Kavli Affiliate: Cheng Peng
| First 5 Authors: Shuiguang Deng, Shuiguang Deng, , ,
| Summary:
The rise of large language model (LLM)-powered agents is transforming
services computing, moving it beyond static, request-driven functions toward
dynamic, goal-oriented, and socially embedded multi-agent ecosystems. We
propose Agentic Services Computing (ASC), a paradigm that reimagines services
as autonomous, adaptive, and collaborative agents capable of perceiving,
reasoning, acting, and evolving in open and uncertain environments. We organize
ASC around a four-phase lifecycle: Design, Deployment, Operation, and
Evolution. It is examined through four interwoven research dimensions: (i)
perception and context modeling, (ii) autonomous decision-making, (iii)
multi-agent collaboration, and (iv) evaluation with alignment and
trustworthiness. Rather than functioning as isolated layers, these dimensions
evolve together. Contextual grounding supports robust deployment; autonomous
reasoning drives real-time action; collaboration emerges from agent
interaction; and trustworthiness is maintained as a lifelong, cross-cutting
commitment across all lifecycle stages. In developing this framework, we also
survey a broad spectrum of representative works that instantiate these ideas
across academia and industry, mapping key advances to each phase and dimension
of ASC. By integrating foundational principles of services computing with
cutting-edge advances in LLM-based agency, ASC offers a unified and
forward-looking foundation for building intelligent, accountable, and
human-centered service ecosystems.
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