# The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

arXiv: https://arxiv.org/abs/2509.09915 · HTML: https://mlsanity.com/paper/2509.09915/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 9 of 9)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [Empowering Scientific Workflows with Federated Agents](https://mlsanity.com/paper/2505.05428/) (2025)
- [Applying the FAIR Principles to computational workflows](https://mlsanity.com/paper/2410.03490/) (2024)
- [AI-coupled HPC Workflow Applications, Middleware and Performance](https://mlsanity.com/paper/2406.14315/) (2024)
- [A Terminology for Scientific Workflow Systems](https://mlsanity.com/paper/2506.07838/) (2025)
- [A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery](https://mlsanity.com/paper/2506.17510/) (2025)

## Built on by (most cited first, 7 of 7)

- [LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agents](https://mlsanity.com/paper/2608.18398/) (2026)
- [R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation](https://mlsanity.com/paper/2601.09749/) (2026)
- [Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows](https://mlsanity.com/paper/2603.06394/) (2026)
- [How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic](https://mlsanity.com/paper/2603.11375/) (2026)
- [Rethinking Scientific Discovery in the Agentic Era](https://mlsanity.com/paper/2607.03863/) (2026)
- [CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows](https://mlsanity.com/paper/2608.04270/) (2026)
- [ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution](https://mlsanity.com/paper/2609.14211/) (2026)

## Abstract (arXiv)

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.