Kategorie: Microsoft Azure

The data behind the design: How Pantone built agentic AI with an AI-ready database

When we talk about agentic AI, it’s easy to default to abstract conversations about models, prompts, and orchestration. But the most compelling stories I see are the ones where AI unlocks something deeply human—creativity, intuition, and expertise—at entirely new speed and scale.

That’s why I was excited to host Color Meets Code: Pantone’s Agentic AI Journey on Azure, a webinar featuring two Pantone leaders, Kristijan Risteski, solutions architect, and Rohani Jotshi, senior director of engineering. During the session, Kris and Rohani shared how they’re applying agentic AI to one of the most foundational elements of creative work: color—and how an AI-ready database, Azure Cosmos DB, plays a central role in making that possible.

Watch the Color Meets Code webinar

The challenge: Scaling color expertise in a real-time, interactive world

Pantone is widely recognized as a global authority on color. For decades, their teams have combined human expertise, color science, and trend forecasting to help designers and brands define, communicate, and control color across industries—from fashion and product design to packaging and digital experiences.

But as Pantone explained in the webinar, translating that depth of expertise into a modern, conversational AI experience came with real challenges. Creating color palettes is both time consuming and critical to the design process. Designers often gather inspiration by navigating between tools, color pickers, and trend reports before they ever land on a usable palette.

Pantone saw an opportunity to rethink that workflow entirely: What if designers could interact with decades of Pantone research, trend data, and color psychology through a chat-based interface—and generate curated palettes instantly?

Introducing the Palette Generator: An agentic AI experience

The result is Pantone’s Palette Generator, an AI-powered experience launched as a minimum viable product to gather real user feedback and iterate rapidly. Rather than offering static recommendations, the Palette Generator uses multiagent architecture to respond dynamically to user intent, conversational context, and historical interactions.

In the webinar, the Pantone team described how they designed the system to include specialized agents—such as a “chief color scientist” agent and a palette generation agent—each responsible for different aspects of reasoning, context retrieval, and response generation. These agents work together to deliver curated color palettes that reflect Pantone’s proprietary data and expertise.

What stood out to me was not just the sophistication of the AI, but the architectural discipline behind it. Agentic AI isn’t just about models—it’s also about data.

Why Azure Cosmos DB was foundational

At the heart of Pantone’s Palette Generator is Azure Cosmos DB, serving as the system’s real-time data layer. Azure Cosmos DB is used to store and manage chat history, prompt data, message collections, and user interaction insights—all of which are essential for responsive, fast, context-aware agents.

As we did our research to find the best persistence storage, we explored different databases. What we found for Azure Cosmos DB was how easy it was to integrate it into our systems. We were able to make our initial proof of concept with a few lines of code and retrieve all the data very, very fast, like in a few milliseconds.
Kristijan Risteski

Azure Cosmos DB was also chosen because of its scale, allowing Pantone to serve users all over the world with fast data retrieval.

This is a critical point. As applications shift from “doing” to “understanding,” databases must support far more than simple transactions. They need to handle massive volumes of operational data, adapt as AI workflows evolve, and support advanced scenarios like conversational memory, analytics, and vector-based search.

Pantone’s architecture demonstrates what it means to be “AI-ready.” Azure Cosmos DB provides the scalability and flexibility needed to track user prompts and conversations across sessions, along with analytics that help Pantone understand how customers interact with the Palette Generator over time.

From text to vectors—and what comes next

Another insight Pantone shared during the webinar was how their architecture is evolving to improve relevance, accuracy, and contextual understanding. While the current system already supports rich conversational experiences, the team outlined next steps that involve moving from traditional text storage to vector-based workflows. This includes embedding user prompts and contextual data, allowing for vector search, and enriching responses with deeper semantic understanding.

Azure Cosmos DB plays a role here as well, supporting vectorized data, integrating with agent orchestration, and embedding models deployed through Microsoft Foundry. This allows Pantone to iterate without rearchitecting the entire system—an essential capability when working in a fast-moving AI landscape.

Real-world results from agentic architecture

Pantone didn’t just talk about vision—they shared concrete results from real usage of the Palette Generator. According to the webinar data, users across more than 140 countries engaged with the tool, generating thousands of unique chats within the first month of release and interacting in dozens of languages. The system observed multiple queries per user session, indicating that designers were actively experimenting, refining prompts, and exploring ideas conversationally.

Just as importantly, Pantone emphasized how rapidly they’ve been able to learn and adapt. Prompt sensitivity, user behavior, and architectural tradeoffs around speed, cost, and reliability all informed ongoing refinements. Azure Cosmos DB’s flexibility made it possible to capture these insights and evolve the experience without slowing innovation.

Lessons for anyone building agentic AI

Pantone’s journey reinforces several lessons I see repeated across customers building AI agents on Azure:

Agentic AI is inherently data driven. Without a real-time, scalable database layer, even the most advanced models struggle to deliver consistent, context-aware experiences.

Feedback loops matter. By capturing prompts, responses, and user interactions in Azure Cosmos DB, Pantone can continuously improve both the AI and the product experience itself.

Flexibility is nonnegotiable. AI architectures evolve quickly—from orchestration patterns to embedding strategies—and databases must evolve with them.

What Pantone has built with the Palette Generator is more than a feature; it’s a blueprint for how organizations can translate deep domain expertise into intelligent, agent-driven applications. By combining Microsoft Foundry, Azure AI services, and an AI-optimized database like Azure Cosmos DB, Pantone is showing how creativity and technology can move forward together.

As more organizations embrace agentic AI, the question won’t be whether they can deploy models—but whether their data foundations are ready to support real-time understanding, memory, and scale. Pantone’s journey makes that answer clear: AI-ready applications start with AI-ready data.

Explore Azure Cosmos DB

The post The data behind the design: How Pantone built agentic AI with an AI-ready database appeared first on Microsoft Azure Blog.
Quelle: Azure

Enhanced storage resiliency with Azure NetApp Files Elastic zone-redundant service 

In today’s globally connected environment, even minutes of downtime can disrupt supply chains, hinder customer experiences, and affect regulatory commitments costing organizations thousands of dollars per minute and risking lasting reputational harm. Data resiliency is no longer optional—it is the foundation that keeps mission‑critical applications running, teams productive, and compliance intact. Organizations must ensure continuous data availability and zero data loss to meet stringent regulatory and audit standards.

Azure NetApp Files (ANF) is an Azure first party, enterprise-grade file storage offering in the cloud built to deliver unmatched performance, security with instant provisioning, and enterprise data management capabilities like snapshots, replication, backups, and flexible, cost-efficient service levels. It’s the trusted platform for critical workloads and the engine behind numerous migrations that drive Azure enterprise cloud journeys.

As businesses scale, the impact of service disruptions grows, and storage platforms must evolve to help maintain operational continuity. To support these needs, we are introducing Azure NetApp Files Elastic zone‑redundant storage (ANF Elastic ZRS)—a new service that provides enhanced resiliency across availability zones (AZs) and advanced data management capabilities. Built on Azure infrastructure, ANF Elastic ZRS can be deployed rapidly, accelerating availability across a broad set of regions. As a customer, you benefit from a service that can quickly adapt, whether that means expanding to a new Azure region you require, or adopting new technological advancements faster.

Synchronous replication and service managed failover

Built on Microsoft Azure’s zone-redundant storage (ZRS) architecture, ANF Elastic ZRS synchronously replicates data across multiple AZs within a region.

The graphic below shows the high-level architecture of ANF Elastic ZRS.

The following summarizes the operational methodology used: 

Synchronous replication: ANF Elastic zone-redundant storage (ZRS) volume replicates data within your NetApp Elastic Account to three or more AZs within the primary region of choice.

Service managed failover: If one AZ goes down (think single datacenter failure, power outage), ANF Elastic ZRS automatically routes traffic to the failover zone that was chosen during initial setup without customer intervention. The mount target and service endpoint remain the same, so your applications barely notice.

Zero data loss and high availability for mission-critical applications: ANF Elastic ZRS ensures uninterrupted operations for mission-critical applications and regulated systems by combining synchronous replication with service-managed failover. This architecture guarantees zero data loss and seamless continuity even during zone-level failures, meeting enterprise resiliency standards.

Capabilities for enterprise mission-critical applications

NFS, SMB protocol support, multi-AZ file service: ANF Elastic ZRS supports NFS and SMB independently with the added benefit of zonal redundancy.

Rich enterprise data management capabilities: Instant writeable space-efficient snapshots, clones, tiering, and backup integration all powered by the NetApp ONTAP® Unified data management platform.

Metadata performance: Beyond consistent throughput, ANF Elastic ZRS provides efficient handling of metadata operations such as rapid file creation and fast enumeration of numerous small files, improving responsiveness of metadata-heavy workloads. Its shared QoS architecture dynamically allocates IOPS across volumes to maintain low-latency, metadata-intensive operations consistently.

Cost optimized: ANF Elastic ZRS delivers multi-AZ high availability in a single volume at a lower cost than cross-zone replication with three separate ANF volumes, making it an efficient and valuable investment. You can create volumes as small as 1 GiB, giving you the flexibility to optimize storage for workloads of any size.

Apart from the above capabilities that are already supported, we will be adding the below capabilities in the future:

Simultaneous multi-protocol, multi-AZ file service: The service will support simultaneous NFS, SMB, and Object REST API access to the same dataset. This flexibility is crucial for environments where Windows and Linux workloads share data, such as analytics platforms and enterprise file shares.

Custom region pair for cross-region replication: ANF Elastic ZRS will be offering the flexibility to choose custom region pairs for cross-region replication, meeting strict compliance and disaster recovery requirements for large enterprise customers. This capability ensures business continuity while aligning with unique geographic or regulatory needs.

Migration Assistant: This capability will simplify data movement from on-premises or other ONTAP® systems to Elastic ZRS service level for seamless cloud adoption.

Targeted use cases

Use caseHow ANF Elastic ZRS helps
General file shares

Corporate user data including home directories or departmental file shares, remain accessible even during zone outages, ensuring seamless employee productivity and business continuity.

Financial services and trading platforms

Delivers zero data loss and nonstop trading even in a zone outage, so financial apps stay compliant, and customers stay confident.

Kubernetes/containerized applications

Keeps Kubernetes and container workloads running even during zone outages by instantly synchronizing data across zones and enabling rapid, automated failover. Your stateful apps stay online with zero data loss and minimal downtime.

Applications

Ensure nonstop access to in-house/line of business apps, even if an AZ fails, as data stays online with zero outages or lost transactions.

Innovation in action

A global healthcare enterprise running mission-critical application, on-premises is looking to modernize its infrastructure by moving mission-critical workloads to the cloud. The organization’s top priorities are eliminating downtime during maintenance and ensuring seamless failover to maintain application availability.

By adopting ANF Elastic ZRS, the organization gained a fully managed, zone-resilient storage solution that synchronously replicates data across three Azure AZs. This ensures zero data loss and near-instant failover, even during zone outages or platform maintenance. The mission-critical application remains highly available without requiring operational intervention, dramatically improving uptime, and simplifying infrastructure management.

ANF Elastic ZRS delivers the same enterprise-grade features as existing Azure NetApp Files service levels—such as NFS/SMB support, snapshots, encryption, and backup—all while removing the complexity of managing high availability (HA) clusters or VM-level failover. For the healthcare provider, this translates into higher SLA compliance, reduced operational overhead, and a more resilient foundation for its mission-critical application landscape.

Take the next step toward resilient storage

NetApp on premises customers: this is the inflection point you’ve been waiting for to move to Azure. ANF Elastic ZRS‑ brings the ONTAP®-powered data management you trust to a fully managed, multi-AZ file service, so you can modernize mission‑AZ file service, so you can modernize mission‑critical workloads with enterprise resilience, meet availability commitments, and reduce operational overhead—without re‑architecting your applications.

ANF Elastic ZRS is available now in these Azure regions and we will be rapidly expanding region availability.

Learn more:

Understand Azure NetApp Files Elastic zone-redundant storage

Create an SMB volume for Azure NetApp Files

Create an NFS volume for Azure NetApp Files

Comparison of Elastic vs other ANF service levels

Explore ANF Elastic ZRS in Azure today
Join the ranks of enterprises who demand the best in performance, availability, and simplicity.

Start experimenting

The post Enhanced storage resiliency with Azure NetApp Files Elastic zone-redundant service  appeared first on Microsoft Azure Blog.
Quelle: Azure

Claude Opus 4.6: Anthropic’s powerful model for coding, agents, and enterprise workflows is now available in Microsoft Foundry

At Microsoft we believe that intelligence and trust are the core requirements of agentic AI at scale. Built on Azure, Microsoft Foundry brings these capabilities together on a secure, scalable cloud foundation for enterprise AI. Today, with the launch of Claude Opus 4.6 in Microsoft Foundry, we bring even more capability to agents that increasingly learn from and act on business systems.

Explore Claude in Microsoft Foundry today

Claude Opus 4.6 brings Anthropic’s most advanced reasoning capabilities into Microsoft Foundry, our interoperable platform where intelligence and trust come together to enable autonomous work. In Foundry, Opus 4.6 can activate knowledge from everywhere: leveraging Foundry IQ to access data from M365 Work IQ, Fabric IQ, and the web. The model is best applied to complex tasks across coding, knowledge work, and agent-driven workflows, supporting deeper reasoning while offering superior instruction following for reliability.

Developers can now delegate their most complex work to AI systems for full-lifecycle development from, requirements gathering to implementation and maintenance. Business users can generate documents, perform research, and draft copy with professional polish and domain awareness.

At Adobe, we’re continuously evaluating new AI capabilities that can help us deliver more powerful, responsible, and intuitive experiences for our customers. We’ve been testing Claude models in Microsoft Foundry and are excited about the direction of Anthropic’s model roadmap. Foundry gives us a flexible, enterprise-ready environment to explore frontier models while maintaining the trust, governance, and scale that are critical for Adobe.
—Michael Marth, VP Engineering for Experience Manager and LLM Optimizer, Adobe

Introducing Opus 4.6: Frontier intelligence, built for real work

Claude Opus 4.6 is the latest version of Anthropic’s most intelligent model, and is considered the best Opus model for coding, enterprise agents, and professional work. With a 1M token context window (beta) and 128K max output, Opus 4.6 is ideal for:

Production code

Sophisticated agents

Office tasks

Financial analysis

Cybersecurity

Computer use

By combining Anthropic’s most advanced model with Foundry’s end-to-end tooling, customers can move from experimentation to production faster, without stitching together infrastructure.

1. Autonomous coding at a new level

Opus 4.6 handles large codebases well and is particularly effective at long-running tasks like refactoring, bug detection, and complex implementations.

This allows senior engineers to delegate work that previously took days, covering everything from requirements gathering to implementation and maintenance, while staying focused on reviews and decision-making.

With Foundry’s managed infrastructure and operational controls, teams can compress development timelines from days into hours, while maintaining the rigor required for real-world production systems.

Macroscope has relied on Anthropic’s frontier models to push what’s possible in AI code review, helping us catch the bugs that matter most to customers before they ever reach production. We’re incredibly excited to deepen this partnership by scaling on Azure infrastructure and delivering our product to teams around the world.
—Kayvon Beykpour, CEO & Co-Founder, Macroscope

2. Better knowledge work across the enterprise

Opus 4.6 delivers a step-change in how enterprises approach knowledge work, which Anthropic defines across three pillars: search, analyze, and create. In Microsoft Foundry, these capabilities can be applied directly to enterprise data, workflows, and agent-driven applications.

Opus 4.6 understands the conventions and norms of professional domains, producing documents, spreadsheets, and presentations that look and read like expert-created work. Combined with Foundry’s governance and access controls, this makes Opus 4.6 a great fit for finance, legal, and other precision-critical industries where quality, context, and compliance matter.

At Dentons, we are scaling generative AI across drafting, review, and research workflows across our global teams. Claude in Microsoft Foundry brings the frontier reasoning strength we need for legal work, backed by the governance and operational controls required in an enterprise environment. Better model reasoning reduces rework and improves consistency, so our lawyers can focus on higher value judgment.
—Matej Jambrich, CTO, Dentons Europe

High context financial analysis

Opus 4.6 excels at connecting insights across regulatory filings, market reports, and internal enterprise data, surfacing conclusions that would traditionally take analysts days to compile.

Its advanced reasoning capabilities allow it to:

Navigate nuanced financial and regulatory contexts

Generate compliance-sensitive outputs

Maintain consistency and traceability across complex analytical workflows

When deployed through Microsoft Foundry, these workflows benefit from Azure’s security, compliance, and auditability, helping organizations apply frontier AI to high-stakes analysis with confidence.

3. Advancing agentic and computer use capabilities

According to Anthropic, Opus 4.6 delivers major gains in computer use, with strong performance on industry benchmarks for visual understanding and multi-step navigation. Opus 4.6 sets a new standard for computer use. Claude can now operate computers more accurately, handle more complex tasks, and work across multiple applications seamlessly.

It can interact with software, navigate interfaces, complete forms, and move data across applications. When deployed in Microsoft Foundry, these actions can run as secure, governed agents, enabling automation of workflows that span legacy systems, document processing, and operational tools.

Opus 4.6 can manage complex, multi-tool workflows with less oversight—an essential requirement for teams operating AI systems at scale.

Anthropic on Azure provides Momentic with the reliability guarantees needed to process millions of tokens per hour on state-of-the-art models like Opus 4.5. Azure’s platform works seamlessly with Anthropic’s SDK, even supporting beta features such as reasoning effort out of the box.
—Jeff Ann, CTO & Co-Founder, Momentic AI

4. Agents, security, and high-stakes reasoning

Opus 4.6 is also best suited for agentic workflows, reliably orchestrating complex tasks across dozens of tools. It can proactively spin up sub-agents, parallelize work, and drive tasks forward with minimal oversight.

For security workflows, Opus 4.6 delivers deep reasoning, enabling teams to identify subtle patterns and complex attack vectors with high accuracy.

Anthropic is a trusted partner for governments and companies alike. Their speed, accuracy, and toolkit are already helping Everstar make fast, safe nuclear energy deployments a reality. I’m excited to see these capabilities integrated natively on Azure for secure deployments for our government and nuclear customers.
—Kevin Kong, Founder & CEO, Everstar

New API capabilities co-launching with Opus 4.6

Alongside Opus 4.6, Anthropic is introducing new API capabilities, available through Microsoft Foundry—that give developers greater control, scalability, and efficiency:

Adaptive thinking: Allows Claude to dynamically decide when and how much reasoning is required, optimizing performance and speed on simpler tasks, while allowing Claude to think harder on complex tasks.

Context Compaction (beta): Supports long-running conversations and agentic workflows by summarizing older context as token limits are reached.

1M Context Window (beta): Support for up to 1M tokens, with premium pricing applied beyond 200K tokens.

Max effort control: A new max effort level joins high, medium, and low, offering finer control over token allocation across thinking, tools, and output.

128K Output Tokens: Enables richer, more comprehensive outputs in a single response.

Claude Opus 4.6 in Microsoft Foundry

As AI systems move from assistance to autonomy, success depends on more than frontier intelligence, it requires intelligence that can be trusted to operate in real-world environments. Claude Opus 4.6 brings advanced reasoning and long-horizon execution, and Microsoft Foundry provides the system context where that intelligence can be applied responsibly, at scale.

Together, Claude in Foundry enables organizations to move beyond isolated experiments and toward intelligent, agent-driven systems that deliver real business impact—grounded in trust, governance, and operational rigor.

Opus 4.6 is also available through Microsoft Copilot Studio, enabling organizations to quickly build, orchestrate, and deploy advanced AI agents without custom code.

Explore Claude Opus 4.6 in Microsoft Foundry
Explore how Claude Opus 4.6 can power trusted, autonomous work in Microsoft Foundry.

Discover Opus 4.6

The post Claude Opus 4.6: Anthropic’s powerful model for coding, agents, and enterprise workflows is now available in Microsoft Foundry appeared first on Microsoft Azure Blog.
Quelle: Azure

Can high-temperature superconductors transform the power infrastructure of datacenters?

As the demand for AI and data-intensive computing is on the rise, the need for efficient and reliable power delivery is critical. Enter high-temperature superconductors (HTS), a game-changing technology that can improve energy efficiency by reducing transmission losses. Microsoft is investigating HTS technology to understand how our datacenters can meet the growing demand for power and how to improve our operational sustainability. Superconductors offer a ‘lossless’ advantage, making power transmission more efficient.

See here how Microsoft datacenters support cloud around the globe

Superconductors let electricity flow with no resistance. This means we can move power more efficiently and increase capacity more quickly. Microsoft is exploring how this technology could make electrical grids stronger and reduce the impact datacenters have on nearby communities. Because superconductors take up less space to move large amounts of power, they could help us build cleaner, more compact systems.

Using this technology could change how power moves through the cloud and support AI and other demanding workloads. To make this possible, we need to rethink traditional power designs and how datacenters move electricity today. By working with superconducting technology partners and system integrators, we aim to turn this advanced science into real solutions that help our customers and communities.

Judy Priest, corporate vice president and chief technical officer of Cloud Operations and Innovation at Microsoft, and Erhan Karaca, Chief Technology Officer at VEIR, during factory test of 3MW superconducting cable.

How superconductors boost datacenter performance and efficiency

Reduction of datacenter impact through HTS capabilities.

Copper and aluminum are good conductors and are used today in most cloud infrastructure wiring and power lines. But HTS cables can do even better because they carry electricity with zero resistance. They are also smaller and lighter, and they don’t produce heat or introduce voltage drops as electricity travels through them. At the center of the technology are scalable high-availability cooling systems, maintaining HTS cables at cryogenic temperatures required to support the operational excellence of Microsoft’s datacenters. In copper, electrical current encounters resistance at every step, losing efficiency, generating heat, and limiting how much current we can move. Superconducting materials behave differently: once cooled, they create a pathway for current to move with zero resistance, eliminating losses, heat buildup, and removing limitations on how far the power can travel.

Why does this matter for datacenters specifically?

HTS is not new and has been researched for decades across energy, transportation, and advanced science. Only recently the economics and manufacturing aspects of this technology made it viable at Microsoft’s cloud scale. Datacenters can benefit from HTS because they concentrate massive electrical loads in compact footprints. Traditional conductors force operators to choose between expanding substations, adding more feeders, reducing deployment densities or curtailing growth. Superconductors break this tradeoff: they increase electrical density without expanding the physical footprint, allowing modern facilities to support AI-era power requirements within the same or even smaller physical constraints.

Inside the datacenters, more power delivered directly to the racks supports high-density, high-performance workloads with improved efficiency. HTS cables are lighter than copper and can carry current over longer distances, enabling further optimization of power distribution across racks and pods and reducing potential bottlenecks. We shared our vision for these novel architectures at OCP 2025 Summit.

In practice, HTS has already demonstrated the potential to reduce the size of the power cables by an order of magnitude when delivering power directly to a server rack—opening new possibilities for how power is distributed within a datacenter.

Ruslan Nagimov, principal infrastructure engineer for Cloud Operations and Innovation at Microsoft, near world’s first HTS-powered rack-prototype (superconducting line seen above the rack).

Increasing capacity with next‑gen power infrastructure

HTS technology also supports Microsoft’s long-term cloud plans. As our AI systems grow, power is still the biggest limit we face. By updating out power systems with superconductors, we can build electrical infrastructure that grows more easily with the rising demand for cloud services. This could even allow us to design new kinds of datacenter facilities in the future.

We need modern power systems that allow electrical capacity to scale dynamically without requiring entirely new power infrastructure. Next-gen superconducting transmission lines deliver an order of magnitude higher capacity than conventional lines at the same voltage level. In turn, they can accelerate the expansion and interconnection of datacenter sites, speeding up compute deployment to meet the growing global demand for cloud services. Superconductors represent a foundational shift for datacenters and the electrical grid, but unlocking their full potential will require reexamining traditional power system assumptions and rethinking today’s approaches to power transmission and datacenter design.

Superconductors are a category defining technology poised to transform how power is moved across the electricity value chain, stretching from generation to datacenter chips. At VEIR, we build complete power delivery solutions that take advantage of these remarkable materials, enabling customers to overcome critical bottlenecks in energy infrastructure, unlock new datacenter capacity faster, and achieve higher power and compute density.
Tim Heidel, CEO at VEIR (a Microsoft Climate Innovation Fund portfolio company)

Reduced impact on the grid and local communities

HTS systems reduce energy loss and require significantly less physical space for power delivery. From a grid perspective, they minimize voltage drop along transmission lines and can be used to introduce fault-current limiting capabilities, with the potential to enhance overall grid stability for high-demand facilities such as datacenters, but also for nearby homes, schools, hospitals and businesses.

Superconducting cables require smaller trenches and reduce the need for intrusive overhead power lines [Source: AMSC, LIPA Superconductor Project].

More importantly, this technology reduces the physical and social footprint of the power infrastructure, reducing the impact on local communities. Furthermore, expanding the electricity supply typically requires a complex effort that includes increasing electrical generation capacity and improving transmission and substation systems. Unlike traditional power lines, which rely on wider corridors and heavier, more visible infrastructure (tall overhead lines and expansive substations), HTS supports smaller, quieter, and far less intrusive systems. HTS transmission lines can transfer the same amount of power as conventional systems at lower voltage, reducing the setbacks and required right-of-ways. This translates to a better use of space, which reduces construction impact, shortens build timelines, and lowers pressure on surrounding communities.

Superconductors enabled ComEd to interconnect electrical grid substations in Chicago without disrupting local businesses or communities. Our proprietary solution uniquely increases grid resilience.
Daniel McGahn, CEO at American Superconductor Corporation (AMSC)

We are striving to accelerate indoor and outdoor applications of advanced power technologies like superconductors for faster and effective deployments of real-world datacenter infrastructure systems. Alongside breakthroughs in networking and cooling technologies like hollow-core fiber and microfluidics, high-temperature superconductors complete a strategic triad of power, network, and thermal innovation in our datacenters. You may never see the power lines, but HTS technology could be working behind the scenes to keep power, capacity, and AI infrastructure efficient, resilient, and future-ready, so our customers focus on what matters most: building and running their cloud infrastructure workloads.

Explore the future of datacenters

HTS is just one of the new technologies shaping the future of datacenters. As the cloud continues to grow, many other innovations—from advanced cooling systems to cleaner power solutions—are helping us build faster, smarter, and more sustainable facilities. Learn more about some of the other projects driving the next generation of datacenter design.

Learn more about Microfluidics cooling: Cooling at the micro level for Microsoft’s datacenters.

Learn more about how Microsoft Azure scales Hollow Core Fiber (HCF) production through outsourced manufacturing.

Learn more about building community-first AI infrastructure.

Get started with Azure today with a free account.

The post Can high-temperature superconductors transform the power infrastructure of datacenters? appeared first on Microsoft Azure Blog.
Quelle: Azure

Agentic cloud operations: A new way to run the cloud

Cloud operations have reached an inflection point. For more than a decade, the industry has focused on scale—more infrastructure, more data, more services, more dashboards to build and manage both infrastructure and applications. While today’s cloud delivers extraordinary flexibility, the rapid growth of modern applications and AI workloads has introduced levels of scale and complexity that traditional operations were not designed for.

See how you can run agents with Azure Copilot

As modern applications and AI workloads expand in scale, speed, and interconnectedness, operational demands are evolving just as quickly. Organizations are now looking for an operating model that builds on their existing practices—one that brings intelligence into the flow of work and translates the constant stream of signals into coordinated action across the cloud lifecycle.

A new operating model for a dynamic cloud

Macro trends are pointing towards major shifts in operations. In the era of AI, workloads can move from experimentation to full production in weeks, making constant change the new norm. Infrastructure and applications are continuously updated, scaled, and reconfigured. Telemetry now streams from every layer—health, configuration, cost, performance, and security—while programmable infrastructure enables action at machine speed. At the same time, AI agents are emerging as practical operational partners—able to correlate signals, understand context, and take action within defined guardrails. Together, these shifts are driving the need for a new operating model—one where operations are dynamic, context-aware, and continuously optimized rather than reactive and manual.

Introducing agentic cloud operations

Agentic cloud operations brings this model to life by enabling teams to harness AI-powered agents that infuse contextual intelligence into everyday workflow. These agents help accelerate development, migration, and optimization by connecting operational signals directly to coordinated action across the lifecycle. They bring people, tools, and data together, so insights don’t stay passive—they become execution. The result is faster performance, reduced risk, and cloud operations that improve over time instead of falling behind as complexity grows.

Azure Copilot: The agentic interface

Azure Copilot brings agentic cloud operations to life as the agentic interface for Azure. Rather than adding yet another dashboard, it delivers a unified, immersive experience grounded in a customer’s real environment—subscriptions, resources, policies, and operational history. Teams can work through natural language, chat, console, or CLI, invoking agents directly within their workflows. A centralized management environment brings observability, configuration, resiliency, optimization, and security together—enabling operators to move seamlessly from insight to action in one place.

Full-lifecycle agents, working in context

At Ignite, we unveiled the agentic capabilities of Azure Copilot. These capabilities span key operational domains—migration, deployment, optimization, observability, resiliency, and troubleshooting—each designed to bring contextual intelligence into the flow of work. Azure Copilot correlates signals, understands operational context, and takes governed action where it matters. Rather than functioning as discrete bots, they operate as a coordinated, context-aware system that continuously strengthens cloud operations.

Plan and prepare

Azure Copilot and agents helps teams start with clarity and confidence. Copilot migration agent can assist with discovering existing environments, mapping application and infrastructure dependencies, and identifying modernization paths before workloads move. Deployment agent then guides well-architected design and generate infrastructure as code artifacts that set strong operational patterns from the outset. In parallel, resiliency agent identifies gaps across availability, recovery, backup, and continuity—so reliability is designed in, not pathed later.

Deploy and launch

When teams are ready to go live, Copilot deployment agent support governed, repeatable deployment workflows that validate both infrastructure and application rollout. Observability agent establishes baseline health from the moment production traffic hits, while troubleshooting agent accelerates early-life issue resolution by diagnosing root causes, recommending fixes, and initiating support actions if needed. Throughout this phase, resiliency agent verifies that recovery and failover configurations hold up under real world conditions.

Operate, optimize, and evolve

In ongoing operations, Azure Copilot’s agentic capabilities deliver compounding value. Observability agent provides continuous, full stack visibility and diagnosis across applications and infrastructure. Optimization agent identify and execute improvements across cost, performance, and sustainability—often comparing financial and carbon impact in real time. Resiliency agent moves from validation to proactive posture management, continuously strengthening protection against emerging risks such as ransomware. Troubleshooting agent helps make the shift from reactive firefighting to rapid, context aware incident resolution. Last but not least, migration agent reenters the lifecycle to identify new opportunities to refactor or evolve workloads—not as a onetime event, but as continuous modernization.

In ongoing operations, Azure Copilot’s agentic capabilities deliver compounding value. Observability agent provides continuous, full stack visibility and diagnosis across applications and infrastructure. Optimization agent identifies and executes improvements across cost, performance, and sustainability—often comparing financial and carbon impact in real time. Resiliency agent moves from validation to proactive posture management, continuously strengthening protection against emerging risks such as ransomware. Troubleshooting agent helps make the shift from reactive firefighting to rapid, context aware incident resolution. Last but not least, migration agent reenters the lifecycle to identify new opportunities to refactor or evolve workloads—not as a onetime event, but as continuous modernization.

A connected system, not disparate tools

These capabilities don’t operate as isolated bots. They work within connected, context-aware workflows—correlating real time signals, understanding operational context, and taking governed action where it matters most. This allows teams to anticipate issues earlier, resolve them faster, and continuously improve their cloud posture across development, migration, and operations. The outcome isn’t fewer tools—it’s better flow, where people, data, and automation operate as a unified system.

Governance and human oversight by design

Agentic cloud operations are built for mission-critical systems, where governance and control are nonnegotiable. Azure Copilot embeds governance at every layer, allowing enterprises to define boundaries, apply policies consistently, and maintain clear oversight. Features such as Bring Your Own Storage (BYOS) for conversation history give customers even greater control—keeping operational data within their own Azure environment to ensure sovereignty, compliance, and visibility on their terms. All of this is grounded in Microsoft’s Responsible AI principles, ensuring autonomy and safety advance together. Every agent-initiated action honors existing policy, security, and RBAC controls. Actions are always reviewable, traceable, and auditable, ensuring human oversight remains central to automated workflows—not removed from them.

Operating with confidence as the cloud evolves

As cloud environments grow more dynamic and complex, operational models must evolve to match them. With Azure Copilot and agentic cloud operations, Microsoft is enabling organizations to operate mission-critical environments with greater speed, clarity, and control—providing the confidence to move forward as the cloud continues to change.

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The post Agentic cloud operations: A new way to run the cloud appeared first on Microsoft Azure Blog.
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Beyond boundaries: The future of Azure Storage in 2026

2025 was a pivotal year in Azure Storage, and we’re heading into 2026 with a clear focus on helping customers turn AI into real impact. As outlined in last December’s Azure Storage innovations: Unlocking the future of data, Azure Storage is evolving as a unified intelligent platform that supports the full AI lifecycle at enterprise scale with the performance modern workloads demand.

What is Azure Storage?

Looking ahead to 2026, our investments span the full breadth of that lifecycle as AI becomes foundational across every industry. We are advancing storage performance for frontier model training, delivering purpose‑built solutions for large‑scale AI inferencing and emerging agentic applications, and empowering cloud‑native applications to operate at agentic scale. In parallel, we are simplifying adoption for mission‑critical workloads, lowering TCO, and deepening partnerships to co‑engineer AI‑optimized solutions with our customers.

We’re grateful to our customers and partners for their trust and collaboration, and excited to shape the next chapter of Azure Storage together in the year ahead.

Extending from training to inference

AI workloads extend from large, centralized model training to inference at scale, where models are applied continuously across products, workflows, and real-world decision making. LLM training continues to run on Azure, and we’re investing to stay ahead by expanding scale, improving throughput, and optimizing how model files, checkpoints, and training datasets flow through storage.

Innovations that helped OpenAI to operate at unprecedented scale are now available for all enterprises. Blob scaled accounts allow storage to scale across hundreds of scale units within a region, handling millions of objects required to enable enterprise data to be used as training and tuning datasets for applied AI. Our partnership with NVIDIA DGX on Azure shows that scale translates into real-world inference. DGX cloud was co-engineered to run on Azure, pairing accelerated compute with high-performance storage, Azure Managed Lustre (AMLFS), to support LLM research, automotive, and robotics applications. AMLFS provides the best price-performance for keeping GPU fleets continuously fed. We recently released Preview support for 25 PiB namespaces and up to 512 GBps of throughput, making AMLFS best in class managed Lustre deployment on Cloud.

As we look ahead, we’re deepening integration across popular first and third-party AI frameworks such as Microsoft Foundry, Ray, Anyscale, and LangChain, enabling seamless connections to Azure Storage out of box. Our native Azure Blob Storage integration within Foundry enables enterprise data consolidation into Foundry IQ, making blob storage the foundational layer for grounding enterprise knowledge, fine-tuning models, and serving low-latency context to inference, all under the tenant’s security and governance controls.

From training through full-scale inferencing, Azure Storage supports the entire agent lifecycle: from distributing large model files efficiently, storing and retrieving long-lived context, to serving data from RAG vector stores. By optimizing for each pattern end-to-end, Azure Storage has performant solutions for every stage of AI inference.

Evolving cloud native applications for agentic scale

As inference becomes the dominant AI workload, autonomous agents are reshaping how cloud native applications interact with data. Unlike human-driven systems with predictable query patterns, agents operate continuously, issuing an order of magnitude more queries than traditional users ever did. This surge in concurrency stresses databases and storage layers, pushing enterprises to rethink how they architect new cloud native applications.

Azure Storage is building with SaaS leaders like ServiceNow, Databricks, and Elastic to optimize for agentic scale leveraging our block storage portfolio. Looking forward, Elastic SAN becomes a core building block for these cloud native workloads, starting with transforming Microsoft’s own database solutions. It offers fully managed block storage pools for different workloads to share provisioned resources with guardrails for hosting multi-tenant data. We’re pushing the boundaries on max scale units to enable denser packing and capabilities for SaaS providers to manage agentic traffic patterns.

As cloud native workloads adopt Kubernetes to scale rapidly, we are simplifying the development of stateful applications through our Kubernetes native storage orchestrator, Azure Container Storage (ACStor) alongside CSI drivers. Our recent ACStor release signals two directional changes that will guide upcoming investments: adopting the Kubernetes operator model to perform more complex orchestration and open sourcing the code base to collaborate and innovate with the broader Kubernetes community.

Together, these investments establish a strong foundation for the next generation of cloud native applications where storage must scale seamlessly and deliver high efficiency to serve as the data platform for agentic scale systems.

Breaking price performance barriers for mission critical workloads

In addition to evolving AI workloads, enterprises continue to grow their mission critical workloads on Azure.

SAP and Microsoft are partnering together to expand core SAP performance while introducing AI-driven agents like Joule that enrich Microsoft 365 Copilot with enterprise context. Azure’s latest M-series advancements add substantial scale-up headroom for SAP HANA, pushing disk storage performance to ~780k IOPS and 16 GB/s throughput. For shared storage, Azure NetApp Files (ANF) and Azure Premium Files deliver the high throughput NFS/SMB foundations SAP landscapes rely on, while optimizing TCO with ANF Flexible Service Level and Azure Files Provisioned v2. Coming soon, we will introduce Elastic ZRS storage service level in ANF, bringing zone‑redundant high availability and consistent performance through synchronous replication across availability zones leveraging Azure’s ZRS architecture, without added operational complexity.

Similarly, Ultra Disks have become foundational to platforms like BlackRock’s Aladdin, which must react instantly to market shifts and sustain high-performance under heavy load. With average latency well under 500 microseconds, support for 400K IOPS, and 10 GB/s throughput, Ultra Disks enable faster risk calculation, more agile portfolio management, and resilient performance on BlackRock’s highest-volume trading days. When paired with Ebsv6 VMs, Ultra Disks can reach 800K IOPS and 14 GB/s for the most demanding mission critical workloads. And with flexible provisioning, customers can tune performance precisely to their needs while optimizing TCO.

These combined investments give enterprises a more resilient, scalable, and cost-efficient platform for their most critical workloads.

Designing for new realities of power and supply

The global AI surge is straining power grids and hardware supply chains. Rising energy costs, tight datacenter budgets, and industry-wide HDD/SSD shortages mean organizations can’t scale infrastructure simply by adding more hardware. Storage must become more efficient and intelligent by design.

We’re streamlining the entire stack to maximize hardware performance with minimal overhead. Combined with intelligent load balancing and cost-effective tiering, we are uniquely positioned to help customers scale storage sustainably even as power and hardware availability become strategic constraints. With continued innovations on Azure Boost Data Processing Units (DPUs), we expect step function gains in storage speed and feeds at even lower per unit energy consumption.

AI pipelines can span on-premises estates, neo cloud GPU clusters, and cloud, yet many of these environments are limited by power capacity or storage supply. When these limits become a bottleneck, we make it easy to shift workloads to Azure. We’re investing in integrations that make external datasets first class citizens in Azure, enabling seamless access to training, finetuning, and inference data wherever it lives. As cloud storage evolves into AI-ready datasets, Azure Storage is introducing curated, pipeline optimized experiences to simplify how customers feed data into downstream AI services.

Accelerating innovations through the storage partner ecosystem

We can’t do this alone. Azure Storage partners closely with strategic partners to push inference performance to the next level. In addition to the self-publishing capabilities available in Azure Marketplace, we go a step further by devoting resources with expertise to co-engineer solutions with partners to build highly optimized and deeply integrated services.

In 2026, you will see more co-engineered solutions like Commvault Cloud for Azure, Dell PowerScale, Azure Native Qumulo, Pure Storage Cloud, Rubrik Cloud Vault, and Veeam Data Cloud. We will focus on hybrid solutions with partners like VAST Data and Komprise to enable data movement that unlocks the power of Azure AI services and infrastructure—fueling impactful customer AI Agent and Application initiatives.

To an exciting new year with Azure Storage

As we move into 2026, our vision remains simple: help every customer unlock more value from their data with storage that is faster, smarter, and built for the future. Whether powering AI, scaling cloud native applications, or supporting mission critical workloads, Azure Storage is here to help you innovate with confidence in the year ahead.

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The post Beyond boundaries: The future of Azure Storage in 2026 appeared first on Microsoft Azure Blog.
Quelle: Azure