Advances & challenges in foundation agents: Preface
This article is the preface of a series of articles featuring the Liu and colleagues’ book Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems.
Large language models (LLMs) have revolutionized artificial intelligence (AI) by demonstrating unprecedented capabilities in natural language and multimodal understanding, as well as reasoning and generation. These models are trained on vast datasets and exhibit emergent abilities such as reasoning, in-context learning, and even rudimentary planning. While these models represent a major step forward in realizing intelligent machines, they themselves do not yet fully embody all the capabilities of an intelligent being. Since the early days of artificial intelligence, AI researchers have long been on a quest for a truly “intelligent” system that can learn, plan, reason, sense, communicate, act, remember, and demonstrate various human-like abilities and agility. These beings, known as intelligent agents, should be able to think both long-term and short-term, perform complex actions, and interact with humans and other agents. LLMs are an important step towards realizing intelligent agents, but we are not there yet.
This series provides a comprehensive overview of the current state of the art of LLM-based intelligent agents. In the past, there have been numerous research papers and books on intelligent agents, as well as a flurry of books on LLMs. However, there has scarcely been comprehensive coverage of both. While LLMs can achieve significant capabilities required by agents, they only provide the foundations upon which further functionalities must be built. For example, while LLMs can help generate plans such as travel plans, they cannot yet generate fully complex plans for complex and professional tasks, nor can they maintain long-term memories without hallucination. Furthermore, their ability to perform real-world actions autonomously remains limited. We can view LLMs as engines, with agents being the cars, boats, and airplanes built using these engines. In this view, we naturally seek to move forward in designing and constructing fully functioning intelligent agents by making full use of the capabilities provided by LLMs.
In this engine-vehicle analogy of the interplay between LLMs and agents, we naturally ask: How much of the capabilities of intelligent agents can current LLM technologies provide? What are the functions that cannot yet be realized based on current LLM technologies? Beyond LLMs, what more needs to be done to have a fully intelligent agent capable of autonomous action and interaction in the physical world? What are the challenges for fully integrated LLM-based agents? What additional developments are required for capable, communicative agents that effectively collaborate with humans? What are the areas that represent low-hanging fruits for LLM-based agents? What implications will there be for society once we have fully intelligent LLM-based agents, and how should we prepare for this future?
These questions transcend not only the engineering practice of extending current LLMs and agents but also raise potential future research directions. Liu and colleagues’ are frontier researchers from AI, spanning from LLM development to agent design, who have come together to comprehensively address these questions. The series consists of four parts. The first part presents an exposition of the requirements for individual agents, comparing their capabilities with those of humans, including perception and action abilities. The second part explores agents’ evolution capabilities and their implications on intelligent tools such as workflow management systems. The third part discusses societies of agents, emphasizing their collaborative and collective action capabilities, and the fourth part addresses ethical and societal aspects, including agent safety and responsibilities.
This series is intended for researchers, students, policymakers, and practitioners alike. The audience includes non-AI readers curious about AI, LLMs, and agents, as well as individuals interested in future societies where humans co-exist with AI. Readers may range from undergraduate and graduate students to researchers and industry practitioners. The series aims not only to provide answers to readers’ questions about AI and agents but also to inspire them to ask new questions. Ultimately, the hope is to motivate more people to join the endeavor in exploring this fertile research ground.
Next part: Chapter 1 – Introduction.
Article source: Liu, B., Li, X., Zhang, J., Wang, J., He, T., Hong, S., … & Wu, C. (2025). Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990. CC BY-NC-SA 4.0.
Header image: AI is Everywhere by Ariyana Ahmad & The Bigger Picture / Better Images of AI, CC BY 4.0.
