{"id":50477,"date":"2026-04-22T09:00:00","date_gmt":"2026-04-22T16:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/en-us\/blog\/?p=50477"},"modified":"2026-04-22T09:12:43","modified_gmt":"2026-04-22T16:12:43","slug":"microsoft-discovery-advancing-agentic-rd-at-scale","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/microsoft-discovery-advancing-agentic-rd-at-scale\/","title":{"rendered":"Microsoft Discovery: Advancing agentic R&amp;D at scale"},"content":{"rendered":"\n<aside class=\"wp-block-msx-kicker-container\">\n\t<div class=\"wp-block-msx-kicker wp-block-msx-kicker--align-right\" data-bi-an=\"Kicker Right\">\n\t\t<p class=\"wp-block-msx-kicker__title\">Transforming R&amp;D with agentic AI: Introducing Microsoft Discovery<\/p>\n\t\t<a\n\t\t\tclass=\"wp-block-msx-kicker__cta btn btn-link\"\n\t\t\thref=\"https:\/\/azure.microsoft.com\/en-us\/blog\/transforming-rd-with-agentic-ai-introducing-microsoft-discovery\/\"\n\t\t\ttarget=\"_blank\"\t\t>\n\t\t\t<span>Read the blog<\/span> <span class=\"glyph-append glyph-append-xsmall wp-block-msx-kicker__glyph glyph-append-chevron-right\"><\/span>\n\t\t<\/a>\n\t<\/div>\n<\/aside>\n\n\n\n<p class=\"wp-block-paragraph\">Over the past year, we\u2019ve made significant progress with <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a> by working closely with research and development (R&amp;D) organizations. Today, we\u2019re sharing how those efforts are translating into real momentum for customers and partners, while also expanding preview access to Microsoft Discovery. This next phase reflects what we\u2019ve learned as we continue to broaden access to enterprise-grade, agentic AI capabilities for R&amp;D. The Microsoft Discovery platform continues to evolve with new capabilities, expanded partner interoperability, and a growing set of results with real-world scientific outcomes and engineering transformation. We believe what comes next can meaningfully change how R&amp;D teams operate and empower them to achieve more.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-75\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Learn how to get started with Microsoft Discovery<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-era-of-agentic-ai-for-research-and-development\">The era of&nbsp;agentic AI for research and development&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Agentic AI opens a new chapter for R&amp;D where autonomous agent teams, guided by human expertise, perform the core research and engineering tasks in a redefined agentic loop. Specialized agents can reason on top of vast amounts of organizational and public-domain knowledge, create hypotheses on an expanded search space, test and validate those hypotheses at scale, analyze the results, and feed conclusions into iterative loops. Empowering science and engineering experts with agentic AI has the potential to reshape the future of science and engineering, enabling organizations to lead boldly in the new Frontier R&amp;D era.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This fundamental shift requires a deep transformation that encompasses both technological and organizational challenges. Scientific discovery has always been defined by ambition and the relentless pursuit of what comes next\u2014a more sustainable material, a cleaner source of energy, a more effective treatment. But for many R&amp;D teams the hardest work can begin after an idea shows promise. Turning concepts into outcomes requires repeated development cycles that involve reformulating candidates as new datasets emerge, re-engineering existing materials to meet evolving regulatory and performance requirements, or adjusting designs when performance, yield, or manufacturability fall short. As R&amp;D grows more complex, tooling must evolve to help close the distance between what researchers and engineers want to pursue and what they can practically deliver.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Earlier generations of AI offered incremental relief through faster search and better retrieval, but lacked the deeper reasoning that genuinely complex, multi-disciplinary science demands. Tradeoffs across cost, performance, yield, compliance, and timelines must be revisited repeatedly as development progresses. But the convergence of large-scale reasoning models, agentic AI architectures, and high-performance cloud infrastructure has created a genuine opportunity to rethink how R&amp;D work gets done\u2014not only to improve existing processes at the margins, but to help teams iterate faster and move from hypothesis to candidate development to outcome with greater confidence.<\/p>\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69e91a8a76eaf&quot;}\" data-wp-interactive=\"core\/image\" class=\"wp-block-image aligncenter size-full wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on-async--click=\"actions.showLightbox\" data-wp-on-async--load=\"callbacks.setButtonStyles\" data-wp-on-async-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/Picture1.webp\" alt=\"Three principles for success with embracing agentic discovery: empower AI agents, automate discovery loop, and scale quality.\" class=\"wp-image-50478 webp-format\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/Picture1.webp\"><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on-async--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Figure 1<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">When <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a> was introduced in private preview last year, it was an early expression of that possibility: an agentic AI platform purpose-built for R&amp;D, bringing together the reasoning depth and collaborative intelligence that complex, real-world R&amp;D requires. The response from engineers and researchers across life sciences, chemistry and materials science, physics, semiconductors, and other fields made clear that the need was real and the approach was right.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-microsoft-discovery-platform\">The Microsoft Discovery&nbsp;platform&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Microsoft Discovery is an extensible platform that brings together agentic orchestration, advanced reasoning, a graph-based knowledge foundation, and high-performance computing. It helps drive the three principles outlined in Figure 1 for effective agentic discovery\u2014enabling agent empowerment, discovery loop automation, and quality at scale. Because it is built on <a href=\"https:\/\/azure.microsoft.com\/en-us\">Microsoft Azure<\/a>\u2019s enterprise cloud infrastructure, Microsoft Discovery is designed to operate within the security, compliance, transparency, and governance frameworks used to manage sensitive real-world R&amp;D environments.<\/p>\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69e91a8a77d16&quot;}\" data-wp-interactive=\"core\/image\" class=\"wp-block-image aligncenter size-full wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on-async--click=\"actions.showLightbox\" data-wp-on-async--load=\"callbacks.setButtonStyles\" data-wp-on-async-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/Picture2.webp\" alt=\"Example of the discovery loop process involving scientific reasoning, hypotheses generation, and experimentation &amp; analysis with HPC, AI, quantum, and robotics.\" class=\"wp-image-50481 webp-format\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/Picture2.webp\"><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on-async--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Figure 2<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Agents are equipped with a broad range of digital, physical, and analytical tools used across R&amp;D. This includes in silico experimentation environments such as high-performance compute (HPC) clusters, specialized large quantitative models (LQMs) and agents, and potential future integration with quantum capabilities as they become applicable to commercial R&amp;D. It also allows interoperability with physical labs, facilitating the lab procedure generation and even direct operation with robotics, lab instrumentation, and Internet of Things (IoT)-enabled devices that agents can operate under human oversight.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At&nbsp;the heart of <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a> is the&nbsp;Discovery Engine&nbsp;that&nbsp;mimics the scientific method where specialized agents reason over&nbsp;large amounts&nbsp;of knowledge, generate hypotheses,&nbsp;and&nbsp;validate&nbsp;them in&nbsp;a complex tree across a vast search space.&nbsp;The Discovery Engine connects proprietary research data with external scientific literature\u2014not solely to retrieve isolated facts but to reason across conflicting theories, experimental results, and domain-specific assumptions in a way that reflects how science&nbsp;actually works. This contextual depth is what separates Microsoft Discovery from general-purpose AI tools and enables the platform to function as a genuine thinking partner across the full arc of a research program.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Built-in governance controls help ensure that agent driven research remains aligned with strategic priorities, security and compliance standards, and safety requirements. These systems provide centralized management, audit trails, and checkpoints that help maintain reliability as agentic throughput grows. The platform is extensible by design which enables integration with existing business tools and assets, partner solutions, and open-source models. Integration with <a href=\"https:\/\/www.microsoft.com\/en-us\/microsoft-365\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft 365<\/a>, <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/ai-foundry\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Foundry<\/a>, and <a href=\"https:\/\/www.microsoft.com\/en-us\/microsoft-fabric\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Fabric<\/a> enables organizations to interoperate across business agents, enterprise data, and institutional knowledge.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"real-world-impact-of-microsoft-discovery\">Real-world impact of Microsoft Discovery&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Previously we shared&nbsp;how a team of <a href=\"https:\/\/www.youtube.com\/watch?v=lC0QLivuRgw\">Microsoft researchers leveraged advanced AI models<\/a> and HPC tools from Microsoft Discovery to identify a novel, non-PFAS, immersion datacenter coolant prototype in about 200 hours. We\u2019re excited to share a few examples of how customers have been using the platform during preview.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"syensqo\">Syensqo<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A global leader in advanced materials and specialty chemicals, Syensqo is advancing a bold, multi-year transformation of its technology landscape to accelerate data-driven science, advanced simulation, and AI-enabled discovery. Building on early success with Microsoft Discovery, Syensqo is now scaling these capabilities enterprise-wide to unlock greater scientific and business impact. This next phase focuses on modernizing R&amp;D knowledge foundations, expanding access to scalable, cost-efficient, cloud-based compute, and establishing a unified operating model that brings together data, high-performance computing, and emerging agentic AI to power the future of innovation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As Microsoft Discovery workflows gained momentum,&nbsp;Syensqo&nbsp;expanded its ambition to scale these capabilities across both R&amp;D and commercial organizations, unlocking new opportunities for end-to-end innovation. This evolution is enabling teams to unify scientific and business datasets, scale simulation environments in line with increasingly complex development&nbsp;needs, and&nbsp;integrate engineering workflows within a connected digital ecosystem. Together, these advancements are&nbsp;establishing&nbsp;a strong, future-ready foundation to accelerate innovation-led growth\u2014from early-stage discovery through engineering and large-scale formulation.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To realize this vision, Syensqo is advancing its science and commercial data and simulation platforms on Azure. By centralizing critical datasets within a governed, enterprise-grade data backbone and extending Microsoft Discovery workflows onto highly scalable cloud compute, the company is establishing a modern, standardized operating model for innovation. This shift enables more seamless collaboration, supports advanced analytics and simulation at scale, and lays the groundwork for next-generation, AI-powered workflows across priority research and innovation (R&amp;I) domains.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>We are entering a new phase of our partnership with Microsoft, focused on scaling AI agents across research, sales and marketing to drive near-term growth. By connecting customer demand to scientific development and back to market execution, agentic AI is enabling faster cycles, sharper prioritization, and tangible impact on revenue growth and business performance.\u201d&nbsp;<\/em><\/p>\n\n\n\n<p class=\"has-large-font-size wp-block-paragraph\">\u2014Mike&nbsp;Radossich, Chief Executive Officer (CEO), Syensqo<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"gigatime\">GigaTIME&nbsp;&nbsp;<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Modern oncology increasingly depends on understanding tumors not only by appearance, but by the biological signals that shape cell behavior, immune response, and treatment outcomes.\u00a0<a href=\"https:\/\/aka.ms\/gigaTIME-Discovery\" target=\"_blank\" rel=\"noreferrer noopener\">GigaTIME<\/a>\u00a0addresses this need by using AI to infer spatially resolved tumor microenvironment signals from routine hematoxylin and eosin (H&amp;E) pathology slides. This approach makes insights such as immune infiltration, checkpoint context, and tumor proliferation more accessible at scale\u00a0without the cost and throughput constraints of experimental assays.\u00a0GigaTIME\u00a0and its outputs within Microsoft Discovery are intended for research use only. They are not a medical device and are not intended for clinical diagnosis, treatment, prevention, or patient-management decisions.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The impact of GigaTIME increases when its outputs are embedded into real research workflows. Within <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a>, virtual multiplex immunofluorescence (mIF) predictions move beyond standalone visualizations and become inputs to ongoing scientific reasoning. Spatial phenotypes can be generated consistently across cohorts, localized to single cell context, and connected to supporting evidence such as literature, biomarkers, and downstream endpoints. This allows researchers to interpret results systematically, question assumptions, and refine biological hypotheses over time.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Microsoft Discovery supports this work in a way that is reproducible, scalable, and governed end to end. GigaTIME can be used alongside additional models, data sources, and tools within a shared environment that supports iteration, comparison, and validation. Rather than accelerating a single analytical step, Discovery supports a full discovery loop\u2014where spatial biology informs hypotheses, hypotheses guide validation, and results feed the next cycle of learning with clarity and confidence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/aka.ms\/gigaTIME-Discovery\" target=\"_blank\" rel=\"noreferrer noopener\">Learn more about the GigaTIME and Microsoft Discovery integration<\/a>\u00a0to see how virtual\u00a0mIF\u00a0outputs are applied within Microsoft Discovery for oncology R&amp;D.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"physicsx\">PhysicsX<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">PhysicsX, a leader in physics AI for industrial engineering and manufacturing, is partnering with Microsoft to bring agentic engineering into production through Microsoft Discovery. At the core of this collaboration is the PhysicsX platform\u2014combining Large Physics Models and AI-native workflows to deliver near-real-time simulation by inference across the full engineering lifecycle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Integrated into Discovery&#8217;s agentic environment, the PhysicsX platform enables engineers to move beyond sequential, solver-driven workflows and explore significantly larger design spaces, evaluating thousands of manufacturable candidates in days, without compromising physical fidelity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The collaboration is already delivering impact at Microsoft Surface. Faced with tightly coupled constraints across thermal performance, acoustics, and form factor, the Surface engineering team used the PhysicsX platform through Discovery to reimagine their cooling fan design process. What previously required weeks of simulation and manual setup is now compressed into days. Discovery agents orchestrate the generation, evaluation, and optimization of thousands of geometries, surfacing high-performing, production-ready designs for validation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The result is a step change in engineering productivity: faster iteration, broader design-space coverage, and more confident decision-making. The approach is now being extended across additional components in the Surface portfolio.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>Engineering is still constrained by workflows built for the pre-AI era. This partnership changes that. PhysicsX&#8217;s frontier physics AI models, combined with Microsoft Discovery&#8217;s agentic orchestration and Azure infrastructure, give engineers the ability to explore design spaces that were previously out of reach\u2014at the speed and scale that modern industrial development demands.<\/em><\/p>\n\n\n\n<p class=\"has-large-font-size wp-block-paragraph\">\u2014Jacomo Corbo, CEO,&nbsp;PhysicsX<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"synopsys\">Synopsys<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Synopsys is a leader in electronic design automation (EDA), computer aided engineering (CAE) tools, and intellectual property (IP), and plays&nbsp;a central role&nbsp;in the design and development of the most complex chips and systems for the leading semiconductor and systems companies of the world.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Synopsys and Microsoft have been partnering since 2019, helping pioneer software-as-a-service (SaaS) models on Microsoft Azure. Synopsys also launched the first Silicon Copilot in collaboration with Microsoft and is continuing that journey by leveraging <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a> to roll out solutions for chip design.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The semiconductor industry is facing an unprecedented set of challenges\u2014demand for high performance chips is growing exponentially, complexity of sustainable, power-efficient chip design, and a critical shortage of skilled engineering. Agentic systems can help mitigate these challenges while accelerating design cycles.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Synopsys agentic AI stack\u202fwith multi-agent workflows built on AgentEngineer\u2122 technology, supported by Microsoft Discovery, have defined a new paradigm for the industry.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>Chip design sits at the intersection of extreme complexity and outsized impact\u2014exactly where AI can make the biggest difference. By bringing together Synopsys\u2019 AI\u2011driven design leadership with Microsoft Discovery, we are enabling agentic AI to redefine semiconductor engineering workflows, unlock step\u2011function productivity gains, and accelerate the next era of technology innovation.<\/em><\/p>\n\n\n\n<p class=\"has-large-font-size wp-block-paragraph\">\u2014Ravi Subramanian, Chief Product Management Officer, Product Management &amp; Markets Group, Synopsys<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a-growing-ecosystem\">A growing ecosystem<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Microsoft Discovery works with an expanding ecosystem of partners offering integrated tools and specialized expertise.<\/p>\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69e91a8a7934a&quot;}\" data-wp-interactive=\"core\/image\" class=\"wp-block-image aligncenter size-full wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on-async--click=\"actions.showLightbox\" data-wp-on-async--load=\"callbacks.setButtonStyles\" data-wp-on-async-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/image005.webp\" alt=\"Logo lockups of partners that work with Microsoft Discovery.\" class=\"wp-image-50525 webp-format\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/04\/image005.webp\"><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on-async--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"expanding-what-is-possible-for-r-d\">Expanding&nbsp;what is possible&nbsp;for&nbsp;R&amp;D&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Expanding the&nbsp;preview marks&nbsp;an important step&nbsp;in making agentic AI available to a broader set of R&amp;D organizations. <a href=\"https:\/\/aka.ms\/MicrosoftDiscovery\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Discovery<\/a> reflects our belief that the next generation of scientific progress&nbsp;can&nbsp;come from systems that combine human&nbsp;expertise&nbsp;with AI that can reason, plan, and act at scale.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We look forward to partnering with organizations that want to rethink how discovery happens and to help shape the future of enterprise R&amp;D.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For&nbsp;organizations looking to get started with&nbsp;Microsoft Discovery&nbsp;be sure to&nbsp;review&nbsp;the&nbsp;<a href=\"https:\/\/aka.ms\/MicrosoftDiscoveryDocs\" target=\"_blank\" rel=\"noreferrer noopener\">technical documentation<\/a>&nbsp;to understand requirements, onboarding prerequisites, and infrastructure considerations.<\/p>\n\n\n\n<aside class=\"cta-block cta-block--align-left cta-block--has-image wp-block-msx-cta\" data-bi-an=\"CTA Block\">\n\t<div class=\"cta-block__content\">\n\t\t\t\t\t<div class=\"cta-block__image-container\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"575\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/02\/Azure-AIPlatform-Dark-1-1024x575.webp\" class=\"cta-block__image\" alt=\"A dark 3D illustration with shapes.\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/02\/Azure-AIPlatform-Dark-1-1024x575.webp 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/02\/Azure-AIPlatform-Dark-1-300x169.webp 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/02\/Azure-AIPlatform-Dark-1-768x432.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/02\/Azure-AIPlatform-Dark-1.webp 1260w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\t\t\t<\/div>\n\t\t\n\t\t<div class=\"cta-block__body\">\n\t\t\t<h2 class=\"cta-block__headline\">Get started with Microsoft Discovery<\/h2>\n\t\t\t<p class=\"cta-block__text\">Learn how Microsoft Discovery enables agent\u2011driven discovery across complex, governed R&amp;D environments.<\/p>\n\t\t\t\t\t\t\t<div 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Statements about future capabilities (including any potential quantum integration) are\u202fforward-looking\u202fand subject to change.&nbsp;Customer and internal outcomes described reflect specific workflows and data;\u202findividual results will vary.&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Expanded preview access for Microsoft Discovery brings new enterprise-grade, agentic AI capabilities for research and development 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