Research Agent: The 'GPT Moment' in the Scientific Community Has Arrived

Preface

What if scientific research had an “autonomous brain”: are you ready to embrace the era of Agentic Science?

What would happen if researchers were equipped with a tireless “autonomous brain”?

For a long time, scientific discoveries have followed a classic three-step model: observe phenomena—propose hypotheses—experimentally validate.

Today, AI for Science (AI4S, that is, AI-driven scientific research) is pressing the acceleration button for this model. However, deeper changes are only just beginning. As the boundaries of AI capabilities expand from “seeing” and “answering” to “reasoning” and “acting,” a completely new mode of scientific research—Agentic Science—is becoming a reality.

01 From 'Tool' to 'Agent': A Qualitative Change in 'Agency'

agentic science

To understand the essence of Agentic Science, we must first clarify a concept: what is an "Agent"?

A Scientific Research Agent (SRA) is an intelligent system based on large language models (LLMs), specifically designed to assist in various aspects of scientific research. By calling upon various professional tools and databases, it autonomously or semi-autonomously completes tasks in research such as searching, analyzing, reasoning, and executing.

In simple terms, past AI served as a passive "tool," whereas today's Agent is an actively collaborative "partner."

Academia has proposed a three-level framework to understand the development path of scientific research Agents:

Agent as Assistant: Focuses on specific narrow tasks, using small models with fine-tuning to perform single actions.

Agent as Partner: Integrates various tools to achieve capability leaps, capable of literature consultation, hypothesis generation, and experimental design.

Agent as Avatar: Possesses strong reasoning, deep memory, and interdisciplinary collaboration capabilities, able to autonomously organize and complete the full research workflow.

The role of AI in scientific research is undergoing a paradigm shift from "AI-assisted" to "AI scientist."

02 The 'Autopilot' of the Full Research Process: A New Height of Efficiency

AI FOR SCIENCE

Today's general-purpose research agents are already capable of covering the full-process loop of 'searching, reading, calculating, doing, and writing.' Relying on two core technical capabilities—massive tool invocation and long-range context management—they provide users with a research experience akin to 'autonomous driving.'

The iteration speed of this capability is astonishing. Studies have shown that the results produced by an agent running for 6 hours can match the saturated work output of a senior theoretical physics PhD over 1 to 3 months. This means that agents can, at an incredible speed, navigate the vast ocean of literature to identify logical relationships and efficiently screen the most promising experimental directions in the vast expanse of hypotheses.

03 Conversational Protein R&D Agent: A Vivid 'Dry-Wet Closed Loop' Case

Mingchen Li, et al., NeurIPS, 2024.

Yang Tan, et al., ISMB/ECCB, 2025

In the cutting-edge field of biomedicine, AI-driven protein development agents vividly demonstrate the power of Agentic Science.

Recently, Tianwu Technology, a domestic AI-driven full-stack protein development platform company, released the conversational protein development intelligent agent MatwingsVenus™ (Xiaowu™). This agent-centered one-stop protein development platform provides a complete 'conversational dry-wet loop.'

Researchers only need to assign tasks in natural language, and the agent automatically decomposes them, calling upon more than 200 integrated protein design tools, a protein database with tens of billions of labeled entries, and over 30 expert-tuned skills to accomplish tasks such as industry research, scaffold screening, interface design, and sequence optimization.

For example, if you say:

'Help me see what this protein does.'

'Check if this target has a structure.'

'Find relevant literature.'

'Which tissues express this gene highly?'

'Does this compound have any known activity?'

It won’t just give you a hollow reply; instead, it will try to search, compare, find, and organize the information.

The true highlight lies in the 'dry-wet loop' — after the agent completes the design, it doesn’t stop at the computational results on the screen. Through the platform’s automatic integration capability, the design can be directly imported into an automated shared laboratory, driving robots to perform sample preparation, protein purification, and functional assays in wet experiments. The experimental results are fed back to the agent, driving the next round of AI optimization iteration.

This forms a sustainable iterative loop where computation drives wet experiments and wet experiments feed back into computation.

04 Practical Validation: From Immune Receptor Targets to Sweet Proteins

AI Science Protein DNA

Theory ultimately has to be reflected in reality. The practical results of MatwingsVenus™ (XiaoWu™) intuitively demonstrate the value of research Agents.

In a completely new project for designing an immune regulatory receptor target from scratch, due to the lack of historical molecular references for the target, the predominance of highly polar regions on the surface, and the extremely high affinity of the natural ligand, traditional R&D approaches were extremely challenging. Relying on the MatwingsVenus™ (XiaoWu™) platform, the Agent independently completed scaffold screening, interface design, sequence optimization, and druggability prediction, ultimately successfully obtaining dozens of novel binder molecules with in vitro cellular blocking activity, completing the full process loop from de novo design to validation.

Another case involves the complex site modification of the sweet protein Monellin. Natural Monellin has a high sweetness but poor stability. The platform employed a multi-round iterative strategy of "Agent design—automated experiments—AI feedback—Agent redesign," gradually narrowing the search space in each round. In the end, several optimized samples had sweetness increased by more than tenfold compared to the wild type, with heat resistance maintained at around 75°C.

The complete experiences of these real projects show that research Agents are no longer just conceptual tools, but are transforming the complex R&D capabilities that were once only available to large enterprises and major research institutes into a "shared scientific power" that individual developers can also access. An era in which more people can participate in innovative scientific research is quietly arriving.

Conclusion

From AI for Science to Agentic Science, what we witness is not only a leap in technical metrics but also a profound transformation of the research paradigm. When research agents have the ability to understand your intentions, autonomously retrieve literature, design complex experiments, drive automated experiments, and provide analytical reports, the boundaries of science are being redefined in unprecedented ways.

The era of Agentic Science has already begun. In the face of this transformation, perhaps the question we most need to answer is not 'What can the agent do' but—

What do you want to discover with it!!!