The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909783512055808 |
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| author | Shin, Woong Souza, Renan Rosendo, Daniel Suter, Frédéric Wang, Feiyi Balaprakash, Prasanna da Silva, Rafael Ferreira |
| author_facet | Shin, Woong Souza, Renan Rosendo, Daniel Suter, Frédéric Wang, Feiyi Balaprakash, Prasanna da Silva, Rafael Ferreira |
| contents | Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous, distributed scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science Shin, Woong Souza, Renan Rosendo, Daniel Suter, Frédéric Wang, Feiyi Balaprakash, Prasanna da Silva, Rafael Ferreira Artificial Intelligence Distributed, Parallel, and Cluster Computing Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous, distributed scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows. |
| title | The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.09915 |