LLM-based agent systems: a systematic literature review and position on hybrid architectures and evaluation frameworks

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems

Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.

Integration of Multi-agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in Agentic AI Systems: A Narrative Review

A Systematic Survey of LLM-Based Agentic AI Frameworks for Multi-Agent Coordination and Interoperability

Large Language Models (LLMs) have spurred the development of agentic artificial intelligence systems that can reason autonomously, plan, use tools, integrate memory, and carry out multi-step tasks. Unlike conventional prompt-response systems, LLM-based agents extend generative models to goal-oriented architectures that can decompose complex objectives, interact with external environments, and coordinate actions in iterative workflows. The survey systematically reviews LLM-based agent frameworks, multi-agent collaboration architectures, internal communication mechanisms, emerging cross-system interoperability protocols, and open research challenges. The review uses a PRISMA-based methodology, including literature from 2020 to March 2026, with particular focus on prominent frameworks such as LangChain, AutoGPT, AutoGen, MetaGPT, CAMEL, ChatDev, and CrewAI. The survey discusses the transition from modular chain-oriented to stateful graph-oriented and autonomous execution models and presents a systematic taxonomy of multi-agent collaboration architectures, including hierarchical, peer-to-peer, and role-based models. It also reviews the main internal communication mechanisms used to facilitate coordination and information sharing between LLM-based agents and distinguishes them from emerging interoperability protocols designed to support interaction across heterogeneous agents, tools, services, and frameworks. The analysis indicates that the promise of LLM-based agents for scalable automation, collaborative reasoning, and complex workflow execution comes with significant challenges in long-horizon reliability, evaluation standardization, communication security, cost-efficient orchestration, governance, and the interpretability of emergent multi-agent behavior. The survey covers architectural evolution, collaboration patterns, communication mechanisms, framework-level characteristics, and open research challenges. This provides a structured foundation for future research on reliable and trustworthy agentic artificial intelligence systems.

Architectures, Learning Loops, and Emergence in Agentic Services Computing

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.

From Multi-Agent Reinforcement Learning to Agentic AI: A Comprehensive Literature Review of Algorithmic Advances and Decision-Analytic Implications (2020-2025)

This literature review synthesizes 57 peer-reviewed and openly archived contributions published since 2019 into a thematic taxonomy spanning value-decomposition algorithms, trust-region and sequence-model policy methods, and LLM-based agentic frameworks, and discusses implications for applied decision analytics.