23 papers · ranked by Valyu relevance
Federico Lorenzo Barra, Giovanna Rodella, Alessandro Costa, Antonio Scalogna + 3 more
'Antonio Scalogna' 'Luca Carenzo' 'Alice Monzani' 'Francesco Della Corte'] Healthcare simulation scenario design remains a resource-intensive process, demanding significant time and expertise from educators. This article presents an innovative AI-driven agentic workflow for healthcare simulation scenario development…
Jörg Schultz
Effective animal training depends on well-structured training plans that ensure consistent progress and measurable outcomes. However, the creation of such plans is often time-intensive, repetitive, and detracts from hands-on training. Recent advancements in generative AI powered by large language models (LLMs) provide…
Varad Kulkarni, V. Jha, Nikhil Reddy, Yogesh Simmhan + 2 more
Agentic workflows that use autonomous AI Agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP) servers is rapidly rising. This introduces challenges in scalable cloud deployment and state management. Traditional hosting on Virtual Machines (VMs) is resource-intensive and lacks elasticity.…
Chengqi Zheng, Jianda Chen, Yueming Lyu, Wen Zheng Terence Ng + 4 more
'Haopeng Zhang' 'Yew-Soon Ong' 'Ivor Tsang' 'Haiyan Yin'] Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLMdriven construction. We propose MermaidFlow, a framework that redefines the agentic search space…
Yu, Chaojia, Cheng, Zihan + 8 more
—In the age of large language models (LLMs), autonomous agents have emerged as a powerfulparadigm for achieving general intelligence. These agents dynamically leverage tools, memory, and reasoning capabilities to accomplish user-defined goals. As agent systems grow in complexity, agent workflows—structured…
Joshua Philpott, Alina Kurjan, Adam P Cribbs
Reproducibility, automation, and flexibility remain persistent challenges in bioinformatics, where complex workflows require integration of diverse computational tools, rigorous quality control, and dynamic adaptability to evolving datasets. Existing workflow managers often trade off usability for flexibility, limiting…
Xinglin Wang, Zishen Liu, Shaoxiong Feng, Peiwen Yuan + 8 more
Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according to their dependencies. While recent work improves agent efficiency by optimizing the performance--cost--latency frontier, real deployments…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Yuanshuo Zhang, Yuchen Hou, Bohan Tang, Shuo Chen + 3 more
'Xiaowen Dong' 'Siheng Chen'] Agentic workflows invoked by Large Language Models (LLMs) have achieved remarkable success in handling complex tasks. However, optimizing such workflows is costly and inefficient in real-world applications due to extensive invocations of LLMs. To fill this gap, this position paper…
Alessandro Berti, Mayssa Maatallah, Urszula Jessen, Michal Sroka + 1 more
'Sonia Ayachi Ghannouchi'] Abstract. Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results. However, state-of-the-art LLMs struggle with complex scenarios that demand advanced reasoning capabilities. In the…
Prachi Gupta, William J. Riehl, Mikaela Cashman, Dylan Chivian + 6 more
Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for…
Alina Kurjan, Adam P. Cribbs
Agentic large language model (LLM) systems are being deployed in bioinformatics faster than they are understood, and single-metric evaluations conflate capabilities that fail independently. We introduce FlowBench, a benchmark that decomposes agentic bioinformatics performance into planning, fault recovery, biological…
Hanchao Liu, Rongjun Li, Weimin Xiong, Ziyu Zhou + 1 more
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction requires expert knowledge, presenting significant technical barriers. Recent advancements in Large Language Models (LLMs) have improved the…
Wenbo Wang, Simran Swain, Jaeyong Lee, Zuwan Lin + 10 more
Reproducibility in biological research and manufacturing remains constrained by the complexity of multi-step protocols, fragmented data-analysis pipelines, and the intrinsic variability of experimental execution. Here, we present Agentic Lab, an agentic-physical AI platform that unifies large language model and vision…
Soham Ghosh, Gaurav Mittal
Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate…
Parvathaneni Naga Srinivasu, Gorli L. Aruna Kumari, Shakeel Ahmed, Abdulaziz Alhumam
Introduction Rapid advancements in artificial intelligence (AI) have ushered in an era of hyperautomation and intelligent orchestration across multiple engineering domains, with healthcare emerging as one of the most impactful application areas. Among recent developments, Agentic AI has gained attention as a sub-domain…
Tsehaye Haidemariam
The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan Jones + 5 more
Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
Mahnoor Zulfiqar, Michael R. Crusoe, Birgitta König-Ries, Christoph Steinbeck + 2 more
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…