Academy
Agentic systems, in which diverse agents cooperate to tackle challenging problems, are exploding in popularity in the AI community. However, existing agentic frameworks take a relatively narrow view of agents, apply a centralized model, and target conversational, cloud-native applications (e.g., LLM-based AI chatbots). In contrast, scientific applications require myriad agents be deployed and managed across diverse cyberinfrastructure. We introduce Academy, a modular and extensible middleware designed to deploy autonomous agents across the federated research ecosystem, including HPC systems, experimental facilities, and data repositories. To meet the demands of scientific computing, Academy supports asynchronous execution, heterogeneous resources, high-throughput data flows, and dynamic resource availability. It provides abstractions for expressing stateful agents, managing inter-agent coordination, and integrating computation with experimental control.
Academy is a modular and extensible middleware for building and deploying stateful actors and autonomous agents across distributed systems and federated research infrastructure. In Academy, you can:
⚙️ Express agent behavior and state in code 📫 Manage inter-agent coordination and asynchronous communication 🌐 Deploy agents across distributed, federated, and heterogeneous resources
Publications
A. Kamatar, J.G. Pauloski, Y. Babuji, R. Chard, M. Sakarvadia, K. Chard, I. Foster, “Empowering Scientific Workflows with Federated Agents.” arXiv preprint arXiv:2505.05428. 2026 Jan. 29.
J.G. Pauloski, K. Chard, I. Foster. “Agentic Discovery: Closing the Loop With Cooperative Agents,” in Computer, vol. 58, no. 10, pp. 20-27, 2025.
Funding and Acknowledgements
Available soon.