Scientific research was traditionally a slow and careful process. Scientists spend years testing ideas and conducting experiments. They read thousands of documents and try to combine various fragments of knowledge. This approach has been working for a long time, but it usually takes years. Today, the world is struggling with urgent problems, such as climate change and diseases that require faster answers. Microsoft believes that artificial intelligence can help solve this problem. On Build 2025Microsoft introduced Microsoft DiscoveryA 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 contemporary scientific research
Traditional research and development have a few challenges that have lasted for decades. Scientific knowledge is huge and spread over many articles, databases and repositories. Combining ideas from various fields requires specialist specialist knowledge and a lot of time. Research projects include many steps, such as a review of literature, creating hypotheses, designing experiments, analyzing data and improving results. Each step needs different skills and tools, which makes it difficult to maintain progress and consistency. Research is also an iteration process. Scientific knowledge is growing thanks to evidence, peer discussion and constant improvement. This iterative nature causes significant time delays between the beginnings and practical applications. Due to these problems, there is a growing difference between how quickly science develops and how quickly we need solutions such as climate and disease change. These urgent problems require faster innovation than traditional research.
Microsoft Discovery: Acceleration of research and development with AI agents
Microsoft Discovery is a new corporate platform built for scientific research. It enables AI agents to cooperate with human scientists, generate hypotheses, analyzing data and conducting experiments. Microsoft has built a platform on the Azure platform, which provides computing power needed for simulation and data analysis.
The platform solves research challenges through three key functions. First of all, he uses the reasoning of knowledge based on charts to combine information in various domains and publications. Secondly, it employs specialized AI agents that can focus on specific research tasks when coordinating with other agents. Thirdly, it maintains an iterative learning cycle that adapts research strategies based on results and discoveries.
What distinguishes Microsoft Discovery from other AI tools is support for the full research process. Instead of helping only one part of the research, the platform supports scientists from the beginning of the idea to the final results. This full support can significantly reduce the time needed for scientific discoveries.
Knowledge engine based on charts
Traditional search systems find documents by matching keywords. Although an effective approach often omits deeper connections in scientific knowledge. Microsoft Discovery uses a knowledge engine based on charts, which maps relations between both internal and external scientific sources. This system can understand conflicting theories, various results of experiments and assumptions in various fields. Instead of finding documents on a given topic, it can show how the arrangements in one area concern problems in another.
The knowledge engine also shows how it draws conclusions. It follows the sources and steps of reasoning, thanks to which scientists can check AI logic. This transparency is important because scientists must understand how conclusions are drawn, not just answers. For example, looking for new accumulator materials, the system can combine knowledge of metallurgy, chemistry and physics. It can also find contradictions or missing information. This wide view helps researchers find new ideas that, otherwise, could be omitted.
The role of AI agents at Microsoft Discovery
Some agent It is a kind of artificial intelligence that can act independently to perform tasks. Unlike ordinary artificial intelligence, which helps only people, following the instructions, agents make decisions, plan actions and solve problems. They work like intelligent assistants who can take the initiative, learn on the basis of data and help a fully complex work without the need for constant human instructions.
Instead of using one large AI system, Microsoft Discovery uses many specialized agents who focus on various research tasks and coordinate with each other. This approach imitates the way human research teams operate, in which experts with various skills cooperate and share knowledge. But AI agents can act continuously, supporting huge amounts of data and maintaining excellent coordination.
The platform allows scientists to create custom agents who meet their specialized requirements. Scientists can determine these requirements in natural language without the need for programming skills. Agents can also suggest which tools or models they should use and how they should work with other agents.
Microsoft Copilot Plays a central role in cooperation. He acts as an AI scientific assistant, which organizes specialized agents based on the researcher's prompts. Copilot understands available tools, models and knowledge base on the platform and can configure full work flows covering the entire discovery process.
Influence in the real world
The real test of any research platform is its actual value. Microsoft scientists found New coolant For data centers without harmful PFA chemicals in about 200 hours. This work would usually last months or years. The newly discovered coolant can help reduce environmental damage in technology.
Finding and testing new formulas in weeks instead of years can accelerate the transition to cleaner data centers. The process has used many AI funds to test particles, simulation of properties and improvement in performance. After the digital phase, they successfully performed and tested the coolant, confirming the AI forecasts and the platform accuracy.
Microsoft Discovery is also used in other fields. For example, Pacific Northwest National Laboratory application To create machine learning models for chemical separation needed in nuclear sciences. These processes are complex and urgent, which makes faster tests critical.
The future of scientific research
Microsoft Discovery defines the way of conducting research. Instead of working alone with limited tools, scientists can cooperate with AI agents that support large information, find patterns in various fields and change methods based on results. This change enables new methods of discovering by combining ideas from various domains. A material scientist can use biology, a drug researcher can use physics results, and engineers can benefit from chemistry knowledge.
The modular design of the platform allows it to grow with new AI models and domain tools without changing the current work flows. It maintains the control of human researchers, strengthening their creativity and intuition, while supporting heavy computer work.
Challenges and considerations
While the potential of AI factors in scientific research is significant, there are several challenges. Ensuring AI hypotheses is accurate, requires strong control. Transparency in AI reasoning is important to gain trust from scientists. The platform's integration to existing research systems can be difficult. Organizations must adapt processes to use agents, in accordance with regulations and standards.
Sharing advanced research tools raises questions about the protection of intellectual property and competition. Because AI facilitates many studies, scientific disciplines can change significantly.
Lower line
Microsoft Discovery offers a new way of conducting research. It enables AI agents to cooperate with human researchers, accelerate discovery and innovation. Early successes, such as the discovery of coolant and interest in the main companies, suggest that AI agents can potentially change the way research and development in various industries. By shortening the time of research from years to weeks or months, platforms such as Microsoft Discovery can help solve global challenges, such as climate change and disease. The key is balancing AI with human supervision, so the technology supports, and does not replace human creativity and decision making.
