Advances & challenges in foundation agents: Chapter 1 – Introduction
This article introduces Chapter 1 of a series of articles featuring Liu and colleagues’ book Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems.
Artificial Intelligence (AI) has long been driven by humanity’s ambition to create entities that mirror and transcend human intelligence. The roots of this fascination trace back to ancient myths and early engineering marvels, which illustrate humanity’s enduring dream of creating intelligent, autonomous beings.
Stories like that of Talos, the bronze automaton of Crete, describe a giant constructed by the gods to guard the island, capable of patrolling its shores and fending off intruders. Such myths symbolize the desire to imbue artificial creations with human-like agency and purpose. In ancient China, 诸葛亮 (Zhuge Liang) was said to have invented the 木牛流马 (wooden ox and flowing horse)—ingenious self-moving transport devices used for military logistics—demonstrating early imagination of autonomous, functional machines shaped by human intent. Similarly, the mechanical inventions of the Renaissance, including Leonardo da Vinci’s humanoid robot (designed to mimic human motion and anatomy) represent the first attempts to translate these myths into tangible, functional artifacts.
These early imaginings and prototypes reflect the deep-seated aspiration to bridge imagination and technology, laying the groundwork for the scientific pursuit of machine intelligence, culminating in Alan Turing’s seminal 1950 question1, “Can machines think?”. To address this, Turing proposed the Turing Test, a framework to determine whether machines could exhibit human-like intelligence through conversation, shifting focus from computation to broader notions of intelligence.
Over the decades, AI has evolved from symbolic systems reliant on predefined logic to machine learning models capable of learning from data and experience and adapting to new situations. This progression reached a new frontier with the advent of large language models (LLMs), which demonstrate remarkable abilities in understanding, reasoning, and generating human-like text2.
Central to these advancements is the concept of agent, a system that not only processes information but also perceives its environment, makes decisions, and acts autonomously. Initially a theoretical construct, the agent paradigm has become a cornerstone of modern AI, driving advancements in fields ranging from conversational assistants to embodied robotics as AI systems increasingly tackle dynamic, real-world environments.
Next part: Section 1.1 – The rise and development of AI agents.
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.
References:
- Alan M Turing. Computing machinery and intelligence. Springer, 2009. ↩
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. ↩
