Traditionally, scientific research is a slow and cautious process. Scientists spend years testing ideas and experimenting. They read thousands of papers and tried to connect different knowledge. This method works for a long time, but usually takes years to complete. Today, the world faces urgent problems such as climate change and diseases that require faster answers. Microsoft believes artificial intelligence can help solve this problem. At Build Build in 2025, Microsoft launched Microsoft Discovery, a new platform that uses AI agents to accelerate R&D. This article explains how Microsoft Discovery works and why agents are important to R&D.
Challenges of modern scientific research
Traditional R&D faces several challenges that have continued for decades. Scientific knowledge is broad and distributed in many papers, databases and repositories. Connecting ideas in different fields requires special expertise and plenty of time. Research projects involve many steps, such as reviewing literature, forming hypotheses, designing experiments, analyzing data and refining results. Each step requires different skills and tools, so it is difficult to keep progress stable and stable. Furthermore, research is an iterative process. Scientific knowledge grows through evidence, peer discussion and continuous improvement. This iterative nature creates a significant time delay between the initial idea and the practical application. Due to these problems, there is a difference between the speed of scientific progress and the solutions we need to address problems such as climate change and disease. These urgent issues require faster innovation than traditional research institutes can offer.
Microsoft Discovery: Accelerate the development of AI agents
Microsoft Discovery is a new enterprise platform for scientific research. It enables AI agents to work with human scientists to generate hypotheses, analyze data, and perform experiments. Microsoft built a platform on Azure that provides the computing power required for simulation and data analysis.
The platform addresses research challenges with three key features. First, it uses graph-based knowledge reasoning to connect information across different fields and publications. Second, it uses a dedicated AI agent that can focus on specific research tasks while coordinating with other agents. Third, it maintains an iterative learning cycle that adjusts research strategies based on results and findings.
What makes Microsoft Discovery different from other AI tools is its support for the complete research process. The platform does not help just part of the research, but starts with the idea and end results. This comprehensive support can greatly reduce the time required for scientific discovery.
Graph-based knowledge engine
Traditional search systems search documents by matching keywords. Although effective, this approach often ignores deeper connections in scientific knowledge. Microsoft Discovery uses a graph-based knowledge engine that maps the relationship between data from internal and external scientific sources. The system can understand conflicting theories, different experimental results, and cross-domain assumptions. It not only allows for papers to be found on one topic, but also shows how discoveries in one field are applied to problems in another.
The knowledge engine also shows how to draw conclusions. It tracks the source and reasoning steps, so researchers can examine the logic of AI. This transparency is important because scientists need to understand how to draw conclusions, not just answers. For example, when looking for new battery materials, systems can connect knowledge of metallurgy, chemistry, and physics. It can also find conflicting or missing information. This broad perspective can help researchers find new ideas that might be missed.
The role of AI agents in Microsoft Discovery
Agent is an artificial intelligence that can perform tasks independently. With ordinary AI, it can only help humans by following instructions, agents make decisions, plan actions and solve problems on their own. They work like smart assistants and can proactively take plans, learn from data and help accomplish complex tasks without the need for ongoing human guidance.
Instead of using a large AI system, Microsoft Discovery adopts many professional agents that focus on different research tasks and coordinate with each other. This approach mimics how human research teams operate, experts with different skills work together and share knowledge. But AI agents can work continuously, process large amounts of data and maintain perfect coordination.
The platform allows researchers to create custom agents that meet their professional requirements. Researchers can specify these requirements in natural language without any programming skills. Agents can also suggest which tools or models they should use and how to work with other agencies.
Microsoft Copilot plays a central role in this collaboration. It acts as a scientific AI assistant, carefully planning a dedicated agent according to the researchers’ tips. Copilot understands the tools, models, and knowledge bases available in the platform and can set up a complete workflow covering the entire discovery process.
Real-world impact
The real test of any research platform lies in its real-world value. Microsoft researchers discovered a new coolant in a data center with harmless PFAS chemicals in about 200 hours. This work usually takes months or years. Newly discovered coolant can help reduce environmental hazards in the technology.
Finding and testing new formulas in weeks rather than years can accelerate the transition to a cleaner data center. This process uses multiple AI agents to screen molecules, simulate characteristics and improve performance. After the digital phase, they successfully made and tested the coolant, confirming the accuracy of AI’s predictions and platform.
Microsoft Discovery is also used in other fields. For example, the Pacific Northwest National Laboratory uses it to create machine learning models for the chemical separations required for nuclear science. These processes are complex and urgent, making faster research critical.
The future of scientific research
Microsoft Discovery is redefining how to do research. Instead of using limited tools, scientists can work with AI agents, instead of working with large amounts of information based on the results, finding patterns and changing methods based on the results. This transformation enables new ways of discovery by linking ideas from different domains. Materials scientists can use biological insights, drug researchers can apply physical discoveries, and engineers can use chemical knowledge.
The platform’s modular design allows it to grow using new AI models and domain tools without changing the current workflow. It keeps human researchers in control, expanding their creativity and intuition while dealing with heavy computational work.
Challenges and considerations
Although the potential of AI agents in scientific research is enormous, there are still some challenges. To ensure that AI assumptions are accurate, please perform a mandatory check. Transparency in AI reasoning is important in gaining trust from scientists. Integrating the platform into an existing research system can be difficult. Organizations must adapt processes to use agents while following regulations and standards.
The widespread use of advanced research tools raises questions about the protection of intellectual property rights and competition. Because AI makes research easier for many people, the science discipline can undergo significant changes.
Bottom line
Microsoft Discovery provides a new way of research. It enables AI agents to work with human researchers to accelerate discovery and innovation. Early successes, such as coolant discovery and interest from major companies, suggest that AI agents have the potential to change the way the industry develops. By reducing research time from years to weeks or months, platforms like Microsoft Discovery can help solve global challenges such as climate change and disease. The key is to balance AI with human supervision, so technology supports rather than replace human creativity and decision-making.
