If you want to compete, you need to be multi-model. And if you want to take advantage of everything that different AI models have to offer, you need a great harness. But building one that works seamlessly across multiple models and surfaces is incredibly hard. You need to solve for performance, quality, and cost. And then you have to maintain it, which is even harder.

Luckily, the GitHub engineering team built a best-in-class, truly multi-model harness, which we’ve adopted company-wide. The Microsoft Copilot harness powers Copilot Code, Copilot Studio, the GitHub Copilot app, the CLI, VS Code, and more. And today, we’re making that harness available through Microsoft Foundry. That gives developers and enterprise customers a fully managed way to build agents that reason, use tools, and complete complex, long-running tasks. You get the right model for the job without switching the harness or the rest of your tech stack. That frees up your time, energy, and resources to focus on your bottom line.

What’s a harness, and why is it hard?

If the model supplies the intelligence, the harness is everything around the model. It’s the orchestration layer that gives an agent the right instructions, context, execution environment, and controls to accomplish meaningful work. And a harness worth its salt has support for open standards like MCP and agent skills, as well as agent plugins.

This is a fast-moving space. A frontier model today might fall behind tomorrow. And building your own harness or model router to keep up can be painful and slow. Now, you can build on the same harness Microsoft and GitHub ship in our own products, and you don’t have to maintain it.

We invest in science and engineering work to continually improve and evolve the harness and ensure high-quality, efficient, and low-cost performance across a range of models and workloads. The benefits compound for us and our customers who use the harness to build and run their own agents. Improve the harness, and every surface it touches benefits.

Born in GitHub, battle-tested at enterprise scale

The Copilot harness was built by the same GitHub engineers behind new technologies like our multi-model orchestration. And those technologies are only possible because we built our own harness. Designed to be fast, token-efficient, and predictable, it was our original, high-quality harness. And it had the highest satisfaction among our most demanding audience: developers. That’s why we’re standardizing all of our Copilot experiences on the strongest foundation possible.

The harness is built to perform across model families, with 20+ frontier models plus bring your own key, so you can use the right model for the right job. Instead of doing the work to keep up with and eval new models, lean on us to intelligently route work to the right model or models based on the task.

Our harness is flexible and configurable to any type of app, agent, or service. We recently ported the harness runtime to Rust, which has resulted in industry-leading performance and reductions in both session overhead and time-to-first token. And because the Copilot harness powers surfaces across Microsoft, it’s battle-tested and proven performant at the scale of millions of users and hundreds of real-world use cases.

A unified harness lets us ship faster, learn faster, and improve Copilot over time. And because the agents you build in Foundry can run on the same harness we’ve adopted across Microsoft, our continuous harness improvements will benefit your agents, too.

Try it yourself

The SDK is available to download, or you can run the harness in the cloud, all managed by Foundry. Our platforms remain open and interoperable, so you can continue to bring in any harness and framework you want, too.

With the Copilot harness, Microsoft runs the agent loop and sandbox, with governance and optimization built in from day one. You bring the instructions, skills, tools, and model choice.

This is an important step towards our vision of a stronger system around the model that preserves choice. It represents the best of GitHub, brought to the Microsoft platform and beyond.

We’ll keep pushing the harness forward, so you can focus on what only you can build.

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Last year at Ignite, we introduced the concept of Frontier Transformation and how it is different from any technology-led pursuit, because it requires us to focus on business outcomes first. It starts with a simple idea: technology was invented to serve humanity, not the other way around. There is no path where this notion of infinite cognitive compute replaces human purpose. Frontier Transformation is an opportunity for a wholesale reinvention of how you think about your unique ideas coming to life and how your organization can thrive in your industry.

Over the course of this past year, we have been leading our customers to focus on the three durable traits that are required for successful transformation: human ambition, intelligence and trust. We are taking a platform approach toward enabling the same: Copilot as the place to call home for interacting with AI to empower human ambition; the Microsoft IQ platform to compound and amplify your intelligence from within while protecting your unique differentiation; and Agent 365 to enable Trust and Observability at every layer of the stack so that you can govern, manage, secure and get business value and return out of the capabilities you deliver.

Our latest earnings are another important marker on that journey. Behind the numbers are thousands of customers, partners, developers and employees putting AI to work in meaningful ways. We have so many inspiring stories from our customers that highlight these outcomes, and below are some of our favorites.

Empowering human ambition with Copilot

Volkswagen has scaled Microsoft Copilot to more than 330,000 employees as it expands the role of AI in decision-making and knowledge-based processes across the enterprise. Building on Copilot Chat adoption that reached more than 300,000 monthly active users, employees are now using Copilot with Work IQ for more advanced scenarios, bringing greater organizational context to decision-making and knowledge-based work. Volkswagen is also working with Microsoft to further scale its Intelligent Data Foundation on Microsoft Fabric to make data easier to find, understand and use across the entire company. With this investment, Volkswagen is demonstrating how companies have evolved from using Copilot as a productivity tool to an operating system to empower human ambition.

Following a successful pilot, Fujitsu is scaling Microsoft Copilot across its global workforce of approximately 100,000. Copilot usage has surpassed 90%, and the company is using the tool to connect employees with specialized agents and business systems. AI agents are already automating roughly 75% of initial responses and triage for a service desk that receives nearly 300,000 inquiries annually, while new agents are supporting work across sales, systems engineering, procurement, finance and accounting. As customer zero, Fujitsu is using Copilot as the front door to a multi-agent work environment and using insights from its own AI adoption to better serve its customers.

KOHLER is using GitHub Copilot to transform software development, with one organization improving code releases by 25% and reducing error defects by 75%. These engineering breakthroughs are part of a broader effort to embed AI processes across the company, spanning IT, finance, marketing, supply chain and operations. Microsoft Copilot has reached 98% adoption, saving an average of 2 to 5 hours per month as employees are making informed decisions and getting answers faster in the flow of their work. Employees across the company have also created more than 3,000 agents to date, with hundreds active daily.

At Krafton, Microsoft Security Copilot and Microsoft Defender XDR are helping the security team respond to a growing volume and complexity of threats. The global gaming company automated key parts of security operations, from alerting and investigation to reporting, combining Microsoft Security solutions with its own systems and data to quickly gather context that helps distinguish real threats from false positives. This approach is having immediate results: security alert recognition time has fallen by approximately 95% to less than one minute, investigation work that previously took tens of minutes now takes about three minutes and roughly 95% of report creation has been automated. By taking on repetitive work, AI is giving Krafton’s security professionals more time to focus on responding to real threats and making critical security decisions.

To accelerate discovery across decades of scientific data, NASA is making complex information easier to explore through Microsoft Copilot. NASA employees are using Copilot to sift through massive amounts of academic literature and streamline daily tasks, and specialized assistants are helping researchers navigate complex Earth science data through natural language, extending access to information that once required significant technical expertise. Hydrology Copilot, for example, can analyze variables like precipitation and soil moisture to help communities better understand water conditions, prepare for droughts and anticipate flood risks, while automatically performing calculations and analyses.

To transform frontline work, Williams, a US natural gas company, is using Microsoft Copilot to help employees spend less time searching documents and completing manual processes, and more time on critical field operations. Copilot helps teams generate plans, interpret procedures, assess regulatory requirements, troubleshoot issues and complete safety reporting, while employees can create agents tailored to their own workflows. Demonstrating that the person closest to a problem is often the best one to solve it, one operations technician built an anomaly detector using Copilot that compares equipment data against baseline statistics and alerts teams to potential issues before they become costly or dangerous. Since deploying Copilot, AI adoption at Williams has grown from single digits to more than 90%, helping employees accelerate operations, improve reporting and compliance and identify equipment issues earlier.

Bristol Myers Squibb has rolled out Microsoft Copilot to 25,000 employees, reaching 96% adoption, and is now putting AI agents to work across everyday use cases such as document review and project management in support of the company’s mission to bring more medicines to more patients faster. One agent, called RABIT, analyzes meeting transcripts, surfaces action items and delivers insights directly within employees’ workflows.

Facing rising patient demand and financial pressures, Brown University Health is using Dragon Copilot to give clinicians more time to focus on patients. The health system adopted Dragon Copilot, and an early pilot across 420 clinicians found users spent less time on after-hours documentation and reported lower cognitive burden. Building on that experience, Brown University Health deployed Microsoft Copilot and Copilot Studio across departments, which led to the creation of more than 24 AI agents to support clinical workflows, hospital policies, patient access and operations. These AI solutions are not only supporting clinicians in the flow of their work but also helping to improve the patient experience and access to care.

Microsoft IQ compounds and protects your intelligence

BNY has made AI part of how its employees work through Eliza, its in-house AI platform built with Microsoft Azure, Microsoft Foundry, Microsoft Copilot and GitHub Copilot – with plans to add Work IQ. More than 300 solutions now run on Eliza, ranging from inquiry management to client onboarding, and 100% of employees have been trained on the platform. Across the bank, that scale is translating into meaningful results: client transaction inquiries are being resolved 80% faster and AI-assisted client onboarding is 20% faster on average. Through the Microsoft CEO Co-Innovation program, BNY also worked with Microsoft to develop an AI research capability with Foundry that investigates complex data issues, analyzes context across multiple sources, identifies likely root causes and recommends next steps, bringing AI-assisted reasoning into processes that have traditionally relied on human investigation alone.

Docusign is helping 1.9 million customers across 180 countries turn agreement data into actionable business insights. The company built an Intelligent Agreement Management platform on Microsoft Azure and used Microsoft Foundry to shift from general-purpose models to a system of task-specific models that better understand context. This change allows Docusign to process more than a million agreement documents daily with up to eightfold greater throughput and 90% lower AI processing costs. Foundry also supports a model lifecycle designed for continuous improvement, where processes are fine-tuned before deployment and monitored in production, while also providing the governance and traceability needed to meet enterprise requirements for compliance, visibility and control. Now agreement content is structured data that compounds from within, so organizations can use it to make decisions and automate workflows, knowing their IP is protected.

To help marketers create more effective content, Kantar turned decades of proprietary advertising intelligence into action with Microsoft Azure and Microsoft Foundry. Working with Microsoft Frontier Company, Kantar connected its LINK AI platform to its proprietary intelligence, AI models and creative workflows, enabling marketers to evaluate, optimize and generate content before it goes to market. The new approach can process and score large volumes of videos 20x faster while reducing platform operating costs by 4x, helping customers move from insight to action in minutes rather than hours. By bringing its unique intelligence into a continuous loop of observation and improvement, Kantar is creating new ways to help marketers turn insight into better creative and business outcomes.

Kinaxis, a Canadian supply chain software company, runs Maestro, its AI-driven supply chain orchestration platform, on Microsoft Azure. Maestro helps businesses make better decisions faster amid constant disruption. It brings together traditionally disparate business data like sales orders, inventory and production capacity with external signals like weather, market shifts and breaking news to give planners a near real-time view of their supply chains. With this unified data foundation, planners and analysts can use predictive AI and optimization models to anticipate demand shifts, evaluate constraints and recommend action, while generative AI agents act as assistants to ask questions, explore scenarios and automate parts of the planning process. With this intelligence in one platform, businesses can understand how disruptions ripple across their interconnected operations, weigh trade-offs and adjust plans as conditions change.

Moody’s is making more than a century of credit and risk intelligence directly accessible through Microsoft Copilot, giving customers trusted, protected context for complex decisions. Through a federated connector built using Model Context Protocol, customers can access live, decision-grade Moody’s intelligence through Researcher in Copilot and within Word, Excel and Teams, without copying or exporting the underlying data. A single question can synthesize information across credit ratings, company financials, ownership information and physical risk models, helping customers reach insights in minutes instead of spending hours or days on research and analysis. This brings trusted, connected intelligence directly into workflows where customers make complex decisions.

To turn conversations into actionable intelligence at global scale, Plaud, an AI hardware and software company, is using Microsoft Azure OpenAI in Foundry Models and Azure Speech in Foundry Tools to power transcription, summarization and AI inference across long-form audio and multilingual scenarios. Serving more than 2 million users worldwide, Plaud increased transcription accuracy and processing speed by 35% and improved inference performance by 30%, helping professionals turn spoken content into structured outputs and insights. Even as its user base doubled, the company used Foundry’s elastic scaling and flexible deployment models to reduce overall compute costs by 35%, while strengthening its ability to meet security, privacy and compliance requirements across global markets.

Rockwell Automation built a Maintenance Copilot using Microsoft Foundry to put decades of manufacturing expertise in the hands of employees. The AI assistant brings together expert knowledge from veteran engineers, manufacturing data and machine manuals, allowing technicians to quickly identify likely causes of equipment issues, see how colleagues have solved similar problems and access step-by-step troubleshooting guidance. At its Singapore factory, the approach has helped reduce machine downtime by 33% and cut servicing and spare-part costs by about 25%. Rockwell estimates it has also reduced the time it takes new workers to learn to troubleshoot hundreds of machines from nine months to three months.

With 1.5 billion passengers riding its elevators and escalators every day, TK Elevator knows that every minute of downtime affects how people move through airports, hospitals and offices. Building on a decade of connected-equipment data on Microsoft Azure, the company created AI agents with Microsoft Foundry that brief service technicians before they enter a building, telling them which elevator, which floor, what the issue is and which tools to bring. After each job, a voice agent records the technician’s account of the work in their own words, with no typing, and adds that expertise to a shared record. With 25,000 technicians caring for 1.4 million units worldwide, TK Elevator is turning each technician’s know-how into compounded intelligence every colleague can draw on.

Telstra partnered with Microsoft Frontier Company to deploy an AI-powered Migration Payment Agent that helps simplify one of the most complex aspects of customer migration: reconciling billing and payments across legacy and new platforms. The agent uses intelligence across these systems to proactively identify inconsistencies, recommend corrective actions and create enriched cases for human review, combining AI-driven insights with human-in-the-loop validation to maintain operational trust and governance. Already supporting more than 272,000 service migrations and delivering recommendations at 95%+ accuracy in production, the solution demonstrates how Telstra is applying agentic AI at enterprise scale to transform complex operations, improve customer outcomes and build reusable AI capabilities while compounding and protecting Telstra’s operational intelligence.

Agent 365 observes AI solutions across the stack

To advance its transformation into an AI-powered organization, Manulife is adopting Microsoft 365 E7: The Frontier Suite, expanding Microsoft Copilot to more than 30,000 employees and leveraging Microsoft Agent 365 to govern, manage and secure AI agents at enterprise scale. With Microsoft Azure and Microsoft Foundry supporting its global enterprise AI platform, Manulife is building a foundation for teams to develop and deploy AI and agentic solutions while maintaining standards for quality, security and cost efficiency. AI is already delivering impact across the business, from generative AI solutions supporting more than 110 million customer calls annually to developers increasing productivity by 30% with assisted and autonomous AI capabilities. With agentic solutions and AI-powered workflows already in production, Manulife is creating the foundation to scale AI responsibly across its global operations and accelerate its next phase of enterprise transformation.

Insight Enterprises, a leading solutions integrator and top Microsoft partner, is using Microsoft Agent 365 to govern AI agents at scale. As a Microsoft customer zero organization for Agent 365, Insight Enterprises is quick to adopt new capabilities internally and pass those learnings on to clients. With Microsoft engineering, it tested how Microsoft and third-party agents could be observed, managed, governed and secured through a unified control plane. To date, Insight Enterprises has discovered 9,734 agents with visibility into which are active, what data they can access and how to monitor their activity over time. The experience has helped Insight Enterprises build practical AI governance expertise it can bring to customers, including organizations in highly regulated industries.

KPMG is adopting Microsoft Agent 365 to manage and secure AI agents across its global organization, providing centralized observability and control as it scales agentic AI. Agent 365 will also strengthen KPMG Workbench, its multi-agent platform built on Microsoft Foundry that coordinates agents across client service delivery platforms. At the same time, KPMG is expanding Microsoft Copilot to more than 276,000 professionals worldwide, helping embed AI more deeply across audit, tax and advisory services. Together, these capabilities are giving KPMG the confidence and security to move agents from experimentation into enterprise-wide deployment, while helping clients do the same.

Operating in one of the most highly regulated industries, where protecting sensitive customer information is essential, Truist Financial Corporation is using Agent 365 to manage, govern and observe more than 13,000 AI declarative agents across its organization. Truist manages its agent ecosystem with discipline: every agent has an accountable owner and agents are automatically disabled after 45 days of inactivity. That rigor is driving strong retention and adoption, with more than 8,000 agents active each month. Building on this trusted foundation, Truist has advanced its broader AI strategy through their investment of more than 50,000 Microsoft 365 E7: The Frontier Suite seats.

—

Every quarter gives us an opportunity to step back and look at the progress we are making with our customers and partners around the world. What continues to strike me is not simply the pace of change, but the ambition we see from organizations using technology to achieve aspirations that simply weren’t possible before. We look forward to working alongside you on the path to becoming Frontier, and we hope to see many of you at Microsoft Ignite this year in November to share the latest innovations that will enable you to reach that destination.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

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Amy Coleman, Executive Vice President and Chief People Officer, shared the below communication with Microsoft employees today.

Today, we’re introducing a new Voluntary Exit Offer (VEO) to eligible employees within participating organizations. This offer gives eligible employees who may be considering a future outside the company a new choice to make that transition now with support. It also gives participating organizations another way to adapt their workforce to changing needs.

The Voluntary Exit Offer reflects an important part of how we’re evolving our approach to change: creating greater flexibility and, where possible, more ways for employees to shape their next chapter. This builds on what we learned earlier this year through the Voluntary Retirement Program.

What employees should know

This offer will be available to eligible employees in participating teams in Canada, India, the United Kingdom and the United States at level 70 and below. This is a time-limited, voluntary program that gives those who are eligible the opportunity to leave Microsoft with financial support and career planning resources. Some organizations are providing the Voluntary Exit Offer, and some are not. Participation varies based on each business’s priorities, customer commitments and workforce needs.

Eligible employees will receive an email with a link to their Voluntary Exit Offer details within the next few hours.

Looking ahead 

The Voluntary Exit Offer is new for us. We’ll learn from it. At the same time, we’ll keep helping people focus on the work that matters most, build new skills and, where it makes sense, find new opportunities across Microsoft. There is no one-size-fits-all approach for every team or every employee. These are a few of the ways we’re trying to meet different business needs while continuing to create opportunities for our own people to grow and contribute.

My promise is to continue to provide clarity, listen and learn and keep improving.

Thank you for everything you continue to do for our customers, Microsoft and for one another.

Amy

This Viva Engage post has been edited slightly for external use.

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In the last few months, concerns have arisen about AI safety as a growing number of incidents were publicly disclosed. These incidents exposed how powerful models have become and how they represent a significant threat if not handled well. And with the global race on innovation, the challenge will compound as models become even more powerful.

While most have been worried about threat actors using powerful models to attack, the focus has been less on agents within our own walls. Since we were the ones who deployed agents internally for our companies, we implicitly trusted our agents.

Unfortunately, frontier models, regardless of their origin, cannot be trusted. This is not because models are inherently malicious. Models have been trained to attain results, which often leads to reward hacking, and models are not optimized around organizational norms or judgment. Agents are unpredictable. They will adapt as the context changes, may not behave as intended and will collaborate and collude with other agents to reach their objectives, without needing to be compromised.

Agents have been unleashed in the enterprise, at scale, and their numbers are growing rapidly. These agents are frequently overprivileged, have access to data, tools and systems and can take actions and make decisions in critical processes. They have become active participants in the enterprise.

They are agentic insiders.

Monitoring and securing those agents must become continuous. We need to understand in real time what the agents are doing and what risks those actions create, and we need to adjust their ability to act accordingly.

To manage and control these insiders, we will need Guardian Agents — an established industry term — that watch and moderate every action of every agent, before it happens, 24/7.

An agent brings together a model, a harness that orchestrates its work, an action space of tools and resources and a runtime in which it executes. Securing agents requires protection across multiple layers: identity, permissions, data, endpoints, network, cloud and runtime.

In July, we announced Project Perception, an agentic security system that uses our new cyber stack and teams of red, blue and green agents to help defenders act with speed, while defending against AI driven attacks from outside of the enterprise.

The same cyber stack is also directly applicable to managing and governing the agentic insiders that run in the enterprise.

Project Perception can be leveraged to contain agentic insiders, implementing deterministic controls on non-deterministic intelligence:

• Sensors/Signals/Actuators: bring together signals across the various sensors (identity, data, cloud, endpoint, network, runtime) to discover agents in the enterprise and bring them under management by assigning an identity, container and set of policy and permissions.
• Context: use those signals to compute, dynamically, the risk associated with what the agents are attempting to do.
• Harness/agents/models: based on dynamic agent risk, a team of Guardian Agents, with green and blue agents, actively and continually supervise every action of every agent and surgically respond as the risk profile increases, in real time.

This is where agentic security changes the equation for security practitioners.

Project Perception can supervise in real time while providing traceability for practitioners to examine what happened, understand why the risk emerged and apply the judgment needed to update their security system accordingly. Agentic security provides speed. Practitioners provide judgment. That is how defenders begin operating at the same speed as the systems they are protecting against.

Today, security systems can reliably observe the actions of the agents. Those signals are essential and tell us what we need to contain. Our security controls should increasingly be able to ask not only “What is happening?” but also “Why is it happening?” Is this consistent with the intent of the task?” This will require greater transparency into the model itself, not only the input and output tokens, but the thinking and chain of thought tokens. Only then can we truly understand whether the model’s intent is aligned with the purpose of the agent. Model transparency will not replace containment. It will make containment smarter and stronger. And it will allow us to better inform accountability and traceability. This needs to be the next step in securing agentic insiders.

With Project Perception, you can get started today on the journey to secure your agentic insiders. There should not be a choice between innovation and safety.

Hayete Gallot leads Microsoft’s work to help organizations operate securely in an AI-driven world. Her scope includes identity, threat protection, compliance and data security at global scale.

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Across my conversations with leaders in government, critical infrastructure and regulated industries, one priority consistently rises to the top: putting technology to work on their most important systems while maintaining the control those systems require.

This challenge is becoming more important as advanced cloud and AI capabilities move into public services, regulated processes and essential operations. Organizations are considering more than where data resides. They also seek to understand who can access and operate their systems, how sensitive digital assets remain protected, which technology dependencies matter most and whether critical services can continue as regulatory, geopolitical, security or network conditions change. Digital sovereignty should give organizations greater control and security over the technology they depend on, while continuing to innovate.

Last year, we introduced Microsoft Sovereign Cloud to give customers more choice in how they address these requirements. Since then, we have continued to turn our commitments into practical capabilities across public cloud and customer-operated environments, while expanding how we enable partners to address sovereignty requirements in their local markets. Today, we are sharing the next stage of that work.

A continuum of control, built around the workload

Our approach to digital sovereignty starts with the workload. Each workload carries different requirements for data, operations, technology dependencies, continuity and risk. Microsoft Sovereign Cloud is designed to make it easier for organizations to apply the appropriate level of control to each workload without forcing every application into the same deployment model.

Those needs can vary widely across organizations. A public-facing digital service may benefit from the scale and pace of innovation in the public cloud. A system supporting essential operations may need to continue running locally if connectivity is disrupted. An AI workload handling highly sensitive data may require models, data and processing to remain within a customer-controlled environment.

Microsoft Sovereign Cloud is designed around this workload-based approach, providing a continuum of management, control and options across Sovereign Public Cloud, Sovereign Private Cloud and the Sovereign Partner Ecosystem. Organizations can apply the level of control appropriate to each workload while maintaining a consistent approach to governance and operations across their digital estate. Sovereign Public Cloud adds capabilities for workloads that require greater control and transparency while continuing to benefit from the scale and innovation of Azure.

Sovereign Private Cloud extends cloud capabilities into customer-operated environments, including connected and disconnected operations. The Sovereign Partner Ecosystem broadens the continuum through independently operated environments, specialized partners, locally relevant expertise and technologies that support customers’ sovereignty requirements.

These are not isolated destinations. Customers can combine public cloud, private cloud and partner capabilities across their digital estate, applying different levels of control to different workloads as their operating needs evolve.

Making control more visible in Sovereign Public Cloud

For many workloads, the public cloud provides a combination of scale, innovation, integrated security and compliance. Organizations can benefit from these capabilities while applying additional sovereignty controls for residency, confidentiality, operator access, policy and continuity. Our work in Sovereign Public Cloud makes these controls easier to understand, apply and demonstrate.

Sovereign Control Panel, entering public preview at Microsoft Ignite, brings these signals together in a unified Azure portal experience. It helps organizations understand their sovereignty posture, apply and manage controls, identify gaps and risks and take action to address them. It also supports business continuity readiness by helping customers understand workload dependencies and assess portability across operating environments when required.

This visibility complements controls across the operator-access lifecycle. Just-in-time Access and Customer Lockbox give customers additional control over access to their environments. For customers in the European Union (EU) and the European Free Trade Association (EFTA), we are rolling out Data Guardian, which adds European-resident oversight by a trusted EU partner for qualifying Microsoft engineer remote access to in-scope production systems. Production Touch Ledger, also launched with phased rollout in the EU and EFTA, creates tamper-evident records of that access and the actions performed.

Azure Key Vault Managed Hardware Security Module (HSM) already gives customers dedicated, single-tenant protection and control over the encryption keys used by supported Azure services. Last year, we committed to extending that control beyond Microsoft infrastructure. External key management with Azure Managed HSM is now generally available, allowing customers to keep the root encryption key in hardware they own and operate on-premises or through a trusted third party. This gives organizations another option for workloads that require physical control of key hardware outside Microsoft infrastructure.

Azure confidential computing helps protect supported data while it is processed in a hardware-based and attested trusted execution environment. We are also increasing transparency into the technologies that underpin that trust. Azure Confidential Clean Rooms, now in limited preview, applies confidential computing to approved multiparty collaboration, allowing participants to analyze shared sensitive data while limiting broader exposure of the underlying information.

Together, these capabilities provide additional technical and operational controls that can support a broader digital sovereignty strategy aligned to an organization’s legal, policy, security, resilience and business requirements.

Designing for resilience and operational independence

For some workloads, digital sovereignty also means maintaining critical operations as connectivity, geopolitical and operating conditions change. Resilience must be designed from the start so essential workloads can continue operating through disruption.

To help customers prepare for these scenarios, we are publishing new sovereign business continuity guidance and architecture patterns. These resources help organizations translate resilience requirements into architectures they can plan, implement and test before they are needed.

For many workloads, the scale and geographic reach of the public cloud provide a strong foundation for resilience across locations and operating conditions. For workloads that require greater operational independence, Sovereign Private Cloud extends that continuum into customer-operated environments. It supports connected and disconnected operating models. The ability to transition workloads from connected to disconnected operations is entering public preview, giving organizations greater flexibility to sustain critical services as connectivity and operating requirements change.

Bringing more of the application lifecycle under local control

Operational independence requires more than infrastructure. Increasingly, organizations need data, development tools, model capabilities and productivity services their applications depend on to operate within the same customer-controlled environment.

Sovereign Private Cloud, built on Azure Local, extends Microsoft cloud capabilities into environments that customers operate and control. SQL Server on Azure Local is already generally available for connected and disconnected deployments. DocumentDB and Blob Storage on Azure Local are both entering public preview, giving organizations more choice in how they run and manage critical data within customer-controlled environments.

GitHub Enterprise Local will become generally available at Microsoft Ignite as a validated GitHub Enterprise Server appliance on Azure Local, helping organizations keep repositories, code and development artifacts under local control. Developers can also use GitHub Copilot with locally hosted models for inference, bringing AI-assisted development closer to their code and data within the environments they control.

The local application environment is expanding with Foundry Local. At Ignite, Foundry Local will become generally available for connected and disconnected operations on Azure Local, with an expanded model catalog that includes Microsoft, partner, open-weight and open-source models. Local inference and agentic retrieval will also enable users to interact with local enterprise data using natural language and receive responses grounded in that data. Integration with Microsoft 365 Local, available in preview for approved customers, extends this functionality to locally operated Microsoft SharePoint and Exchange servers.

Confidential virtual machines on Azure Local are generally available, extending confidential computing capabilities to customer-controlled infrastructure. Hardware-encrypted memory and hardware-based isolation help protect sensitive data while it is being processed. Workload attestation adds assurance before sensitive data or keys are released. For regulated, sensitive and mission-critical workloads, these safeguards provide another layer of protection while giving organizations greater control over where their data and applications run.

Azure Local will support multitenancy, allowing organizations to separate resources, governance and management across multiple tenants while sharing the underlying infrastructure. This significantly expands what customers with distinct business units or security boundaries can achieve with Sovereign Private Cloud. Multitenancy also creates new opportunities for local operators and service providers to deliver differentiated sovereign cloud services on Azure Local infrastructure.

These additions bring more of the application lifecycle under local control, spanning data, development, productivity, AI and operations. Organizations can determine what runs locally based on the requirements of each workload.

Extending the continuum through partners

Navigating sovereignty is increasingly complex. Regulatory, security, operational and business requirements vary across countries, industries and workloads. Organizations must translate these needs into technology and operating models that work in their local context. Meeting these needs often requires a combination of technology, local operating authority, sector expertise and specialized solutions.

The Sovereign Partner Ecosystem broadens the ways customers can address these requirements. National Partner Clouds, including Bleu in France, provide independently operated environments for customers that require a different ownership and operating model. Software partners can enhance Azure Local environments with capabilities across security, identity, observability, backup and recovery, migration, data, networking and applications. Digital sovereignty specialized partners combine Microsoft technologies with local knowledge and implementation expertise. Platform co-innovation partners expand infrastructure choice from datacenter to the edge.

This ecosystem gives customers the flexibility to combine technology, operating models and trusted expertise based on the requirements of each workload.

The next chapter of digital sovereignty

AI is amplifying both the opportunity and the impact of technology choices. As AI systems and agents interact with sensitive data, business processes and essential infrastructure, they also expand the security considerations organizations need to manage. Organizations need clear control over where these systems run, who can operate them, what actions they can take and how critical services continue when conditions change.

Microsoft Sovereign Cloud brings these choices together as a continuum of control across the public cloud, customer-operated environments and our partner ecosystem. It gives organizations the flexibility to apply the appropriate level of control to each workload while continuing to benefit from cloud capabilities and ongoing innovation.

We will continue to expand this continuum so organizations can innovate with cloud and AI while maintaining the control, confidentiality, resilience and operational authority their most important workloads require.

Douglas Phillips leads global engineering efforts for Microsoft’s specialized, sovereign and private clouds. He is responsible for Microsoft’s global strategy, products and operations that bring Microsoft’s industry-leading solutions, including Azure, our adaptive cloud portfolio and Microsoft 365 collaboration suite, to customers with additional sovereignty, security, edge and compliance requirements.

Learn more

• Microsoft Sovereign Cloud
• Sovereign Landing Zone architecture guidance
• Sovereign Public Cloud Tech Community blog
• Sovereign Private Cloud Tech Community blog

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Is Your IT Provider a Supplier—or a Genuine Business Partner?

For many UK small and medium-sized businesses, the relationship with an IT provider follows a familiar pattern: something breaks, a ticket is raised, the problem is fixed and an invoice follows. That may be competent support, but it is still a supplier relationship. A strategic technology partner works differently. Instead of waiting for faults, they learn how the business operates, understand where it is heading and help leadership make better technology decisions before problems become expensive.

The distinction matters because technology now influences almost every business objective: productivity, customer experience, cash flow, cyber resilience, compliance, recruitment and growth. If your IT provider only measures success by closed tickets, they may be maintaining the present while doing little to prepare your business for the future.

The Reactive IT Support Model

Reactive IT support is built around incidents. Its core question is: “What has gone wrong today?” The provider may respond quickly and resolve faults efficiently, but the engagement is driven by short-term demand. Advice tends to arrive when equipment reaches end of life, licences need renewing or a serious security issue forces action.

• Most contact begins with a fault or user request.
• Recommendations are often product-led rather than outcome-led.
• Planning is limited to renewals, replacements and immediate risks.
• Reporting focuses on ticket numbers, response times and closure rates.
• Technology decisions are separated from wider business planning.

This model can keep systems running, but it can also create hidden costs: duplicated software, inconsistent processes, avoidable downtime, poorly timed capital expenditure and security weaknesses that remain unaddressed until they become urgent.

What a Strategic Technology Partner Does Differently

A strategic technology partner starts with a different question: “What is the business trying to achieve?” At Fruition Systems Ltd, that means viewing IT as part of the management agenda rather than a separate technical function. Support still matters—problems must be resolved—but day-to-day service sits within a broader plan for improvement, resilience and growth.

1. Aligning IT with Business Objectives

Technology should support the organisation’s priorities. If the objective is to open a new site, improve hybrid working, increase recurring revenue or strengthen compliance, the IT plan should show how systems, data, security and user adoption will contribute.

A partner translates business goals into a practical technology roadmap. That might include simplifying applications, improving management information, automating repetitive work, reducing single points of failure or ensuring that infrastructure can scale. The discussion is not “Which server should you buy?” but “What capability does the business need, when will it need it and what is the most sensible route?”

2. Quarterly Business Reviews That Look Forward

A quarterly business review should be more than a service report. It is a structured conversation between your leadership team and technology partner about performance, risks, priorities and upcoming change. It creates a regular decision-making rhythm instead of relying on occasional conversations when something has already gone wrong.

• Progress against the agreed technology roadmap.
• Changes in business objectives, staffing, locations or customer needs.
• Cybersecurity, continuity and compliance risks.
• Budget forecasts, lifecycle planning and upcoming decisions.
• Opportunities to improve productivity, visibility or service delivery.
• Clear actions, owners and target dates for the next quarter.

3. Strategic Planning Instead of Repeated Firefighting

Fixing problems is necessary; preventing them is more valuable. Strategic planning considers the whole technology environment over a realistic time horizon. It identifies dependencies, sequences investment and gives managers visibility over likely costs. This turns IT spending from a series of surprises into a controlled programme.

For example, replacing ageing devices may be coordinated with a cloud migration, security improvements and staff training rather than handled as four unrelated purchases. The result is less disruption, better adoption and a clearer return on investment.

4. Measuring Value Rather Than Ticket Volumes

Ticket data is useful for managing support, but it is not a complete measure of business value. A high number of closed tickets may indicate a busy helpdesk—or a persistent underlying problem. A strategic partner looks beyond activity to outcomes that matter to the organisation.

• Reduced avoidable downtime and fewer recurring issues.
• Improved employee experience and faster onboarding.
• Lower operational or security risk.
• Better use of existing licences and technology investments.
• More predictable IT costs and fewer emergency purchases.
• Progress towards agreed business capabilities and milestones.

Supplier vs Partner: The Questions to Ask

When reviewing your current IT relationship, ask whether your provider can answer these questions clearly:

• What are our top three business priorities, and how does the IT roadmap support them?
• Which technology risks could disrupt our plans over the next 12 to 24 months?
• What improvements have you proactively recommended this quarter?
• How are you helping us plan and control future technology costs?
• Which outcomes demonstrate that we are receiving value?
• When did we last review our roadmap with senior decision-makers?

If the answers centre on response times, ticket closures and products purchased, you probably have a supplier. If they connect technology decisions to growth, resilience, productivity and commercial priorities, you are closer to a genuine partnership.

Why the Difference Matters for UK SMEs

SMEs rarely have unlimited budgets or large internal technology teams. Every investment has to work harder, and every avoidable disruption has a disproportionate impact. A strategic partner helps leadership make informed choices, prioritise effort and avoid paying for technology that does not serve a defined purpose.

The strongest relationships combine dependable support with commercial understanding. They give business owners and senior managers a clearer view of risk, cost and opportunity—without requiring them to become technology experts.

Move from IT Support to Technology Partnership

Reliable support should be the baseline, not the whole relationship. Fruition Systems Ltd works with UK SMEs to connect technology with business direction, build practical roadmaps and review progress regularly. The aim is not simply to fix more problems; it is to help the business experience fewer avoidable problems and gain more value from every technology decision.

Ready to find out whether your IT provider is acting as a supplier or a partner? Speak to Fruition Systems Ltd about a strategic review of your current technology, risks and priorities.

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Azure Maia 200 now in production; Azure Maia 300 coming in 2027

As we move into the era of Super Intelligence, Microsoft is entering a new phase of infrastructure. We will scale our Azure Maia family to multi-gigawatt deployments starting in 2027, making Maia the primary inference system for Microsoft products and services.

The multigenerational Azure Maia program has become a foundational element of Microsoft’s AI infrastructure strategy. Maia 200 is now serving Microsoft production workloads, including Microsoft Copilot, and supporting the MAI team as they develop next-generation models, with GitHub Copilot and Microsoft Foundry to follow by end of year. Across these workloads, Maia 200 runs OpenAI models, Microsoft’s own MAI models and leading open-source model families. Insights from the scale of Microsoft’s AI services allow for unique co-design between hardware systems and software applications to optimize real-world use cases across enterprise applications and industry scenarios.

Maia 300 is a complete intelligent system designed for the new types of AI workloads emerging today: Long-context reasoning, latency-sensitive conversational experiences and sustained, multi-step agentic tasks. By improving efficiency across the system, we expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems. And in a full 96-accelerator configuration, it delivers up to 7x the rack-scale token throughput of Maia 200.

Maia 200: Driving the workloads that Microsoft runs at scale

The Azure Maia system is designed around the real-world workloads Microsoft runs at scale and the models we expect to build in the years ahead. Because silicon development takes years, our software and hardware teams collaborate early to anticipate architectures, scale and capabilities, translating those requirements into greater memory capacity, higher bandwidth and more accelerators operating as a single system.

That co-engineering is already delivering measurable efficiency gains. Maia 200 is deploying across the United States, Europe and Australia and delivering up to 1.7x more tokens per watt for built-in AI capabilities in Microsoft Teams, Copilot and Cowork experiences compared with leading GPU infrastructure.

Experience running our AI models and services on Maia 200 directly informed the design of Maia 300. Together, the two generations are designed to support a broad set of additional Microsoft’s AI services: multi-step agentic work that will help GitHub deliver coding experiences more efficiently; cloud-scale model serving that will help Microsoft Foundry support diverse architectures and deployment environments; inference and data generation for the MAI team; and latency-sensitive interactions that will help Dragon Copilot scale workflows and reduce clinicians’ administrative burden.

Maia 300: Scaling more useful AI within every megawatt

AI is becoming a foundational layer of every application, workflow and business process. As enterprises move beyond chat experiences to systems of agents that can reason, act and collaborate, AI infrastructure must deliver intelligence securely and efficiently at scale while continuously adapting as models and workloads evolve.

As the third generation of our Azure Maia program, Maia 300 is designed with an inference-first architecture optimized for long-context, memory-intensive and decode-heavy AI workloads. This involves balancing compute, memory bandwidth, networking and power efficiency at cloud scale.

Key capabilities of the Maia 300 system include:

Less data movement, more useful compute: Moving data efficiently, rather than raw compute power, is increasingly the bottleneck in large inference systems. Maia 300’s software-defined dataflow architecture and AI-assisted kernel optimization work together to maximize locality, reduce data movement and improve utilization across compute, memory and networking resources. By treating dataflow as a first-class design principle, Maia 300 delivers a significant performance-per-watt advantage over traditional GPU architectures.

Higher memory bandwidth within every watt: As AI workloads become increasingly context and memory-bound, Maia 300 delivers industry-leading HBM bandwidth per watt. Eight HBM4 stacks provide 18.0 TB/s of peak memory bandwidth within a 950-watt TDP, enabling efficient execution of large models and long-context workloads. Specialized kernel support uses on-die SRAM as a software-managed scratchpad, reducing pressure on HBM.

Chiplet architecture for enhanced efficiency: A new multi-chiplet architecture separates compute, memory-controller and I/O functions, improving efficiency, scalability and cost structure. Compute chiplets are built on TSMC’s N2P process node to maximize performance-per-watt.

Larger context, richer intelligence: By designing for advanced low-precision formats, including enhanced MXFP4, Maia 300 reduces memory and bandwidth requirements for both computation and KV-cache storage. The result is larger context windows, higher throughput and improved model efficiency within the same power envelope.

Scale-up networking at datacenter scale: Maia 300 treats networking as a seamless extension of the software-defined dataflow architecture. Rather than moving data through separate communication layers, dataflow operations span compute, memory and network resources as a single system. High-bandwidth interconnects between Maia accelerators are optimized for tensor parallelism, while a custom AI transport delivers performant and reliable scale-up communication across accelerators and racks. The AI-optimized transport leverages Ethernet-based networking infrastructure to enable a unified topology that scales efficiently from a single rack to datacenter-scale deployments.

Higher AI throughput from every rack: Maia 300 rack systems bring together 96 accelerators within a 167-kilowatt power envelope, delivering 27.6 TB of HBM capacity and 1.7 PB/s of aggregate HBM bandwidth across a high-speed scale-up domain. That density allows Microsoft to serve more simultaneous AI interactions and larger workloads from each rack, improving datacenter capacity and the economics of expanding AI services.

Maia 300 will begin production deployments in 2027. For more information on Maia 300 architectural details, read our technical paper.

Azure Maia is at the heart of our Frontier Transformation

The future will not be defined by a single model or a single piece of silicon or a single innovation. Model and silicon choice are inherent in our vision for Frontier Transformation. Our focus is on developing responsible, affordable and scalable platforms that will help everyone amplify their intelligence to drive breakthrough outcomes that matter for them.

Microsoft is unique because we are able to optimize from silicon to systems to agent on an open, heterogenous and model diverse platform our customers can count on for their AI transformation, today and in the years ahead. This platform is at the heart of Frontier Transformation, and Azure Maia is a foundational component of our approach to expand access, accelerate innovation and help customers capture more value from AI.

Next month at Microsoft Ignite 2026 in San Francisco and online we’re looking forward to sharing more information on our strategy, technology adoption trends and success in Frontier Transformation.

For more details, photos and videos, visit our Maia 300 site.

Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.

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Satya Nadella, Chairman and CEO, and Ryan Roslansky, Executive Vice President of LinkedIn and Microsoft 365, shared the below communications with Microsoft and LinkedIn employees this morning.

SATYA NADELLA MESSAGE

After nearly 18 years at LinkedIn and Microsoft, Ryan Roslansky has decided to leave Microsoft and embark on his next chapter.

As Ryan and I have been discussing our roadmap and work ahead, we felt this was the right moment for this change. Ryan will stay through the end of the calendar year as an advisor to me and will support on the transition.

Ryan leaves the organization in a strong position. With Copilot, we are executing rapidly against our vision to build a new OS for work that spans every model, every form factor, and every task, with Office fully embedded in Copilot and vice versa! Over the past year, Ryan’s team has done critical work bringing together the product, engineering, and design foundations that have made this next phase possible. Last week’s launch was an important milestone in that journey, and the strong response from customers, partners, and the broader ecosystem reinforced both how far we’ve come and how much opportunity remains ahead.

At LinkedIn, we remain focused on our core vision of creating economic opportunity for every member of the global workforce, helping our 1.3 billion members access the knowledge, insights, and opportunities they need to navigate rapidly changing professional and labor markets.

Effective today we will make the following changes:

• Sumit Chauhan and Lan Ye will report to Charles Lamanna.

• Jon Friedman will report to Jacob Andreou.

• Dan Shapero will continue as CEO of LinkedIn and Mohak Shroff will continue in his role, both reporting to me.

Over his nearly two decades at Microsoft, Ryan has helped shape how people around the world find opportunity, build their careers, and get their work done. I’ve always appreciated his clarity of thinking and I’m deeply grateful for everything Ryan has built, for the leaders and teams he has developed, and for his friendship and counsel.

Please join me in thanking Ryan for his extraordinary contributions to LinkedIn and Microsoft, and in wishing him the very best in what comes next.

Satya

RYAN ROSLANSKY MESSAGE

Team,

In 1996, as a college freshman, I taught myself to code so a few friends and I could help other students find a place to live around campus. I didn’t know much. I did know the internet was about to change how people found things, and I wanted to be as close to that as possible.

In 2009, Jeff Weiner made me his first hire at LinkedIn. I’ve spent the nearly 18 years since working on a much bigger version of that same idea: helping people find opportunity, and then helping them do great work once they have it.

This has (obviously) been the best professional experience of my life.

As a product manager, I got to build dozens of features that are still around today (some good, some not so good!). When I became CEO in 2020, the world was months into a pandemic that changed what work even meant. Nobody hands you a playbook for that. This team wrote one anyway. Since then, LinkedIn has passed a billion members and nearly tripled its business, and every bit of that belongs to the people who built it.

Last year, Satya asked me to help build the future of M365. Once again, the old rules didn’t apply. AI was rapidly changing how professionals work, and the world’s largest productivity suite needed to lead the way. That meant figuring out what Copilot, and the future of work, should look like as one ecosystem.

We still have a ways to go, but the progress over the past year has been inspiring to watch. A lot of talented people set aside their own roadmaps to make that happen. Thank you. I know it wasn’t easy.

Satya, thank you. When LinkedIn joined Microsoft, you gave us room to build, and then trusted many of us with a lot more. I’ve learned more from you than I can fit in an email, and I’m grateful for the friendship that came with it.

All of which made this a hard decision. I know what it would take for me to lead the next phase of this work well: being in Redmond full time, every day. But my life and my family’s life are here in the Bay Area. My wife Leigh gives her time to more local organizations than I can keep track of. Our youngest daughter just started high school, and our dog Buddy remains deeply committed to the trails on the Peninsula!

I thought about this for a long time. It’s the right call. The work now goes to leaders who are set up to take it further. I can’t wait to see what they do with it.

Over the years, people have asked me what’s kept me so energized and motivated in this work. I think the answer is that I got to spend my career working where doing good and doing well are the same thing. In technology, that’s the best place to operate.

Most companies start by focusing on doing well, so doing good becomes a compromise. It’s the last slide of the PowerPoint, or a separate .org. But when doing good is built into the product itself, it becomes a real competitive advantage. When we build valuable products, people find jobs, they hire, they learn skills, they start companies, they run companies. That creates value for members, customers and the world. And for our business too.

Not many companies get to work this way, and not many people get the chance to build something like it. We do.

That matters more now than it ever has. Technology will continue to touch every job on the planet, and the people building it will decide what kind of world it helps create. I can’t think of a better group to figure that out than the people reading this note.

Thank you for the best 18 years I could have asked for.

RR

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