forward in performance, security, and efficiency. At the heart of every Cobalt 200 VM is the Cobalt 200 System-on-Chip (SoC), our second-generation Arm processor built around the Arm Neoverse V3 Compute Subsystems, the highest-performance V-series core in Arm’s portfolio. Fabricated on TSMC’s 3nm (N3P) process, Cobalt 200 features a modern chiplet architecture, custom accelerators, and a custom memory controller.

Key innovations in Cobalt 200 VMs include:

Generational performance gains across compute, storage, and networking: Compared to previous-generation Cobalt 100 VMs, new Cobalt 200 VMs deliver up to 50% better CPU performance, 20% higher remote storage IOPS with NVMe, 10% better remote storage throughput with NVMe, and 15% higher network bandwidth, with improvements varying by workload.

Up to 128 vCPUs: Cobalt 200 VMs scale up to 128 vCPUs, delivering more compute capacity for demanding scale-out, cloud-native, agentic AI, and data-intensive workloads.

Faster remote storage and networking: Azure Boost integration helps improve remote storage IOPS and throughput with NVMe while increasing network bandwidth, benefiting distributed applications, storage-intensive services, and high-throughput data pipelines.

Advanced cache and scalable system design: A modern chiplet architecture with a larger cache hierarchy—including 3 MB of L2 cache per core and 192 MB of system-level L3 cache—keeps more active data closer to the workload, helping reduce latency and improve responsiveness for databases, in-memory caches, analytics engines, and other data-intensive services.

Stronger security by default: Memory encryption is enabled by default through a custom-designed memory controller, helping raise the baseline security posture for every workload with negligible performance impact.

Performance for agentic AI workloads

Cobalt 200 delivers the per-core performance and scalability needed to power modern agentic AI workloads. Each Cobalt 200 core is a full physical core, paired with dedicated 3 MB of L2 cache, and leading memory bandwidth per core. These design points enable higher isolation and sustained performance under load, which allow agentic workloads to pack more agent sandboxes per VM while meeting latency and throughput requirements.

Real cloud workload performance

Cobalt 200 VMs show a broad generational uplift over Cobalt 100 on the workloads that matter most in production.

With cloud workloads, we observe:

Up to 135% better performance for cloud database workloads.

Up to 40% better performance for web serving workloads.

Up to 45% better performance for communication encryption workloads.

And up to 80% better performance for caching workloads.

The uplift measured is visible in real hyperscale services. Taken together, these results show that Cobalt 200 raises the floor on per-core performance across cloud-native applications, databases, analytics, caches, and communications workloads.

Industry partners and customer adoption

We have been working closely with our technology partners and customers during the preview period to ensure Cobalt 200 Arm-based VMs deliver strong results across a wide range of real-world workloads. Leading software development companies and enterprises are already evaluating and adopting these VMs for their most demanding applications.

Teradata

Teradata is excited to be an early preview partner for Microsoft’s Cobalt 200 VMs. Microsoft has been a strong partner, and we value the opportunity to help shape the design and specifications to better meet the needs of our joint customers. Early testing has been encouraging, and we look forward to continued collaboration.
—Brandon Mincey, Engineering Fellow, Teradata

Elastic

Elastic is committed to delivering best-in-class performance with the Search AI Platform that powers observability, security, search, and AI solutions. Performance and cost efficiency are critical for teams running these workloads at scale and our initial testing of the new Cobalt 200 VMs shows promise for further improvements in these areas. We look forward to bringing these benefits to Elasticsearch users on Azure through our continued collaboration with Microsoft.
—Yuvraj Gupta, Director, Product Management, Elastic

Arm

Agentic AI is reshaping the cloud and creating demand for infrastructure that can efficiently orchestrate and scale millions of intelligent interactions in real time. Our collaboration with Microsoft on Cobalt 200, built on Arm Neoverse CSS V3, reflects how purpose-built Arm-based compute is enabling the next generation of AI-driven services while continuing to deliver exceptional performance for cloud-native applications.
—Eddie Ramirez, Vice President of go-to-market, Cloud AI Business Unit, Arm

Canonical

Cobalt 200 delivers key advances for production Linux workloads on Arm, including memory encryption enabled by default, built-in acceleration for compression and encryption, and higher throughput for data-intensive cloud services. Ubuntu gives organizations a consistent platform for cloud-native and agentic AI workloads across Azure, and Ubuntu Pro keeps it production-ready with long-term security maintenance and Livepatch, which now brings rebootless kernel updates to Arm so always-on services stay available.
—Jehudi Castro-Sierra, Public Cloud Alliance Director, Canonical

Developer ecosystem and Arm compatibility

The Arm developer ecosystem continues to thrive. Cobalt 200 Arm-based VMs deliver full compatibility for workloads currently running on Cobalt 100 VMs, making migration seamless. Major developer platforms and languages—including C++, .NET, Java, Python, and Rust—provide Arm-native versions with optimizations that fully leverage the capabilities of the Arm architecture.

The broader ecosystem has embraced Arm with native support across popular infrastructure and deployment solutions. GitHub Actions supports Arm through both self-hosted and GitHub-hosted runners. Azure Kubernetes Service (AKS) supports Arm agent nodes as well as mixed x86 and Arm architecture clusters. Containerized workloads benefit from the growing availability of Arm-native container images across the ecosystem.

Microsoft services powered by Cobalt 200 VMs

Microsoft’s own cloud services are among the first to adopt Cobalt 200 Arm-based VMs, building on the success of Cobalt 100 adoption across our most demanding, mission-critical workloads.

Dataverse

Dataverse is the core application and data platform underpinning Dynamics and Power Platform workloads, requiring high performance, scalability, and low latency to deliver a responsive customer experience.

We deployed the Power Apps platform on Cobalt 100 for its improved performance and power efficiency. We are validating Cobalt 200 now and excited by the performance gains we are seeing–up to 60% better performance for our base workload over Cobalt 100.
—Mauktik Gandhi, VP Engineering, Agent 365 Platform

Azure Databases

Azure SQL Database is a natural fit for Cobalt 200 Arm-based VMs. The built-in compression and cryptography accelerators are particularly impactful for database workloads—by offloading compression and encryption tasks to the Cobalt 200 accelerator, Azure SQL is able to reduce the use of critical compute resources, prioritizing them for customer queries and transactions.

Our teams collaborated from the beginning to ensure that the architecture of Cobalt 200 is optimized for hosting databases in Azure. We are excited about the performance gains we are seeing over Cobalt 100 and looking forward to broader availability later this year.
—Shireesh Thota, Corporate Vice President, Azure Databases

VM families and specifications

Cobalt 200 VMs significantly expand our Arm VM portfolio based on customer feedback to support a broader set of workloads. While Cobalt 100 offered General Purpose (Dp,Dpl), and Memory Optimized (Ep) VM families, Cobalt 200 adds two more VM families: the High-Memory Optimized Mpsv4 VMs and Dense Local Storage Lpsv5 VMs, bringing more choice across compute, memory, and storage profiles.

All VM series deliver up to 85 Gbps of network bandwidth and 70 Gbps of remote storage throughput, except Mpsv4/Mpdsv4, which provide up to 70 Gbps of network bandwidth and 46 Gbps of remote storage throughput. Most series are available with or without local NVMe disks, while Lpsv5 includes local NVMe storage across all sizes, giving customers flexibility to optimize for cost or performance.

VM familyvCPUsMem-to-vCPU Ratio Local NVMe storage Best-fit workloads General Purpose Dplsv7/Dpldsv7 1–1282:1Yes – Up to 7 TiBMost non-memory-intensive and scale-out workloads such as microservices, small databases, caches, gaming servers, and more.General Purpose Dpsv7/Dpdsv7 1–1284:1Yes – Up to 7 TiBMost scale-out enterprise workloads such as web and application servers, small to medium databases, caches, and more.Memory Optimized Epsv7/Epdsv7 1–1288:1Yes – Up to 7 TiBLarge relational and NoSQL databases, in-memory caches such as Redis and Memcached, and real-time analytics.(New) High Memory Optimized Mpsv4/Mpdsv41–8416:1Yes – Up to 4.4 TiBLarge in-memory databases, ERP systems, large-scale caching layers, and memory-intensive analytics workloads.(New) Storage Optimized Lpsv5 1–1288:1Yes – Up to 23 TBData pre-processing and staging, relational and NoSQL databases with local storage requirements, big data analytics, and search/index engines.

All Cobalt 200 VMs will support remote disk types including Standard SSD, Standard HDD, Premium SSD, and Ultra Disk storage. You can deploy these new VMs using existing methods, including the Azure portal, SDKs, APIs, PowerShell, and the command-line interface (CLI).

Availability

Cobalt 200 Arm-based VMs are now available in preview. The new VMs will be available in preview in the following regions: West US3, East US2, Central US, Sweden Central, East US, West US2, Spain Central, and Indonesia Central, with additional regions to be announced.

The next chapter in Azure’s custom silicon journey

The announcement of Cobalt 200 Arm-based Virtual Machines marks the next chapter in Azure’s purpose-built infrastructure journey. With our end-to-end systems approach—designing the CPU in-house and optimizing it for Azure’s infrastructure—we are delivering a tightly integrated platform that offers performance, power efficiency, and security for our customers.

Whether you’re accelerating product development, scaling analytics platforms, running mission-critical databases, or improving user experiences, Cobalt 200 Arm-based VMs offer a compelling choice for modern cloud workloads. We are excited to see how our customers and partners create breakthrough products and services on this new platform.

Try the Cobalt 200 Arm-based VMs today in early access preview and experience the next generation of Azure’s custom infrastructure innovation.

Thank you for joining us on this exciting journey.

Get early access to Azure Cobalt 200 VMs.

Additional resources

For questions, please go to Azure Support, and our experts will be there to help you.

Read Arm’s Cobalt 200 VMs preview supportive blog.

Read Canonical’s Cobalt 200 VMs preview supportive blog.

Run next-generation AI workloads with Azure Cobalt 200
Discover how Azure Cobalt 200 Arm-based VMs deliver high-performance, scalable compute for agentic AI, cloud-native, and data-intensive workloads.

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The post New Azure Cobalt 200 VMs deliver 50% performance improvement, fully optimized for modern agentic AI workloads appeared first on Microsoft Azure Blog.
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Microsoft Build 2026: Building agentic apps with Microsoft Fabric and Microsoft Databases

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Introducing Rayfin: From prompt to production backendMicrosoft Databases, designed for AI applicationsBuilding an AI‑ready data foundation with Microsoft FabricWatch these announcements from Microsoft BuildExplore additional resources for Microsoft Fabric

AI is driving a fundamental shift in how work gets done and how applications are built. As the 2026 Microsoft Work Trend Index Report highlights, a growing share of workers are moving beyond asking questions to handing off entire tasks and orchestrating multi-agent systems.

This shift introduces a new constraint. The challenge is no longer model capability, but consistent, shared data context across the business.

Developers and business users know what they want to automate, and today’s models can deliver. The bottleneck is context. Every new agent starts from zero, relearning how the business works, where data lives, and what rules to follow. Without a consistent foundation, agents can’t coordinate or scale.

That’s the challenge we are solving with Microsoft Fabric. It provides a unified data and AI platform that empowers you to bring together data and move from isolated AI experiments to production-ready agent systems, in which each new agent builds on shared organizational context. This vision is already driving strong momentum among the millions of developers building on Fabric and Microsoft Databases.

At Microsoft Build, we are extending this foundation with new capabilities that help developers move from prototype to production faster. These include Rayfin, a new software development kit (SDK) and command-line interface (CLI) designed to make Fabric a production-ready application backend, and Azure HorizonDB, a new PostgreSQL database designed for AI‑powered applications, now in public preview.

Unify teams and data with Microsoft Fabric

Introducing Rayfin: From prompt to production backend

Coding agents are accelerating app development. Moving those applications from prototype to production, however, remains a challenge. Agent-created or not, every production-ready application still relies on a backend to manage data, enforce identity and permissions, coordinate state, and operate reliably over time. Existing software-service platforms were either not designed for agents or do not fully meet enterprise requirements for deployment, security, and governance.

Rayfin, a new open-source SDK and CLI, is designed to close that gap. It lets developers and coding agents describe what to build and get an enterprise-grade application backend directly into the application code, including a database, authentication, and more. Rayfin then deploys directly to Microsoft Fabric, giving every application enterprise-grade security and scale from day one. Developers and AI agents can now move from prompt to production without managing infrastructure.

With Rayfin, developers work through familiar GitHub‑based workflows to define data models, backend logic, and access policies entirely in code, giving teams and agents a consistent, programmable interface for building and managing applications. Watch Rayfin in action:

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Because Rayfin can be deployed directly on Fabric, application data lands directly in OneLake, where it is immediately available to the full Fabric data stack, unified with analytics, operational and real-time data, and AI engines by default. This enables developers to build their enterprise apps on trusted business logic, integrate with semantic models, and embed rich data visuals.

We’re excited to partner with Replit, a leading AI coding platform, to help customers build enterprise-grade apps in the interface they know and love while keeping app, data, and services managed in their own Fabric tenant.

Rayfin unlocks a new development model for our users. Agents write the code. Fabric ships it quickly and safely. Together, we’re giving developers something they’ve never had before: a path from idea to enterprise-grade production that’s measured in hours, not months.

—Amjad Masad, Chief Executive Officer (CEO) of Replit

Learn more about Rayfin by watching the Microsoft Build session “BRK225 – Data, apps, and agents: the future of app dev with Microsoft Fabric” on Wednesday, June 3, 2026, at 1:30 PM PT.

Microsoft Databases, designed for AI applications

For decades, databases have been the backbone of enterprise applications. As applications become more intelligent and agent‑powered, we are evolving Microsoft Databases into the foundation optimized for real‑time, AI‑ready, and operationally rich experiences.

Azure HorizonDB: Enterprise-ready PostgreSQL built for the demands of AI applications

As a leading PostgreSQL committer, Microsoft has long invested in the PostgreSQL community. But as AI‑powered applications place new strains on scale, latency, and resilience, the demand for a new class of PostgreSQL database is clear.

Azure HorizonDB is that next step. Now available in public preview, HorizonDB is a fully managed, PostgreSQL‑compatible database that combines PostgreSQL familiarity with cloud‑scale architecture. It’s zone resilient by default and delivers elastic storage that scales to 128 TB, massive scale‑out compute up to 3,072 vCores, and can sustain sub‑millisecond, multi‑zone commit latency for demanding transactional scenarios.

Learn more about the Azure HorizonDB preview

As our data demands have expanded exponentially because of our use of Azure AI to chat with our data, HorizonDB has come at the perfect time to meet the performance, scale, and security we need to shift into this new world of AI-enabled data.

—Rand Morimoto, President of Convergent Computing (CCO)

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Beyond scalability, we’ve also infused Azure HorizonDB with experiences designed specifically for AI applications like vector search, integrated AI model management, and direct connectivity to Microsoft Foundry and Fabric. These features provide a modern foundation for building, modernizing, and scaling AI‑powered applications with confidence. Mohsin Shafqat, Director of Software Engineering at NASDAQ, mentioned, “What stood out with HorizonDB is that it aligns closely with how we already think about the problem. Instead of stitching together multiple components, it brings transactional data, vector search, and AI capabilities into a single platform, which simplifies the architecture without forcing a complete rethink.”

Learn more by watching the Microsoft Build session “BRK223 – From rows to reasoning: Designing databases for AI apps and agents” on Tuesday, June 2, 2026, at 2:30 PM PT.

New security and migration tooling for Azure Database for PostgreSQL

As Azure HorizonDB powers a new class of high-scale, mission‑critical applications built for AI, Azure Database for PostgreSQL remains a trusted, open‑source foundation for modernizing and operating existing PostgreSQL workloads.

Today’s updates introduce two meaningful enhancements. First is the Microsoft Defender for Cloud integration, now in preview, which delivers continuous security and compliance assessments to help teams identify misconfigurations and reduce risk. Second, we’ve released new discovery and assessment tooling to help you more confidently plan migrations to Azure Database for PostgreSQL. These tools evaluate Oracle and PostgreSQL environments and provide readiness insights, sizing guidance, and cost estimates. Learn more about this migration tooling in the Microsoft Build on-demand session “OD822 – Smarter PostgreSQL migrations to power modern, intelligent apps.”

Powering intelligent, multi‑agent systems at global scale with Azure Cosmos DB

Azure Cosmos DB is Microsoft’s AI-ready NoSQL and vector database for building responsive applications and intelligent AI experiences at any scale. OpenAI, for example, chose Azure Cosmos DB as its primary operational database “because of its automatic scaling and schema-less flexibility, allowing us to iterate quickly,” said Nick Cooper, senior technical staff member at OpenAI.

At Microsoft Build, we are focused on improving developer productivity and AI quality. The Azure Cosmos DB Linux Emulator is now generally available, enabling developers to build, test, and validate applications locally across Linux, macOS, and Windows without a cloud dependency. New AI capabilities are also now in preview, including semantic reranking, which improves search relevance using built‑in contextual understanding. In addition, a new agent memory toolkit helps developers standardize persistent memory for AI agents using Azure Cosmos DB, Azure Durable Functions, and Microsoft Foundry models. Learn more in the Microsoft Build on‑demand session “OD820 – Designing reliable multi-agent apps with Azure Cosmos DB.”

Unifying databases and Fabric on a single platform

Microsoft Databases can be centrally managed through the new Database Hub in Fabric, currently in private preview, and mirrored into OneLake, bringing operational and analytical data onto a single foundation. From there, you can use Fabric to make it trusted, contextual, and ready for AI.

Building an AI‑ready data foundation with Microsoft Fabric

In the era of AI, data is the fuel, but data alone is not enough. Equally important is how that data is understood: the definitions of customers, orders, products, revenue, and the relationships between them. Today, that understanding is fragmented across customer relationship management (CRM) and enterprise resource planning (ERP) systems, productivity tools, and spreadsheets, and too often it does not travel with the data. Organizations have long relied on people to recreate this context. But as agents take on more responsibility, this gap becomes critical. Without a shared understanding of the business, agents cannot reliably reason, coordinate, or act.

Microsoft IQ addresses this missing layer by unifying enterprise intelligence into a shared foundation built to activate AI agents. It enables consistent reasoning and enterprise-scale impact, rather than isolated interactions and and brings together four interconnected capabilities: Work IQ captures how work happens, Fabric IQ models how the business operates, Foundry IQ enables agents to discover and reuse knowledge, and the new Web IQ, announced today at Microsoft Build, adds real-time global context from the web.

Fabric IQ is central to this system. It powers a continuous operational loop where people and agents observe live signals, reason over shared context, and take governed action in the moment across analytics, operations, and the productivity tools where work happens. It provides three integrated layers of business context:

Unified data: OneLake unifies the organization’s data estate, spanning analytical and operational data into a single, accessible layer.

Business intelligence: Semantic models provide structured, governed representations of that data which organizations already rely on for trusted business metrics and analyze their business.

Operational intelligence: Ontologies capture operational context by defining business entities and their relationships so agents can reason in the language of the business. This context can include live signals from Fabric Real-Time Intelligence, enabling organizations and their agents to understand what is happening right now and act in time to change outcomes.

This three-tiered foundation helps ensure that every agent starts with the same understanding of the business and can apply it correctly across workflows. But Frontier organizations cannot start at the IQ layer. Building this capability requires a unified data foundation. Microsoft Fabric delivers this through four core capabilities:

Unifying your data estate

Processing and harmonizing data

Curating semantic meaning  

Empowering AI agents to act

Learn more about how Fabric can help you create an AI-ready data foundation in the Microsoft Build on-demand session “OD811 – Powering the next AI frontier with a unified data platform.”

1. Unifying your data estate with Microsoft OneLake

Most organizations struggle to see their entire data estate. It is spread across systems, duplicated in multiple places, and owned by different teams, making it difficult to know what exists, where it lives, and how it connects. Microsoft OneLake brings it together into a single, AI‑ready data lake that unifies your multi‑cloud estate and enables organization‑wide access for analytics and AI.

We are making it easier to connect existing data to OneLake without moving or duplicating it with the release of shortcuts to SharePoint and OneDrive into general availability and the ability to create shortcuts directly from Fabric Data Warehouses, now in preview. We are also adding the preview of workspace-level Azure Private Link support for mirrored data sources.

The general availability of the OneLake catalog in Microsoft Foundry

At the same time, we are making it easier to connect that data to AI. With the recent general availability of the OneLake catalog in Microsoft Foundry, you can discover trusted data, explore rich metadata, and connect it directly to AI solutions. The catalog is embedded within Foundry’s Knowledge experience, making it simple to move from data discovery to AI development in a single workflow.

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Learn more about all of these OneLake announcements by watching the Microsoft Build on-demand session “OD815 – Unify your entire data estate on a single, AI-ready data lake.”

2. Process data faster with a new class of GPU-accelerated analytics

Once data is unified, the next challenge is turning it into insights quickly, reliably, and at scale. We are introducing GPU-acceleration built directly into Fabric Data Warehouse to unlock a new level of performance without adding complexity.

The research behind this innovation was recently recognized by ACM SIGMOD as the “Best Industry Paper of 2026.” This breakthrough establishes Fabric Data Warehouse as the first fully managed data warehouse to offer GPU acceleration.

By integrating NVIDIA accelerated computing, query acceleration in Fabric Data Warehouse fundamentally changes how fast queries can run. In internal benchmarking conducted in May 2026, the GPU-accelerated Fabric Data Warehouse delivered up to 7x faster performance relative to three comparable external vendors for reporting and application workloads at 64-user concurrency.

This shift reflects a broader change in how modern data systems need to operate. As Ian Buck, Vice President of Hyperscale and HPC at NVIDIA, explains, “AI applications are redefining how a data warehouse needs to perform. As AI agents reason over enterprise data, analytics systems need low-latency performance for many simultaneous users. With NVIDIA accelerated computing and custom CUDA kernels built directly into Microsoft Fabric Data Warehouse, Microsoft is bringing the SQL workflows customers already use into the production AI era.”

Customers are already seeing that impact. At UNC Health, “We’re seeing up to 5x improvement in our query speeds, which allows our teams to spend less time managing performance and more time delivering meaningful insights,” said Shaun McDonald, IT Manager.

Query acceleration works automatically within Fabric, speeding up queries without requiring any query rewrites. The result is consistently low‑latency analytics that power responsive applications, interactive reporting, and agent‑driven analysis, delivering fresher insights and greater confidence in data.

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Query acceleration will be available for an early access preview in the next few weeks. You can learn more by watching the Microsoft Build on-demand session “OD813 – Powering modern data analytics in Fabric Data Warehouse.”

3. Curating semantic meaning with Fabric IQ, now generally available

Once data is prepared, the next challenge is not access but understanding. Most organizations lack a shared layer of business context, forcing every agent to relearn how the business works from fragmented data. Fabric IQ, now generally available, addresses this gap.

With data unified in OneLake, Power BI’s industry-leading semantic models then provide structured representations of your data for trusted business intelligence, serving as an ideal foundation for training agents.

Ontologies in Fabric IQ, expected to be generally available in the coming months, extend semantic models by adding operational context. They define business entities, relationships, properties, rules, and actions, and connect to live signals from Fabric Real-Time Intelligence. Operations agents, now generally available, then reason over shared live context, make decisions based on policy, and take action in the moment. Running on the governed foundation of Fabric and integrated with Microsoft Foundry, operations agents move beyond answering questions to driving action and outcomes.

Announcing the general availability of graph and planning in Fabric

We’re announcing the general availability of graph in Fabric, with general availability of the planning in Fabric coming later this month. Available today, graph introduces a highly scalable, relationship‑first model that connects business entities, systems, and signals so teams and agents can understand how changes propagate across the enterprise and act with full context. Planning extends this foundation by enabling organizations to create plans, budgets, forecasts, and scenario models on top of Fabric’s semantic models. Notably, planning in Fabric is not just static outputs. They can be written back into Fabric to drive execution, enabling closed-loop alignment with the same system of data and context.

Extending Fabric IQ to Microsoft Foundry and Agent 365

Today, we are extending Fabric IQ across the agent ecosystem so this shared understanding can be used consistently across every agent and application.

Microsoft Foundry and Agent 365 

Now in preview, Ontologies are accessible directly from Microsoft Foundry as knowledge sources, bringing trusted business context into both custom and built-in agent experiences. Also in preview, Fabric IQ is now integrated with Microsoft Agent 365 as a first-party model context protocol (MCP) tool, enabling organizations to ground agents in shared meaning and ensure consistent behavior across their agent estate.

Microsoft 365 Copilot: Cowork and Copilot Chat 

Fabric IQ is also extending into Microsoft 365 Copilot, including Cowork and Copilot Chat. This enables agents to access governed Fabric data, starting with Power BI reports and semantic models, to turn insights into action. Instead of static dashboards, agents can detect changes in key metrics, generate updates, trigger follow-ups, and schedule next steps, all grounded in trusted, governed data. The result is faster, more consistent execution across the organization. These experiences are currently available customers in Frontier with a Microsoft 365 Copilot license. Join the program today.

GitHub Copilot CLI 

Using Agent Skills for Fabric, Fabric IQ tools and skills for data insights are accessible through GitHub Copilot CLI, bringing governed semantic context directly into the terminal. Now you can also query Power BI reports and semantic models from the command line, grounded in governed semantic context in Fabric IQ. Teams can ask natural language questions about usage, metrics, or customer behavior and get answers grounded in Fabric data directly within their workflow, reducing back-and-forth and accelerating data-driven decisions. 

Learn more about these enhancements in the Build on‑demand session “OD812 – Bringing Enterprise Ontology Directly into the Developer Workflow.”

4. Empowering agents to act with operations agents and Copilot in Fabric

Enterprises are increasingly moving to multi-agent systems made up of specialized agents grounded in specific data or domain expertise. These agents can be reused across multiple systems, making it easier to scale and deliver more consistent outcomes.

Announcing the general availability of operations agents

Microsoft Fabric supports this shift with native agent capabilities, including Fabric data agents and operations agents. I’m excited to share operations agents are now generally available. These agents are designed to continuously monitor real-time data, detect patterns or anomalies, and act based on predefined business logic.

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Apply agentic analytics in Power BI

In addition, we recently released open-source Agent Skills for Fabric, designed to make it easier for AI tools and agents to interact directly with Fabric. These capabilities now extend to Power BI, enabling developers and analysts to create reports using natural language or even a screenshot of the desired outcome, significantly accelerating how insights are built and shared.

Copilot in Power BI can now also modify semantic models, with built-in recommendations that improve performance and make models more AI-ready. This helps teams iterate faster and deliver insights more quickly without leaving the tools they already use.

Learn more by reading the Power BI blog or by watching the Microsoft Build on-demand session “OD817 – Agentic analytics with Power BI and Microsoft Fabric.”

Watch these announcements from Microsoft Build

If you’re interested in learning more and seeing live demos on these announcements, I encourage you to watch the Microsoft Database and Microsoft Fabric sessions at Microsoft Build, available in the Microsoft Build catalog.

Tuesday, June 2, 2026

BRK223 – From rows to reasoning: Designing databases for AI apps and agents from 2:30 PM PT to 3:15 PM PT.

Wednesday, June 3, 2026

BRK225 – Data, apps, and agents: the future of app dev with Microsoft Fabric from 1:30 PM PT to 2:15 PM PT.

BRK224 – PepsiCo’s blueprint for agentic AI from 2:45 PM PT to 3:30 PM PT.

On-demand sessions (available now)

Click the links below to watch immediately.

Microsoft Databases

OD820 – Designing reliable multi-agent apps with Azure Cosmos DB

OD821 – Building Azure DocumentDB on open-source foundations

OD822 – Smarter PostgreSQL migrations to power modern, intelligent apps

OD823 – Faster AI Responses with Semantic Caching in Azure Managed Redis

OD824 – Scalable Applications Without Polyglot tax: Azure SQL Hyperscale

Microsoft Fabric

OD811 – Powering the next AI frontier with a unified data platform

OD812 – Bringing Enterprise Ontology Directly into the Developer Workflow

OD813 – Powering modern data analytics in Fabric Data Warehouse

OD815 – Unify your entire data estate on a single, AI-ready data lake

OD816 – Securing, scaling, and sustaining your data estate in Microsoft Fabric

OD817 – Agentic analytics with Power BI and Microsoft Fabric

OD818 – The AI-native data engineer

OD819 – Real-Time Intelligence: Bringing event-driven AI apps & agents

OD810 – Build fast, not fragile on Microsoft Fabric

Explore additional resources for Microsoft Fabric

Sign up for the Microsoft Fabric free trial.

View the updated Fabric Roadmap.

Try the Microsoft Fabric SKU Estimator.

Visit the Fabric website.

Join the Fabric community.

Read other in-depth, technical blogs on the Microsoft Fabric Updates Blog.

Get started with Microsoft Fabric

The post Microsoft Build 2026: Building agentic apps with Microsoft Fabric and Microsoft Databases appeared first on Microsoft Azure Blog.
Quelle: Azure

Announcing Microsoft Discovery general availability and Microsoft Discovery app preview

In this article

How Microsoft Discovery supports R&D workflows at scaleExpanding access with the Microsoft Discovery app previewApplying Microsoft Discovery across R&D

Breakthroughs in science and engineering rarely come from a single insight. They emerge through cycles of hypothesis, experimentation, refinement, and review across teams, tools, and data.

Today at Microsoft Build, we are announcing that Microsoft Discovery is now generally available for all organizations, providing a comprehensive platform for building and governing agentic AI workflows across scientific and engineering disciplines. We are also introducing the Microsoft Discovery app in preview, a local desktop experience that helps researchers, students, and scientific teams begin working with Microsoft Discovery today.

Explore Microsoft Discovery features

Since introducing Microsoft Discovery in private preview at Microsoft Build last year, we have worked closely with organizations applying AI to complex research and development (R&D) workflows. Their feedback helped reinforce where agentic AI needs to go beyond individual assistance, like supporting the iterative loops, evidence preservation, and tool coordination that define scientific work.

The most challenging problems in R&D require more than just a prompt interface or a single model response. Scientific workflows require:

Integration with institutional knowledge and domain expertise.

Access to specialized modeling, simulation, and analysis tools.

Connection to experimental evidence and validation data.

Support for review processes that shape research decisions.

A materials scientist may need to evaluate performance, safety, and cost alongside manufacturability and regulatory constraints. A semiconductor team may need to explore a larger design space without losing physical fidelity or traceability. A life sciences researcher may need to connect literature and experimental data with models and cohort-level evidence before deciding what to validate next.

Microsoft Discovery is designed to work within these existing R&D environments, not replace them. The platform helps experts understand the reasoning path behind outputs and keeps human judgment at the center of scientific and engineering decisions. The general availability of Microsoft Discovery marks a significant milestone in turning these requirements into a production-ready platform for R&D environments with governance and transparency built in.

Figure 1: The Microsoft Discovery workspace welcome experience.

How Microsoft Discovery supports R&D workflows at scale

Microsoft Discovery enables organizations to define agentic workflows around their own R&D programs. Teams can create and coordinate specialized agents, connect those agents to institutional knowledge and external scientific information, and orchestrate work across modeling, simulation, analysis, and validation tools.

At the center of the platform is the Microsoft Discovery Engine, which supports the core loop of scientific work by helping teams move from evidence to hypotheses, through execution and analysis, and into the next iteration. This loop allows teams to move beyond isolated analysis toward repeatable, evidence-driven exploration, where they can compare tradeoffs, question assumptions, and narrow a search space in a way that can be reviewed and repeated.

Figure 2: The Microsoft Discovery Engine task creation and status overview.

As we continued product development, we focused on what it takes to bring agentic AI into production R&D environments:

Workflows need to remain reproducible.

Outputs must be reviewable.

Proprietary knowledge must be connected and governed appropriately.

Agentic systems need to fit into the operating model of R&D organizations.

Those considerations, along with continued customer feedback, helped shape the general availability release and the platform capabilities behind it.

Figure 3: The Microsoft Discovery Engine output with confidence scoring and cited research findings.

Expanding access with the Microsoft Discovery app preview

An important goal for Microsoft is to make advanced AI and computing capabilities more accessible to the people working on some of today’s most difficult scientific and engineering challenges. Alongside the general availability of Microsoft Discovery, we are introducing the Microsoft Discovery app available in preview today.

The Microsoft Discovery app is a localized experience that gives researchers, students, academic labs, and scientific teams a simpler way to begin using Microsoft Discovery capabilities without starting with a full enterprise deployment. It is available for download on the Microsoft Discovery GitHub and users can get started with a GitHub Copilot account.

This preview extends Microsoft Discovery to earlier stages of exploration, where research ideas begin as small-team projects, academic work, or individual investigation. The Microsoft Discovery app is designed to lower the barrier to hands-on exploration, with a practical entry point for literature exploration, hypothesis generation, scientific reasoning, and iterative experimentation.

The app lets researchers explore Microsoft Discovery capabilities using their own working environment. As projects mature and complexity increases, researchers and teams can bring work developed locally into Microsoft Discovery platform to support more advanced R&D programs.

Figure 4: The Microsoft Discovery app welcome screen.

Applying Microsoft Discovery across R&D

During preview, organizations helped shape the path to general availability by sharing feedback on how they were using Microsoft Discovery to explore advanced R&D workflows grounded in domain-specific data, established research methods, and expert review.

Partners are contributing domain expertise and solution depth that can help organizations adapt Microsoft Discovery to the tools, data, and processes already central to their R&D work. Together, this work offers an early view into how Microsoft Discovery is being used across domains and how a growing ecosystem can help make complex R&D workflows more systematic, transparent, and repeatable.

Yale Engineering

A collaboration across Professor David Kwabi’s group at Yale Engineering and researchers from Microsoft used the Discovery Engine to advance the frontier of agentic small molecule design for grid-scale aqueous organic redox flow batteries (ORFBs).

ORFBs are promising, leading candidates for sustainable, environmentally friendly, long-duration energy storage, but challenging to optimize. Electrolytes must balance complex molecular properties like redox potential, aqueous solubility, synthetic tractability, and electrochemical reversibility. The Discovery Engine, building on our cognitive loop via in-situ optimization research, enables long-horizon scientific reasoning while ensuring trust in the entire process.

With these capabilities, the team used the agentic loop to drive in-silico exploration and convergence of candidates, interpret experimental results, and propose diagnostic experiments. Experts at Yale Engineering led all experimental characterization, verified results interpretation, and evaluated the practical applicability of the designs. The research is available here.

This work introduces a powerful new framework for advancing battery science with AI. By endowing an agent with the ability to reason from and adapt to experiments, we combine the strengths of human-led experimentation with AI’s capacity to explore vast chemical design spaces – and we’re only beginning to see what it can do.
—David Kwabi, Associate Professor, Yale

Read more about the Yale and Microsoft collaboration

Georgia Institute of Technology

Georgia Tech is exploring how an agentic AI system can re-evaluate the prebiotic plausibility of histidine, a biochemically important amino acid whose emergence under plausible prebiotic conditions remains unclear despite its ubiquity in biology. Classical machine learning and AI approaches have struggled in this domain due to the lack of standardized datasets and the inherently multimodal nature of the data.

The proposed scenario requires a multi-agent AI system composed of specialized AI ‘scientists’ for distinct data modalities, including mass spectrometry analysis, literature extraction, planetary mission data retrieval, and chemical reaction pathway modeling.

These agents will collaborate through a central reasoning coordinator to integrate diverse and heterogeneous datasets, aiming to move from “absence-of-evidence” to a robust, evidence-based assessment of histidine’s prebiotic viability. The framework developed can also be repurposed to investigate other contested biosignatures, building a scalable pipeline for origins-of-life inquiry.

Our collaboration with the Microsoft Discovery team through the Georgia Tech AI for Research program has been highly valuable, both scientifically and operationally. Working together on agentic AI systems to probe questions about the origins of life has given us early exposure to the state of the art embodied in the Discovery platform, while also enabling genuinely close technical collaboration. This hands-on partnership has enabled meaningful bidirectional learning.
—Dr. Amirali Aghazadeh, Assistant Professor, School of Electrical and Computer Engineering, Georgia Tech

Pacific Northwest National Laboratory

Microsoft and Pacific Northwest National Laboratory (PNNL) are rewriting the rules of scientific discovery, unleashing AI that doesn’t just assist researchers but orchestrates the entire discovery journey from new hypotheses to real-world experiments.

Powered by Microsoft Discovery, cutting-edge robotics and AI agents work like a virtual research team: imagining experiments, reasoning across mountains of scientific data, designing brand-new molecules, and learning on the fly from live laboratory results at PNNL.

In energy storage, this collaboration is fast-tracking the hunt for next-generation organic redox flow battery materials—breakthroughs that could slash our reliance on critical minerals like vanadium while providing cheaper, more scalable energy storage technologies that make our power grid tougher than ever.

In biosystems engineering, Microsoft Discovery is plugging directly into PNNL’s laboratory automation infrastructure to launch self-driving scientific workflows that autonomously design, run, and fine-tune biological experiments in real time.

Together, Microsoft and PNNL are pioneering a new model for science, where robotics and autonomous laboratories fuse with AI and cloud infrastructure into one intelligent, closed-loop discovery engine that dramatically reduces the timeline from ideas to breakthroughs and opens the door to a new era of innovation in energy, biology, and material synthesis.
—Robert Runkle, Physicist and Lead for Autonomous Discovery Strategy, Pacific Northwest National Laboratory

Ginkgo Bioworks

Ginkgo Bioworks and Microsoft are collaborating to bring agentic AI into biological discovery. Specialized agents can analyze biological datasets, generate hypotheses, and design experiments to execute on an autonomous lab. Soon, researchers will be able to scope and plan experiments in Microsoft Discovery and run them directly on Ginkgo Cloud Lab—no in-house automation required.

Together, agentic AI and autonomous labs will change every part of the scientific process. Iteration cycles will get faster, experiments will require less manual hands-on time, and computational analyses will become more systematic and exhaustive. By making both easier to use, Microsoft and Ginkgo aim to bring greater speed, scale and reproducibility to pre-clinical research.
—Jason Kelly, CEO, Ginkgo Bioworks, Inc.

Causaly

Causaly provides agentic solutions that compound the world’s biomedical evidence with an organization’s proprietary knowledge to deliver confident, traceable, cited decisions at every stage, from discovery through launch.

Drug discovery does not suffer from a lack of data. It suffers from a lack of trustworthy interpretation. Microsoft Discovery brings scientific computation over enterprise data, and Causaly brings the prior knowledge, mechanistic reasoning, and provenance needed to turn those signals into decisions. Together, we can help researchers move from raw data to evidence-backed judgment much faster and with greater confidence.
—Yiannis Kiachopoulos, Co-Founder and CEO, Causaly

Cambridge Consultants

With Microsoft Discovery, Cambridge Consultants is helping demonstrate how AI agents, simulation, and physical lab systems can work together in a closed-loop discovery process.

These autonomous, AI-powered cycles can turn months of experimental work into days or hours. The result is a more connected model for R&D, one designed to accelerate candidate generation, experimental planning, and real-world validation.

Microsoft Discovery has the potential to help researchers move faster from promising ideas to real-world results. We see this as an important step toward more scalable, integrated, and intelligent R&D.
—Joe Corrigan, Chief Technology Officer, Cambridge Consultants

Wiley

At every stage of the research and development process, life sciences and pharmaceutical teams need fast access to the most current, credible evidence available. Wiley Research Agent: Life Sciences delivers a continuously updated index of more than one million authoritative, high-quality, and trusted articles with hybrid search capabilities to support advanced scientific reasoning.

The agent searches, retrieves, and synthesizes relevant findings into a coherent, evidence-based response to queries. It can operate as a stand-alone research service, or in orchestration with other Microsoft Discovery agents, fitting naturally into the broader scientific reasoning workflows that Discovery enables. The Wiley Life Sciences Research Agent will be the first of several Wiley agents offered commercially on the Microsoft Discovery platform over time.

Scientific discovery depends on connecting trusted evidence with increasingly powerful AI systems. By bringing Wiley’s authoritative life sciences research into Microsoft Discovery, we can help life sciences and pharmaceutical teams accelerate hypothesis generation, experimentation, and results interpretation across a continuous scientific reasoning loop.
—Josh Jarrett, Senior Vice President and General Manager of Applied Research Intelligence at Wiley

BHP

BHP, the largest mining company in the world, is using Microsoft Discovery to accelerate discovery of advanced copper leaching solutions—in a matter of months instead of years.

As copper demand grows and new deposits become harder to find and more expensive to develop, improving recovery from existing ores is a critical lever to help meet future supply needs. This partnership has given our technical experts the tools they need to narrow an almost infinite field of possibilities down to a small number of options that could one day be deployed in our global copper operations. We are testing against the realities of our ore bodies and operating constraints, so we are solving for what can actually work in practice. This shows how technology and human expertise can be applied together to solve complex, real-world challenges.
—Jessica Farrell, Vice President Innovation, BHP

Syensqo

Syensqo is a global science company developing groundbreaking solutions that enhance the way we live, work, travel, and play. The company is currently leveraging Microsoft Discovery to scale agentic AI that accelerates discovery, improves decision-making, and unlocks measurable business impact, particularly in the development of next-generation heat transfer fluids for semiconductor manufacturing.

We are now entering a new phase of our partnership with Microsoft, focused on scaling AI agents across research, sales and marketing to drive near-term growth. By connecting customer demand to scientific development and back-to-market execution, agentic AI is enabling faster cycles, sharper prioritization, and tangible impact on revenue growth and business performance.
—Mike Radossich, CEO of Syensqo

GSK

GSK, the global biopharma, is working to accelerate the discovery, development, and delivery of medicines and vaccines to patients.

Working with partners like Microsoft Discovery, we see the opportunity to rapidly iterate on candidate molecules, potentially accelerating decision-making via rapid data generation and analysis.
—Christopher Austin, Senior Vice President, R&D Technologies, GSK

Figure 5: Microsoft Discovery has an expanding ecosystem of partners offering integrated tools and specialized expertise.

Get started today with Microsoft Discovery
Microsoft Discovery is now generally available for organizations ready to bring agentic AI into R&D workflows.

Review documentation

Microsoft Discovery is generally available. The Microsoft Discovery app is available in preview. Preview features and capabilities are subject to change.
The post Announcing Microsoft Discovery general availability and Microsoft Discovery app preview appeared first on Microsoft Azure Blog.
Quelle: Azure

Claude Fable 5 available today in Microsoft Foundry: Powering the next era of autonomous agents

Claude Fable 5, Anthropic’s latest frontier model, is available today in Microsoft Foundry, powering agents in GitHub Copilot and Foundry Agent Service. Claude Fable 5 makes Mythos-level capabilities available to all customers, with strong safeguards designed to make it safe for general use.

See what’s new in Claude Fable 5

Fable 5 is designed for long-running, multi-stage, and asynchronous tasks like complex code refactoring, deep research synthesis, and document-heavy workflows. This elevated level of autonomy changes what teams can ask AI to do. Enterprises can now delegate sophisticated multi-turn projects to agents, enabling them to reason over your organization’s data to solve real problems.

For enterprises, this frontier capability is only part of the equation. To turn autonomy into real business impact, organizations need a platform to evaluate, ground, govern, deploy, and scale these systems in production.

That is where Microsoft Foundry comes in. Foundry brings Anthropic’s next generation of intelligence into the broader Microsoft agent platform, helping enterprises build ambitious, high-impact AI solutions on Azure with the security, governance, reliability, and operational controls required for real business workflows.

Combined with Microsoft IQ, Fable 5 can reason over your team, your knowledge, and your data across Power BI, your applications, and the web, with a continuously updating view that learns as usage grows.

The next generation of frontier intelligence

According to Anthropic, Claude Fable 5 is the next generation of intelligence for the hardest knowledge work and coding problems. It can handle tasks previous models couldn’t sustain and represents a step change in what teams can hand off to Claude.

Claude Fable 5 can plan its approach, check progress against the goal, and refine its work as it goes, instead of waiting for the next instruction. This makes it well suited for complex coding tasks, research workflows, and long-running knowledge work where the model needs to reason across multiple steps.

With improved vision capabilities, Claude Fable 5 is also a strong fit for multimodal projects involving documents, PDFs, diagrams, charts, and dense tables. It does not just read the words on a page; it can interpret the meaning carried by visual and structured information. When critical context is embedded in a chart, schematic, or complex table, Claude Fable 5 can take it in and reason over it, helping finance, legal, analytics, and architecture teams work through material that is often reviewed manually today.

Claude Fable 5 real-world enterprise use cases

For business leaders, Claude Fable 5 changes how knowledge work is accomplished across the organization. Fable 5 is a frontier general-purpose model designed to help support complex, cross-functional tasks that span reasoning, creation, and execution.

Key enterprise use cases include:

Software development: Supporting demanding coding and system-level builds that stretch across days and stages, by carrying context from analysis through implementation and review.

Financial services: Building investment research, working through earnings, weighing credit and risk, and supporting compliance workflows—including the numbers locked inside long filings and their exhibits.

Legal: Marking up and reviewing contracts, streamlining due diligence, digging through case law, and producing first-pass motions and memos.

Beyond these, Claude Fable 5 also carries professional work across marketing, sales, and analytics, shaping strategy, drawing out insight, and turning it into decision-ready output as part of connected, end-to-end workflows.

Built with safeguards for responsible use

According to Anthropic, Claude Fable 5 is being introduced with additional safeguards that reflect the company’s approach to responsible AI development. Because the model has advanced capabilities in sensitive domains such as cybersecurity, biology, and chemistry, Anthropic has placed limits on how far the broadly available version will go in those areas. Beyond the model’s built-in safeguards, Microsoft Foundry provides developers with advanced guardrails and controls, as well as robust observability and security capabilities in Foundry Control Plane. Last week at Build, we announced guided guardrail setup, which walks developers through a few questions about an agent’s users, data, tools, and actions, then recommends and applies the right controls at the right intervention points, tailored to each agent’s scenario. These enterprise grade responsible AI features help developers govern, monitor, and manage AI systems and agent fleets.

For a small set of select users, including participants in Project Glasswing, Anthropic is also offering Claude Mythos 5. Mythos 5 is a version of the same model intended for internal, defensive use with those domain restrictions removed. This approach allows Anthropic to make most of Claude Fable 5’s frontier capabilities broadly available while continuing to refine safeguards for higher-risk use cases.

For enterprise customers, this means access to advanced autonomous capabilities with a clearer safety posture, which is an important consideration when deploying AI systems in real business environments.

Why Microsoft Foundry?

Access to a powerful model like Claude Fable 5 is only the starting point. The real challenge for enterprises is operationalizing autonomous AI securely, reliably, and in alignment with organizational policies.

As Jay Parikh, Executive Vice President of CoreAI at Microsoft, recently said:

The winners won’t be those with the most demos, but those that turn AI into a governed, continuously improving system for running real work.

That is why platform matters. Microsoft’s agent platform spanning GitHub, Microsoft IQ, Foundry, Agent 365, Azure, and Microsoft 365, gives organizations the foundation to build, ground, govern, and improve AI systems at scale.

Microsoft IQ plays a critical role by connecting agents to enterprise context across Microsoft 365, business systems, and knowledge bases, so models like Claude Fable 5 can reason with the right organizational context instead of operating in isolation.

With Microsoft Foundry, customers can turn frontier models into enterprise-ready solutions. They can evaluate and deploy Claude Fable 5 alongside other Claude models, apply platform-level security and governance controls, integrate AI into existing workflows, and scale from pilot to production with identity, access management, and operational controls built in.

Pricing

ModelInput/1M TokensOutput/1M TokensClaude Fable 5$10$50

Moving from experimentation to impact

Claude Fable 5’s arrival on Microsoft Foundry marks a milestone for enterprises ready to tackle more ambitious problems, streamline complex workflows, and enhance productivity across the organization. By bringing Claude Fable 5 to Foundry, Azure enables organizations to move faster, from experimentation to real, accelerated business impact without compromising on trust or control.

Ready to explore what autonomous AI can do for your organization? Access Claude Fable 5 today in Foundry Models, Foundry Agent Service, and GitHub Copilot.

Claude Fable 5
Unlock advanced agent capabilities for long-running tasks, enterprise workflows, and AI-driven productivity.

View model

The post Claude Fable 5 available today in Microsoft Foundry: Powering the next era of autonomous agents appeared first on Microsoft Azure Blog.
Quelle: Azure