Papers that address the orchestration of teams by synthesizing their workflows into a coherent whole, whether these teams are composed of human, machine, Generative AI (gen-AI), robot or AI-Agentic members are proposed.
Abstract
For our Special Interest Group (S.I.G.), we propose papers that address the orchestration of teams by synthesizing their workflows into a coherent whole, whether these teams are composed of human, machine, Generative AI (gen-AI), robot or AI-Agentic members. The bigger picture of interdependence, teamwork and Gen-AI indicates the need by organizations to build a library of human and artificial agents with bidirectional agency (responsibility) to achieve operational goals (missions), considering agentic risk tolerances, available skills, and vulnerabilities across a complex trade space among the skills available versus those needed for the tasks assigned to complete an operation. In this trade space, agents (human or artificial) from multiple systems with the requisite skills to accomplish a designated task and timeline combined to form a hierarchy of humans, robots, machines and AI. This complex system produces workflows that must be synthesized into a unit(s), then orchestrated to accomplish the goals assigned to it, yet remain trusted even in competitive and uncertain environments. Once synthesized into a unit (e.g., a team), Gen-AI provides the opportunity to not only advance the science of teams by orchestrating team products and performances, but also has raised several concerns (viz., AI used for deception, superintelligence, blackmail, or existential threats to humans). For our S.I.G., We are interested in orchestrating teams: What are the benefits, drawbacks, and, most importantly, can humans, machines and Agentic AI be synthesized and managed (orchestrated)?
This workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production, and focuses on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Min Du, Anbang Xu, Jasmine Jaksic et al.· Proceedings of the 32nd ACM...· 0 citations
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Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 2 citations
This paper introduces Physical Agentic AI, a framework for skill-grounded robot agent orchestration, in which each robot exposes a typed library of executable skills while a foundation model planner decomposes a task into phases and assigns each phase to a robot-skill pair.
Xin-Yuan Liu, Eren Sadikoglu, R. Chatterjee et al.· 0 citations
This paper examines the phase transition from deterministic algorithmic execution (DevOps) to probabilistic socio-technical orchestration (AgentOps) and synthesizes evolutionary biology and Hellenistic philosophy to reframe human-agent teaming as the integration of a synthetic symbiote.
Svetlana Meissner· TH Wildau Engineering and Na...· 0 citations
This hands-on tutorial introduces LangGraph, a framework built on top of LangChain for designing and orchestrating stateful agentic AI workflows that support complex reasoning workflows, adaptive execution paths, and collaborative multi-agent architectures.
Mohammad Amin Kuhail· Proceedings of the 32nd ACM...· 0 citations
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Nalan Karunanayake, Savindu Nanayakkara, Kasun Gayashan Hettihewa et al.· International Journal of Net...· 0 citations
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