Advances & challenges in foundation agents: Section 1.4 – Navigating this series

This article is Chapter 1, Section 1.4 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.

This series is structured to provide a comprehensive, modular, and interdisciplinary examination of intelligent agents, drawing inspiration from cognitive science, neuroscience, and other disciplines to guide the next wave of advancements in AI.

While many existing surveys offer valuable insights into various aspects of agent research, Liu and colleagues’ work distinguishes itself by systematically comparing biological cognition with computational frameworks to identify synergies, gaps, and opportunities for innovation. By bridging these domains, Liu and colleagues aim to provide a unique perspective that highlights not only where agents excel but also where significant advancements are needed to unlock their full potential.

The series is divided into four key parts:

• Part I: Core components of intelligent agents introduces the core modules of agents, including the cognition module, which serves as the “brain” of the agent; the perception systems for interpreting sensory input; as well as the action systems for interacting with the external world. Within the cognition system, this part further discusses the memory, world modeling, emotion, goal, and reward systems, analyzing their current progress, limitations, and research challenges.
• Part II: Self-enhancement in intelligent agents shifts focus to the capability of agents to evolve and optimize themselves. Mechanisms like adaptive learning, self-reflection, and feedback-driven improvement are explored, inspired by the human ability to grow and refine skills over time. This part also addresses the importance of dynamic memory systems and continuous knowledge integration for agents to remain relevant and effective in changing environments.
• Part III: Collaborative and evolutionary intelligent systems examines how agents interact with each other and their environments to solve complex, large-scale problems. Multi-agent systems are discussed, highlighting their applications in fields such as robotics, medical systems, and scientific discovery. This part explores multi-agent system topologies and agent protocol, tracing the evolution of communication and collaboration from static to dynamic frameworks. Liu and colleagues align agents with human collaboration paradigms, examining how interaction patterns shape the co-evolution of intelligence and how multi-agent systems adapt their decision-making in various collaborative settings to solve complex challenges through collective intelligence.
• Part IV: Building safe and beneficial AI provides a comprehensive analysis of the security landscape for LLM-based agents. A framework categorizing threats as intrinsic or extrinsic is introduced. Intrinsic vulnerabilities arise from within the agent’s architecture: the core LLM “brain”, and the perception and action modules that enable interactions with the world. Extrinsic risks stem from the agent’s engagement with memory systems, other agents, and the broader environment. This part not only formalizes and analyzes these vulnerabilities, detailing specific attack vectors like jailbreaking and prompt injection, but also reviews a range of defense mechanisms. Moreover, future directions are explored, including super-alignment techniques and the scaling law of AI safety—the interplay between capability and risk.

By weaving together these threads, the series aims to provide a holistic perspective on the current state of intelligent agents and a forward-looking roadmap for their development. Liu and colleagues’ unique focus on integrating cognitive science insights with computational design principles positions this series as a foundational resource for those seeking to design agents that are not only powerful and efficient but also adaptive, ethical, and deeply aligned with the complexities of human society.

Next part: Introduction to Part I – Core components of intelligent 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.