Transforming R&D with agentic AI: Introducing Microsoft Discovery
Read the blog
Over the past year, we’ve made significant progress with Microsoft Discovery by working closely with research and development (R&D) organizations. Today, we’re 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’ve learned as we continue to broaden access to enterprise-grade, agentic AI capabilities for R&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&D teams operate and empower them to achieve more.
Learn how to get started with Microsoft Discovery
The era of agentic AI for research and development
Agentic AI opens a new chapter for R&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&D era.
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—a more sustainable material, a cleaner source of energy, a more effective treatment. But for many R&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&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.
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&D work gets done—not 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.
Figure 1
When Microsoft Discovery was introduced in private preview last year, it was an early expression of that possibility: an agentic AI platform purpose-built for R&D, bringing together the reasoning depth and collaborative intelligence that complex, real-world R&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.
The Microsoft Discovery platform
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—enabling agent empowerment, discovery loop automation, and quality at scale. Because it is built on Microsoft Azure’s enterprise cloud infrastructure, Microsoft Discovery is designed to operate within the security, compliance, transparency, and governance frameworks used to manage sensitive real-world R&D environments.
Figure 2
Agents are equipped with a broad range of digital, physical, and analytical tools used across R&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&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.
At the heart of Microsoft Discovery is the Discovery Engine that mimics the scientific method where specialized agents reason over large amounts of knowledge, generate hypotheses, and validate them in a complex tree across a vast search space. The Discovery Engine connects proprietary research data with external scientific literature—not solely to retrieve isolated facts but to reason across conflicting theories, experimental results, and domain-specific assumptions in a way that reflects how science 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.
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 Microsoft 365, Microsoft Foundry, and Microsoft Fabric enables organizations to interoperate across business agents, enterprise data, and institutional knowledge.
Real-world impact of Microsoft Discovery
Previously we shared how a team of Microsoft researchers leveraged advanced AI models and HPC tools from Microsoft Discovery to identify a novel, non-PFAS, immersion datacenter coolant prototype in about 200 hours. We’re excited to share a few examples of how customers have been using the platform during preview.
Syensqo
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&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.
As Microsoft Discovery workflows gained momentum, Syensqo expanded its ambition to scale these capabilities across both R&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 needs, and integrate engineering workflows within a connected digital ecosystem. Together, these advancements are establishing a strong, future-ready foundation to accelerate innovation-led growth—from early-stage discovery through engineering and large-scale formulation.
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&I) domains.
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.”
—Mike Radossich, Chief Executive Officer (CEO), Syensqo
GigaTIME
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. GigaTIME addresses this need by using AI to infer spatially resolved tumor microenvironment signals from routine hematoxylin and eosin (H&E) pathology slides. This approach makes insights such as immune infiltration, checkpoint context, and tumor proliferation more accessible at scale without the cost and throughput constraints of experimental assays. GigaTIME and 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.
The impact of GigaTIME increases when its outputs are embedded into real research workflows. Within Microsoft Discovery, 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.
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—where spatial biology informs hypotheses, hypotheses guide validation, and results feed the next cycle of learning with clarity and confidence.
Learn more about the GigaTIME and Microsoft Discovery integration to see how virtual mIF outputs are applied within Microsoft Discovery for oncology R&D.
PhysicsX
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—combining Large Physics Models and AI-native workflows to deliver near-real-time simulation by inference across the full engineering lifecycle.
Integrated into Discovery’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.
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.
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.
Engineering is still constrained by workflows built for the pre-AI era. This partnership changes that. PhysicsX’s frontier physics AI models, combined with Microsoft Discovery’s agentic orchestration and Azure infrastructure, give engineers the ability to explore design spaces that were previously out of reach—at the speed and scale that modern industrial development demands.
—Jacomo Corbo, CEO, PhysicsX
Synopsys
Synopsys is a leader in electronic design automation (EDA), computer aided engineering (CAE) tools, and intellectual property (IP), and plays a central role in the design and development of the most complex chips and systems for the leading semiconductor and systems companies of the world.
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 Microsoft Discovery to roll out solutions for chip design.
The semiconductor industry is facing an unprecedented set of challenges—demand 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.
Synopsys agentic AI stack with multi-agent workflows built on AgentEngineer™ technology, supported by Microsoft Discovery, have defined a new paradigm for the industry.
Chip design sits at the intersection of extreme complexity and outsized impact—exactly where AI can make the biggest difference. By bringing together Synopsys’ AI‑driven design leadership with Microsoft Discovery, we are enabling agentic AI to redefine semiconductor engineering workflows, unlock step‑function productivity gains, and accelerate the next era of technology innovation.
—Ravi Subramanian, Chief Product Management Officer, Product Management & Markets Group, Synopsys
A growing ecosystem
Microsoft Discovery works with an expanding ecosystem of partners offering integrated tools and specialized expertise.
Expanding what is possible for R&D
Expanding the preview marks an important step in making agentic AI available to a broader set of R&D organizations. Microsoft Discovery reflects our belief that the next generation of scientific progress can come from systems that combine human expertise with AI that can reason, plan, and act at scale.
We look forward to partnering with organizations that want to rethink how discovery happens and to help shape the future of enterprise R&D.
For organizations looking to get started with Microsoft Discovery be sure to review the technical documentation to understand requirements, onboarding prerequisites, and infrastructure considerations.
Get started with Microsoft Discovery
Learn how Microsoft Discovery enables agent‑driven discovery across complex, governed R&D environments.
Explore more
Microsoft Discovery is offered in preview. Features, availability, integrations, and performance characteristics described in this post may change prior to, or without, general availability and are not commitments. Statements about future capabilities (including any potential quantum integration) are forward-looking and subject to change. Customer and internal outcomes described reflect specific workflows and data; individual results will vary.
The post Microsoft Discovery: Advancing agentic R&D at scale appeared first on Microsoft Azure Blog.
Quelle: Azure
