Architectures, Learning Loops, and Emergence in Agentic Services Computing

Abstract

Large Language Model (LLM)-based agents are evolving from isolated task executors into interconnected societies of autonomous services capable of coordination, adaptation, and collective intelligence. This paper surveys and synthesizes recent advances in agentic services computing, LLM-based multiagent systems, and language-augmented reinforcement learning to analyze how feedback-driven learning loops enable emergent behaviors at system scale. We organize the design space along four dimensions: perception and context modeling, autonomous decision-making, multi-agent collaboration, and evaluation with alignment and trustworthiness. Building on this analysis, we propose a reference architecture for feedback-driven LLM-agent societies that integrates reinforcement learning, verbal feedback, episodic memory, coordination, and governance layers. We further define sociocognitive execution metrics for coordination density, goal agreement, role specialization, recovery, strategy diversity, throughput, behavioral variance, and failure tolerance, and illustrate their use through case studies and a localized ASC Micro-Testbed prototype. The prototype results show how critic feedback, episodic memory, and macro-level safety filtering support bounded recovery and constraint preservation. Finally, we identify open challenges, including cumulative learning without knowledge entropy, scalable coordination, trustworthy evolution, and standardized evaluation for reliable emergent agentic systems.