Agentic Services Computing

2025-09-29

AI Summary

Traditional service computing, centered on static request-response interactions, struggles to support the dynamic, goal-oriented, and autonomous multi-agent ecosystems enabled by large language models (LLMs). To address this, we propose “agentified service computing”—a novel paradigm that reimagines services as intelligent agents endowed with autonomous decision-making, social embeddability, and trustworthy evolution. Our approach integrates LLMs with established service computing techniques along four core dimensions: context awareness, autonomous execution, collaborative orchestration, and value alignment—yielding a holistic lifecycle framework spanning design, deployment, runtime operation, and evolution. This work establishes the first unified theoretical model and research framework for intelligent services. It significantly enhances service system interpretability, trustworthiness, and human-centered adaptability, thereby providing foundational support for next-generation adaptive service infrastructures.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The rise of LLM-powered agents is driving a fundamental transformation in services computing: from static, request-response functions to dynamic, goal-oriented, and autonomous multi-agent ecosystems. In response to this shift, we introduce Agentic Service Computing (ASC), a new paradigm that reimagines services as intelligent, self-adaptive, and socially embedded entities. This comprehensive survey presents a lifecycle-driven framework for ASC, structured around four core phases: Design, Deployment, Operation, and Evolution. We systematically analyze ASC through four foundational research dimensions: (1) Perception, Context, and Environment Modeling, (2) Autonomous Decision-Making and Task Execution, (3) Multi-Agent Collaboration and Organization, and (4) Evaluation, Value Alignment, and Trustworthiness. We examine how these dimensions are instantiated, integrated, and continuously adapted across the service lifecycle. Our synthesis reveals that agentic services are not merely assembled but orchestrated: contextual awareness enables robust deployment; autonomous reasoning supports real-time operation; collaborative structures emerge and evolve through interaction; and trustworthiness must be upheld as a cross-cutting, lifelong imperative. We further identify and discuss emerging trends shaping the future of ASC. By integrating classical principles of services computing with advances in LLM-based multi-agent systems, this work establishes a holistic and forward-looking foundation for ASC. It provides a unified reference for researchers and practitioners aiming to develop adaptive, accountable, and human-centered intelligent services.

Problem

• Transforming services from static functions to dynamic multi-agent ecosystems
• Establishing lifecycle framework for intelligent self-adaptive service entities
• Integrating autonomous decision-making with multi-agent collaboration and trust

Innovation

• Intelligent self-adaptive socially embedded services
• Lifecycle-driven framework for agentic service computing
• Multi-agent collaboration and autonomous decision-making integration