The Agent Era Is Here: A Comprehensive Survey of Large Language Model Agents
The Dawn of Intelligent Agents
The artificial intelligence landscape is undergoing a revolutionary transformation. We are witnessing the emergence of LLM agents—intelligent entities powered by large language models that can perceive environments, reason about goals, and execute actions autonomously. Unlike traditional AI systems that merely respond to user inputs, these modern agents actively engage with their environments through continuous learning, reasoning, and adaptation.
Today, we're excited to share our comprehensive survey paper, "Large Language Model Agent: A Survey on Methodology, Applications and Challenges," which systematically explores this rapidly evolving field.
Why This Survey Matters Now
The convergence of three key developments has transformed theoretical agent constructs into practical systems:
- Unprecedented reasoning capabilities of large language models
- Advancements in tool manipulation and environmental interaction
- Sophisticated memory architectures that support longitudinal experience accumulation
Commercial AI agent systems (e.g., DeepResearch, DeepSearch, and Manus) exemplify this paradigm shift—autonomously executing complex tasks from in-depth research to computer operation while adapting to specific user needs.
A Novel Methodological Framework
Our survey presents a unified taxonomy connecting agent construction, collaboration mechanisms, and evolutionary pathways. We offer a comprehensive perspective on how agents are defined, how they function individually or collectively, and how they evolve over time.
The Build-Collaborate-Evolve Framework
At its core, our methodology-centered approach examines agent systems through three interconnected dimensions:
1. Construction
How agents are built, including profile definition, memory mechanisms, planning capabilities, and action execution.
2. Collaboration
How agents interact and work together through centralized control, decentralized collaboration, or hybrid architectures.
3. Evolution
How agents learn and improve over time through self-learning, multi-agent co-evolution, and external resources.
Going Beyond Previous Surveys
Our work stands apart from existing surveys through:
- Methodology-centered taxonomy: We deconstruct LLM agent systems into their fundamental methodological components
- Build-Collaborate-Evolve framework: We analyze the three interconnected dimensions holistically
- Frontier applications and real-world focus: We examine cutting-edge tools, communication protocols, and real-world challenges including security, privacy, and ethics
Applications Across Domains
LLM agents are already transforming various sectors:
Scientific Discovery
From chemistry to biology, multi-agent systems are accelerating scientific research through hypothesis generation, experimental design, and data analysis.
Gaming
Agents are revolutionizing both game playing (as intelligent NPCs) and game generation (creating dynamic content).
Social Science
Economic simulations, psychological research, and social behavior modeling are being enhanced through agent-based approaches.
Productivity Tools
Software development and recommender systems are becoming more efficient and personalized through LLM agent integration.
Challenges and Future Directions
Despite remarkable progress, significant challenges remain:
- Scalability and coordination issues in multi-agent systems
- Memory constraints limiting long-term adaptation
- Reliability concerns in high-stakes domains
- Evaluation framework limitations for dynamic, multi-turn interactions
- Regulatory and ethical considerations for safe deployment
The Road Ahead
As we navigate the agent era, we anticipate transformative developments in coordination protocols, hybrid architectures, self-supervised learning, and safety mechanisms that will further enhance agent capabilities across diverse domains.
This survey aims to provide researchers and practitioners with a structured taxonomy for understanding, comparing, and advancing LLM agents from different perspectives. As these systems increasingly integrate into various critical domains, understanding their architectural foundations becomes essential not only for researchers but also for policy scholars, industry practitioners, and society at large.
Explore the Full Survey
Paper Link: LLM Agent Survey
GitHub Repo: Awesome-Agent-Papers