Survey - LLM Agents

In this session, our readings cover:

Reading on: FOUNDATIONS - The Agent “Basics” Components

Review the core components of LLM agent architectures: Brain (Reasoning Engine), Perception (Input Processing), Memory Systems, Action & Tools, Planning & Orchestration, Multi-Agent Collaboration, and Safety & Evaluation.

┌─────────────────────────────────────────────────────────────┐
│                     AGENT ARCHITECTURE                       │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  BRAIN (Reasoning Engine) ────────────────────┐            │
│   ↓                                            │            │
│  PERCEPTION (Input Processing) ←───────────────┤            │
│   ↓                                            │            │
│  MEMORY (Context & Knowledge) ←────────────────┤            │
│   ↓                    ↓                       │            │
│  WORLD MODEL (Environment Understanding) ←─────┤            │
│   ↓                                            │            │
│  PLANNING (Task Decomposition) ←───────────────┤            │
│   ↓                                            │            │
│  ACTION (Tool Use & Execution) ←───────────────┤            │
│   ↓                                            │            │
│  MULTI-AGENT (Collaboration) ←─────────────────┤            │
│   ↓                                            │            │
│  SAFETY & EVALUATION ──────────────────────────┘            │
│   ↓                                                          │
│  DEPLOYMENT & SERVING                                        │
│   ↓                                                          │
│  APPLICATIONS                                               │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Core Component: LLM as the Central Reasoning Engine

Understanding the foundation model that serves as the “brain” of agentic systems - the core reasoning, language understanding, and decision-making capabilities.

Key Concepts: Deep neural networks, transformer architecture, emergent abilities, multimodal capabilities, recent architectural advances

Here are the related slide deck from the previous two course offerings:

2025 HIGH-IMPACT PAPERS on this topic

• a. Large Language Model Agent: A Survey on Methodology, Applications and Challenges (March 2025)
  • Link: https://arxiv.org/abs/2503.21460
  • GitHub: https://github.com/luo-junyu/Awesome-Agent-Papers
  • Framework Coverage: Brain-Perception-Action model, memory systems, planning mechanisms, multi-agent coordination, evolutionary pathways, evaluation methodologies
• b. A Survey on Large Language Model based Autonomous Agents** (Updated March 2025)
  • arXiv: https://arxiv.org/abs/2308.11432
  • Unified framework: Brain (profiling, memory, planning, action)
  • Extensive application coverage: single-agent, multi-agent, human-agent cooperation
  • Agent societies analysis: behavior, personality, social phenomena
• c. SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? (September 2025)
  • arXiv: https://arxiv.org/abs/2509.16941
  • Leaderboard: https://scale.com/leaderboard/swe_bench_pro_public
  • 1,865 problems from 41 actively maintained repositories
  • Enterprise-level complexity: Tasks requiring hours to days for professional engineers
  • Multi-file modifications: Substantial code changes across repositories
  • Three datasets: Public (11 repos), held-out (12 repos), commercial (18 proprietary repos)
  • Contamination-resistant: GPL-licensed and commercial codebases
• d. From LLMs to LLM-based Agents for Software Engineering: A Survey (August 2024, Updated 2025)
  • Link: https://arxiv.org/html/2408.02479v2
  • Six key topics: Requirement engineering, code generation, autonomous decision-making, software design, test generation, software maintenance
• e. LLM-Powered AI Agent Systems and Their Applications in Industry (May 2025)
  • Link: https://arxiv.org/html/2505.16120v1
• f. A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools (2025)
  • Referenced in: https://github.com/luo-junyu/Awesome-Agent-Papers
  • Comprehensive taxonomy of FMs in materials science
  • Reviews advances, resources, and future directions
  • Integration of agents in materials discovery workflows

More Readings:

A Survey on Large Language Model based Autonomous Agents

• [Submitted on 22 Aug 2023 (v1), last revised 15 Dec 2024 (this version, v6)]
• URL
• Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen
• Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at this https URL. Comments: change several 35 pages, 5 figures, 3 tables

Deploying Foundation Model Powered Agent Services: A Survey

• Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen
• [Submitted on 18 Dec 2024]
• Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI). To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize computational and communication resources, thereby ensuring effective resource allocation and seamless service delivery. In pursuit of this vision, this paper proposes a unified framework aimed at providing a comprehensive survey on deploying FM-based agent services across heterogeneous devices, with the emphasis on the integration of model and resource optimization to establish a robust infrastructure for these services. Particularly, this paper begins with exploring various low-level optimization strategies during inference and studies approaches that enhance system scalability, such as parallelism techniques and resource scaling methods. The paper then discusses several prominent FMs and investigates research efforts focused on inference acceleration, including techniques such as model compression and token reduction. Moreover, the paper also investigates critical components for constructing agent services and highlights notable intelligent applications. Finally, the paper presents potential research directions for developing real-time agent services with high Quality of Service (QoS).