Scientific research has traditionally been a slow and careful process. Scientists spend years testing ideas and experimenting. They read thousands of papers and try to connect different knowledge. This approach has worked for a long time, but it usually takes years to complete. Today, the world is facing urgent issues like climate change and diseases that need faster answers. Microsoft believes that artificial intelligence can help solve this problem. At Build 2025, Microsoft introduced Microsoft Discovery, a new platform that uses AI agents to accelerate research and development. This article explains how Microsoft Discovery works and why agents are important for research and development.
Challenges in modern scientific research
Traditional research and development faces several challenges that have continued for decades. Scientific knowledge is vast and spreads across many papers, databases and repositories. Connecting ideas from various fields requires special expertise and ample time. Research projects involve many steps, including reviewing literature, forming hypotheses, designing experiments, analyzing data, improving results, and more. Each step requires different skills and tools, making it difficult to maintain stable and consistent progress. Research is also an iterative process. Scientific knowledge grows through evidence, peer discussion, and continuous refinement. This iterativeness creates a large time delay between the initial idea and the practical application. These issues have led to a growing gap between how quickly science advances and how quickly solutions to problems such as climate change and disease are needed. These urgent issues require faster innovation than traditional research can provide.
Microsoft Discovery: Accelerating R&D with AI Agents
Microsoft Discovery is a new enterprise platform built for scientific research. This allows AI agents to work with human scientists to generate hypotheses, analyze data, and conduct experiments. Microsoft has built its platform on Azure. This provides the computing power needed for simulation and data analysis.
The platform solves research challenges through three important features: First, we use graph-based knowledge inference to connect information across different domains and publications. Second, we employ specialized AI agents that can focus on specific research tasks while coordinating with other agents. Third, we maintain an iterative learning cycle that adapts research strategies based on results and findings.
What makes Microsoft Discovery different from other AI tools is its support for the complete research process. Instead of supporting parts of the research, the platform supports scientists from the beginning to the end of the idea. This complete support can significantly reduce the time required for scientific discovery.
Graph-based knowledge engine
Traditional search systems find documents by matching keywords. Although effective, this approach often overlooks deeper connections within scientific knowledge. Microsoft Discovery uses a graph-based knowledge engine that maps relationships between data from both internal and external scientific sources. This system can understand conflicting theories, various experimental results, and field-wide assumptions. In addition to finding papers on topics, it can also show how findings from one area apply to problems in another area.
The knowledge engine also shows how to reach a conclusion. To track source and inference steps, researchers can see AI logic. This transparency is important because scientists need to understand not only the answers but also how conclusions are made. For example, if you are looking for new battery materials, the system can link your metallurgical, chemistry and physics knowledge. You can also find inconsistencies and missing information. This broad perspective helps researchers find new ideas that they might otherwise miss.
The role of AI agents in Microsoft Discovery
Agents are a type of artificial intelligence that allows you to act independently to perform tasks. Unlike normal AI, which helps humans by following instructions, agents make decisions, plan actions, and solve problems themselves. They work like intelligent assistants who help you get initiatives, learn from the data and complete complex tasks without the need for certain human instructions.
Instead of using one large AI system, Microsoft Discovery employs many professional agents that focus on a variety of research tasks and coordinate with each other. This approach mimics how human research teams behave with experts with different skills working together to share knowledge. However, AI agents can work continuously, process huge amounts of data and maintain full coordination.
The platform allows researchers to create custom agents that meet their professional requirements. Researchers can specify these requirements in natural language without the need for programming skills. Agents can also suggest which tools or models to use and how to work with other agents.
Microsoft Copilot plays a central role in this collaboration. It acts as a scientific AI assistant who coordinates specialized agents based on researcher prompts. Copilot allows you to understand the available tools, models, and knowledge bases within the platform and set up a complete workflow that covers the entire discovery process.
Real-world impact
The true test of any research platform lies in its real value. Microsoft researchers found a new coolant in a data center that was free from harmful PFAS chemicals in about 200 hours. This task usually takes months or years. Newly discovered coolants help reduce the environmental harms of technology.
Finding and testing new formulas in weeks rather than years can potentially move your data center cleanly. This process used multiple AI agents to screen molecules, simulate properties, and improve performance. After the digital phase, they successfully created and tested the coolant to see AI predictions and platform accuracy.
Microsoft Discovery is also used in other areas. For example, the Pacific Northwest National Laboratory uses it to create machine learning models for chemical separations needed in nuclear science. These processes are complex and urgent, making faster research important.
The future of scientific research
Microsoft Discovery is redefineing how research is carried out. Instead of working alone with limited tools, scientists can work with AI agents that process large information, find patterns across fields, and modify methods based on the results. This shift allows for new ways of discovering by linking ideas from different domains. Materials scientists can use biology insights, drug researchers can apply physical findings, and engineers can use chemical knowledge.
The platform’s modular design allows you to grow with new AI models and domain tools without changing your current workflow. It gives human researchers control and amplifies creativity and intuition while handling heavy computing tasks.
Issues and considerations
The possibilities of AI agents in scientific research are substantial, but some challenges remain. Ensuring that your AI hypothesis is an accurate need is a powerful check. Transparency in AI reasoning is important to gain trust from scientists. Integrating the platform into existing research systems is difficult. Organizations need to coordinate their processes to use agents while they follow regulations and standards.
Creating advanced research tools is widely available and raises questions about intellectual property and competition protection. The science field can change dramatically as AI makes research easier for many people.
Conclusion
Microsoft Discovery offers new ways to do research. This allows AI agents to work with human researchers to speed up discovery and innovation. Early successes like coolant discoveries and the interests of major companies suggest that AI agents could change how research and development works across the industry. By reducing research time from years to weeks or months, platforms like Microsoft Discovery can help you solve global challenges such as climate change and disease faster. The key is to balance the power of AI with human surveillance. Therefore, technology supports human creativity and decision-making rather than exchange.
