mlsanityData to 14 Oct 2026

A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond

arXiv 2508.11957 ↗First posted 16 Aug 2025cs.MA cs.AI cs.LGcited by 6

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Artificial Intelligence (AI) agents have rapidly evolved from specialized, rule-based programs to versatile, learning-driven autonomous systems capable of perception, reasoning, and action in complex environments. The explosion of data, advances in deep learning, reinforcement learning, and multi-agent coordination have accelerated this transformation. Yet, designing and deploying unified AI agents that seamlessly integrate cognition, planning, and interaction remains a grand challenge. In this review, we systematically examine the architectural principles, foundational components, and emergent paradigms that define the landscape of contemporary AI agents. We synthesize insights from cognitive science-inspired models, hierarchical reinforcement learning frameworks, and large language model-based reasoning. Moreover, we discuss the pressing ethical, safety, and interpretability concerns associated with deploying these agents in real-world scenarios. By highlighting major breakthroughs, persistent challenges, and promising research directions, this review aims to guide the next generation of AI agent systems toward more robust, adaptable, and trustworthy autonomous intelligence.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 70 of its 163 references that are in the map. First posted on arXiv 16 Aug 2025.

Builds on

70 of 163 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  2. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 68

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  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
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38 more
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50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2026InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agentscited by 10
  2. 2025InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarioscited by 4
  3. 2025Reliable agent engineering should integrate machine-compatible organizational principlescited by 2
  4. 2025Modeling User-System Behavior for Training-free Building of Private Domain Conversational Agentscited by 0
  5. 2026Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?cited by 0
  6. 2026Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation taskscited by 0

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