AI has arrived in the enterprise, and the shift is happening all at once. Every function, every role, every workflow is being reshaped. At the same time, a new class of organizations is emerging, one that will look fundamentally different from the companies that defined the last era of business. The winners won’t be those with the most demos, but those that turn AI into a governed, continuously improving system for running real work.

This isn’t just about chatbots, either. Those experiences are useful, but they don’t transform how large organizations operate. The real opportunity is teams of agents executing long running work across functions like software delivery, support, finance, HR, and operations — with the identity, context, policy, and human oversight required to trust them in production.

To make this possible, enterprises need more than access to a powerful AI model or scalable compute. What determines success is the system around the AI: how agents are built and deployed by engineering teams, how they’re contextualized in the enterprise, how they’re governed and observed in production, and how they improve safely over time. Without that system, AI remains fragmented, fragile, and difficult to trust at scale.

We’re taking a fundamentally different approach. We are building a comprehensive agent platform: one that supports many models, is open, and gives you choice and flexibility at every layer of the stack. And we are purposefully designing it with developers at the center. Today, the next pieces of that platform are clicking into place.

Building a system for the agentic enterprise

To succeed in this new era, an agent platform must meet a higher bar. It must run real production workloads, map real organizational complexity, and manage real business responsibility.

We’re building around three key principles:

First, it must be a single, integrated system, with support for a wide range of models.
Enterprises can’t afford to assemble their agent strategy one piece at a time. Disconnected tools stitched together after the fact can slow teams down and introduce unnecessary risk. Building, contextualizing, running, governing, and improving agents should happen within one coherent system. That’s why we’re bringing together Azure, GitHub, Microsoft IQ, Fabric, Foundry, Windows, Microsoft Security, and Microsoft 365 to operate as a single system you can use to deploy agents at enterprise scale. Enterprises also need the flexibility to choose the right model for the task, balancing quality, speed, and cost — including Microsoft models, partner models, and open models.

Second, it must be secured and governed by design.
Governance is easy to claim and much harder to deliver. Making it real means starting with a single stack that spans development through production, built on the identity, access, compliance, and security foundations enterprises already trust. By extending Entra, Purview, Defender, Agent 365, and the broader Microsoft Security stack, governance becomes native to the system rather than bolted on later, supporting the ambitions of an AI first enterprise without compromising control.

Third, it must improve continuously.
Enterprise AI systems can’t be static. Agent behavior, outcomes, and human feedback must flow back into the system, so it can improve safely over time under human oversight. As the system runs, models, workflows, and agents become more capable and more specific to an enterprise’s unique business processes. The result is a system that compounds in value the longer it’s in use.

These properties are becoming must-haves, and enterprises that align their AI ambitions with these three principles will pull ahead in quarters, not years.

So how does a system like this actually take shape inside a real enterprise? It starts where work begins, with how agents are built. Let’s walk through what that looks like on the platform we’ve built.

 

1. Build in GitHub

GitHub is where your developers already work. It’s where your dependencies live, where your application and code context is kept, where you collaborate with the open source community you depend on, and where you drive innovation. Building agents anywhere else means leaving all that behind.

Agents should be built the same way production software is built. You write code with GitHub Copilot to move faster. You bring together the assets that matter most: codebases, work items, agent skills, and tools. And because agents aren’t just code, you bring your evals and observability assets alongside them, all versioned the way any production system should be.

Agents must follow a lifecycle: source, test, deploy, observe, and improve. GitHub sets up that lifecycle and provides the necessary controls from day one. The result is a workflow designed for building agents with the right guardrails from the start. And you can do all this in one place, in a new app built for this system.

2. Contextualize with Microsoft IQ

Code is only part of an agent. To be useful, an agent also has to understand your business: your customers, your products, your contracts, your processes. Without enterprise context and intelligence you can trust, even the most capable model is guessing.

Enterprises require a wide variety of models and the ability to match the right model to the right job, but model choice alone is not enough. Microsoft IQ grounds agents in enterprise context by connecting to your business data wherever it lives, across Microsoft 365, your core business systems (such as customer and revenue data), and other systems your enterprise already relies on, like knowledge bases and your website. With Web IQ, the latest addition to the IQ platform, agents can also incorporate relevant information from the web when appropriate.

Contextualizing agents in enterprise data isn’t just about access. Pointing AI at raw information is inefficient and brittle. Microsoft IQ organizes, secures, and surfaces the right information in forms agents can actually use, so they can reach accurate insight without drowning in noise or hallucinating answers.

Once agents are grounded in the right context, enterprises can go further. With Frontier Tuning, you don’t just call AI models. You improve how they behave using your data and real-world workflows.

That includes Microsoft’s seven new MAI models, spanning image, voice, transcription, coding, and reasoning. Together, this model family is designed to work across the kinds of tasks that matter in the real world, and critically, these models are not static endpoints. They’re built to learn from how work actually gets done in your business.

Our reinforcement learning environments allow our models to be reinforced through actual outcomes in your environment. Think of them as training gyms for AI. Here the agent learns your very specific processes, standards, and way of working. It becomes specialized and adapted to you, delivering a measurable and better ROI.

Moreover, your custom or post-trained models all stay in your environment. Your intellectual property, your proprietary data, and the way work actually gets done become part of how your agents reason and act. The resulting intelligence runs in your environment, under your control, and the learning stays yours.

Without context and Frontier Tuning, agents are capable generalists. With it, they become a customized partner that understands the business they’re operating in.

3. Run in Foundry

Once agents are built and contextualized, they need a place to run. Not as an experiment. In production.

Agents and teams of agents place very different demands on a runtime than traditional applications do. They need to reason, act, call tools, coordinate with other agents, and adapt over time, all while operating under enterprise controls. Foundry is the runtime designed for that reality.

• The largest collection of models: Different agents need to be good at different things at different price points. Whatever the task, whatever the cost profile, Foundry provides access to the right model, and an optimized model router helps you balance quality, speed, and cost for each agent.
• Optimized performance for open models: With Fireworks AI on Foundry, enterprises get faster, more efficient inference directly into the platform.
• Support for any agent, including those not built on our stack: Bring in agents built on the Microsoft Agent Framework, LangGraph, GitHub Copilot SDK, Claude Agent SDK, or a custom harness.
• Tools and actions: Agents act on enterprise systems through MCP, connectors, APIs, and workflows, with safe execution by default.
• Evals and traces: Observability and traces make agent behavior measurable. If you can’t measure it, you can’t improve it.
• Continuous optimization: Foundry enables tuning of models, harnesses, IQs, tools, and actions over time, improving performance as agents operate in your world.

A trust, security, and policy rail wraps the entire runtime. Policy applies consistently across context access, tool calls, optimization updates, traces, and response delivery. The agent doesn’t just work. It works the way your enterprise requires.

This is where your agent stops being a project and starts becoming a production system.

4. Govern with Agent 365

Now multiply that agent by hundreds. Then thousands. That’s what happens as different teams build agents across an enterprise. Some are well designed. Some aren’t. Some have access they shouldn’t. Others are doing valuable work that no one else in the organization benefits from.

Enterprise governance isn’t optional. Enterprises need a way to see what’s running, understand what it can access, monitor task adherence, and enforce policies across their entire agent estate.

Agent 365, along with Entra, Purview, Defender, and the broader Microsoft Security stack, come together to do just this. And if you’re interested in AI for security in addition to securing your AI, there’s “MDASH.”

Every agent in your organization shows up in a single catalog, whether it was built in Foundry or elsewhere. IT sees who deployed an agent, what data and tools it can access, how it’s behaving, and what it costs. They can enforce policy or take action when required.

One place. Full visibility. Real control over what your agents do and don’t do.

5. Improve continuously

Agents can’t be static. Every agent action generates signal: trajectories, outcomes, feedback. The system captures it, refines it, and feeds it back. Observe. Evaluate. Improve. Roll out safely. Repeat.

This learning loop runs continuously, in production.

Most gains start with eval-driven improvements to the agent itself: prompts, context, skills, and tools. As clear patterns emerge, learning can extend into model routing across multiple models, fine-tuning, or reinforcement learning. But it all stays anchored in evaluation, improving agent quality and ROI to the level the business requires.

The loop is governed, not closed. Enterprises need to audit it, correct it, and control how to roll out changes. The system becomes more capable over time, guided by human oversight and increasingly autonomous, but never beyond your reach.

This is the hill-climbing model in action: system-level improvement, happening continuously while the system runs.

6. Surface where people work, and scale on Azure

Of course, none of this matters if it doesn’t reach the people doing the work.

Agents surface directly in the flow of work, in Teams, across Microsoft 365, and inside your own applications and experiences. Identity, security, and compliance are built in from the start, so the agents that your teams rely on day to day inherit the same trust model as the rest of your environment.

We support multiple platforms, but your agents can be developed and run in an optimized and secure way on Windows. You can run models both in the cloud and locally on your machine, and best-in-class sandboxing lets you run always-on agents safely.

When you need compute optimized for AI, global and sovereign infrastructure, or a route to market, the system scales on Azure, the same enterprise foundation customers have trusted for decades.

The system compounds

Every leading enterprise will converge on this model: a central AI platform that orchestrates work across the business, bringing together data, models, agents, and human judgment into a continuously improving and secure system.

As that system runs, its value compounds. Velocity increases and the bottleneck shifts from effort to human creativity and coordination. People are able to do more work independently, guided by shared context and fewer handoffs, while the business moves faster without adding friction.

We’re in a time of profound disruption. The enterprises that lead in this moment will be those that adapt as conditions change, simplify how work is coordinated across the business, and consistently turn intelligence into real outcomes. Microsoft’s agent platform is designed to do exactly that: it unlocks the ability to build, contextualize, run, govern, and improve agents as a single, integrated system.

At that point, the platform becomes more than a build layer. It becomes the operating system for enterprise AI at scale, where intelligence and trust are built in by design.

The post AI alone won’t change your business. The system running it will. appeared first on The Official Microsoft Blog.

Platforms shift when developers build. We explore, choose tools, dream, create.

This platform shift comes with more information than ever, ready at your fingertips. This shift, it’s about building fast AND THEN: it’s about building, operating, optimizing and observing. Securing your infrastructure, applications and agents in a seamless way that doesn’t slow you down from the moment you open your laptop to the moment you ship to production.

But there’s a duality in being a developer – you’re a tinkerer, choosing your own tools and models, and you’re an enterprise builder, shipping systems that demand governance, security and trust from day one.

Developers don’t need another way to just build and run an agent or app. They need trust. They need native context and knowledge. Most of all, they need choice to access the right model for the right problem.

This duality is where Microsoft thrives. We ask: what does it mean to be a modern developer today? And at Microsoft Build, we shared how we empower developers to build in this era of ubiquitous intelligence with the controls and security you expect at scale – on a platform that’s model diverse, open and heterogeneous at every layer of the stack. Bringing together what you know with what the world knows natively.

There’s a lot of news today, but there are three themes to anchor on.

First, intelligence that’s truly yours. With the Microsoft Agent Platform powered by your context and intelligence from Microsoft IQ, you can build your agent in GitHub, deploy it to Microsoft Foundry and optimize it automatically with models best suited for the job. Ground it in your intelligence and the world’s knowledge, then access it via Microsoft Teams, M365 or anywhere your team works. Designed to reduce the need to make tradeoffs between context and governance, security and speed, or models and tools.

Second, the full stack built your way. You should be able to build the way you want to build, with the tools, models and workflows you choose, and make it real. This expands beyond the agent platform to across the stack. Silicon to OS to developer tools to cloud – and that starts with Windows. Not Windows for “Windows developers.” Windows for developers, period. We’re bringing a new developer configuration that gives you more flexibility, a frictionless intelligent shell and terminal experience, local sandboxing for agents, new Windows Subsystem for Linux capabilities and powerful options to do it on your local machine.

Third is what comes next, where agentic systems move from code to human progress, amplifying what scientists and researchers can achieve. New frontiers in science and computing that start with the same developer platform underneath.

Together, developers get a multi-model ecosystem, from your laptop to the cloud, so you can build the frontier without giving up the control and craft that truly makes the work yours.

And as always, it starts with the developer. Let’s dive in.

Agents that know you, your business, and the world

As models become more capable and more available, the differentiator for any organization is no longer access to intelligence, but ownership. How does your expertise, data and way of working become a system that continuously learns and drives better outcomes? The goal is an ecosystem that gives companies their own agency, not one that funnels value back to a consultant or the model maker.

Your agents should reflect how you think and operate, from your business logic and institutional knowledge, down to your workflows.

That starts with context. Microsoft IQ, generally available today across GitHub Copilot, Microsoft Foundry and Copilot Studio, is a new context layer that grounds agents in both world knowledge and enterprise knowledge. Work IQ is the workplace intelligence layer for agents, capturing how work actually happens across Microsoft 365, organizational systems and external sources: people, emails, documents, meetings and how they connect. The Work IQ APIs, generally available on June 16, provide programmatic access to this intelligence layer and give agents the context they need to work effectively in your organization. Fabric IQ provides a shared semantic foundation over structured business data. Foundry IQ ties it together and enables retrieval planning across both enterprise knowledge and the live web.

New to the family is Web IQ, announced today: the fastest real-world grounding you can give your agents. An AI-first web search stack that’s model-agnostic and MCP-native, returning relevant passages at nearly 2.5x the speed of the next best alternative.

We’re also looking at how this context applies to new form factors, specifically always-on autonomous agents. Microsoft Scout is a new personal agent for work that we are bringing to Frontier customers today. Built on OpenClaw and WorkIQ, Microsoft Scout understands how you work, uses the tools you already live in, like Teams and Outlook, and proactively handles things like meeting prep, scheduling conflicts and routine tasks without asking. We’re excited to share more soon as we expand what Microsoft Scout can do and roll it out more broadly.

On the model layer, the Microsoft AI Superintelligence Team released a family of seven new in-house models, starting with MAI-Thinking-1 – Microsoft AI’s first reasoning model. Trained from scratch with zero distillation on enterprise grade, clean and commercially licensed data you can build on with confidence.

It’s a mid-sized, 35 billion active parameter model with a 256K context window built for high efficiency and performance, but importantly, at a low-token cost. On a blind test, independent raters prefer it to Sonnet 4.6 [1], and it matches Opus 4.6 on coding abilities on SWE Bench Pro [2]. MAI-Thinking-1 was designed to be good at complex multi-step instructions, long-context reasoning and code generation, and it’s open now on Foundry in private preview.

But that isn’t the only new model. MAI-Image-2.5 and its flash variant are Microsoft’s first models to serve both text-to-image (#3 on the Arena AI leaderboard) and enabling image-to-image workloads (#2 on the Arena AI leaderboard, surpassing Nano Banana 2). These are especially useful in creative workflows, when you want some assistance taking a concept into reality or enhancing existing image work. These models are live in PowerPoint, rolling out on OneDrive, and today, they’re landing on Foundry with market-leading quality per dollar.

There are other new members of the MAI family too: MAI Transcribe 1.5 combines state-of-the-art accuracy across 43 languages, with streaming coming soon. MAI-Voice-2 and its flash variant are now available in more than 15 additional languages with new voice options. And MAI-Code-1, our inference efficient coding model tuned for GitHub, is now available in Copilot and VS Code.

Developer choice doesn’t stop at our catalog. MAI models will also be available on Fireworks AI, Baseten and Open Router. And Fireworks AI is now generally available on Foundry, giving developers a single platform experience with enterprise governance and Azure data residency, regardless of the model they choose.

For organizations ready to make intelligence truly their own, Frontier Tuning applies reinforcement learning within your compliance boundary so agents can learn how the business actually works. Using your own data, domain knowledge and workflows, the result is a loop that sharpens as agents work. Available in private preview today.

And security and governance wraps the entire system. Agent 365 for local agents extends Entra, Defender and Purview into a single control plane to observe, govern and secure agents across your estate, regardless of where they’re hosted or what framework they’re built on. This is how you build at speed while maintaining control.

Alongside it is an open, end-to-end trust stack for AI agents on any framework anchored by two open-source projects: Adaptive Spec-driven Scoring for Evaluation and Regression Testing (ASSERT) for policy-driven safety evaluation, and the Agent Control Specification to standardize where and how to apply controls in the agent loop.

Also strengthening our defense is Codename MDASH. Our new multi-model agentic security system deploys 100+ agents to find exploitable bugs by reasoning about data flow, business logic and exploit chains with context-aware fixes delivered directly in the Defender Portal.

The full stack, your way

When we think about work in the agentic age, it requires a ubiquitous intelligence platform that spans cloud and edge. But as a developer, how do you build these rich, agentic systems while staying firmly in control? That means staying in flow instead of waiting on tools and running experiments in minutes rather than hours.

It starts at the silicon, and that’s where Surface RTX Spark Dev Box comes in – it’s designed for sustained workloads: long-running training jobs, agentic AI pipelines and local model fine-tuning.

Powered by NVIDIA RTX Spark, it delivers up to one petaflop of AI compute and 128 GB of unified memory, capable of running up to 120B parameter LLMs with up to 1 million tokens context using agents locally without cloud GPU instances [3]. Windows Services for Linux (WSL) 2 with native GPU passthrough and full CUDA support comes pre-configured for developers, with Visual Studio Code, GitHub Copilot and many more of your favorite tools pre-installed. Surface RTX Spark Dev Box will be available later this year in the US via Microsoft.com.

In the OS layer, Microsoft is making Windows an agent-native runtime. Microsoft Execution Containers (MXC), now in preview, gives developers and IT administrators a simpler way to create enterprise-grade sandboxed environments for agents, with containment enforced by the operating system itself. Describe your requirements once, and Windows enforces them everywhere your agents run.

This technology is now being used by OpenClaw on Windows, enabling execution of multi-step workflows inside these OS-enforced boundaries. NVIDIA’s OpenShell secure runtime for autonomous agents uses MXC and adds policy management, inference routing and PII obfuscation. Together, these capabilities give developers a safe environment for agent development and deployment and provide IT teams with the governance tools they need across local devices and cloud environments.

And when agents move to the cloud, hosted agents in Foundry Agent Service, in preview, provide the same model at scale: instant-on sandboxes per session, isolated execution, persistent memory and elastic scale. Think of it as the primitive for agents the way containers were for cloud-native apps.

Agentic development flows, whether in the IDE or in the command line, helps us write code faster than ever before, but that’s only one part of building software.

The GitHub Copilot app, now in preview, brings agentic development to a native desktop experience – and a much wider audience. Start from an idea, an existing issue or PR, orchestrate multiple agent sessions in parallel, and keep changes moving through review, CI and merge. Each session uses git worktrees, so work stays separated. Copilot handles execution, while developers say in control.

Developers can generate applications in seconds, but getting those apps into production still requires stitching together databases, APIs, authentication and infrastructure.

At the platform layer, Rayfin, now in preview, solves that. It brings a managed, backend-as-a-service to Microsoft Fabric, defined through GitHub-based workflows, so developers can move from prototype to production without managing infrastructure. Integration with Replit creates a fast path from prototype to enterprise-grade deployment with governance from day one. And as agentic applications scale, Azure HorizonDB delivers performance and reliability to meet your most demanding database requirements. It’s a fully managed PostgreSQL service on Azure that delivers more than 3x the throughput of comparable self-managed setups in internal testing.

The future belongs to builders

In the same way long-running agents have helped redefine software development and the role of the developer, new agents will help change research and development and what scientists can achieve.

Microsoft Discovery is generally available today. Built on Azure, it gives researchers an enterprise-grade agentic AI platform for the full science workflow. BHP is using it to find copper-leaching solutions in months instead of years. Syensqo is accelerating semiconductor R&D. GSK is iterating on drug discovery. Additionally, a free Discovery local app was announced for the broader scientific community. It is available in preview and only requires a GitHub Copilot account.

Finally, our next generation quantum computing chip Majorana 2 represents a giant step toward scale: an average qubit lifetime of 20 seconds with instances up to a minute, 1,000x higher reliability than our previous generation, and a path to one million qubits on a chip that fits in the palm of your hand. With the help of agentic AI, we will achieve a scalable quantum machine by 2029.

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Platforms don’t shift on their own; developers build them forward. Today is about giving you more to build with.

These are just some of the announcements at Build. We’re excited to connect with those of you joining virtually and in person for keynotes, code deep dives, hack sessions and more. Many sessions will also be available on demand.

For the full set of news, visit the Microsoft Build Live blog.

Now, let’s build.

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Footnotes:
1: measured via Surge our independent human rating partner
2: Based on the SWE Bench Pro Benchmark
3: Source: NVIDIA. Based on 1 Theoretical FP4 TOPS using the sparsity feature.

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