Advances & challenges in foundation agents: Section 1.3 – Foundation agents: a modular and brain-inspired AI agent framework

This article is Chapter 1, Section 1.3 of a series of articles featuring the book Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems.

One core issue in the LLM era is the lack of a unified framework that integrates the rich cognitive and functional components required by advanced agents. While LLMs offer exceptional language reasoning capabilities, many current agent designs remain ad hoc. They incorporate modules like perception, memory, or planning in a piecemeal fashion, failing to approximate the well-coordinated specialization seen in biological systems such as the human brain. Unlike current LLM agents, the human brain seamlessly balances perception, memory, reasoning, and action through distinct yet interconnected regions, facilitating adaptive responses to complex stimuli. LLM-driven agents, by contrast, often stumble when tasks require cross-domain or multimodal integration, highlighting the need for a more holistic approach akin to the brain’s functional diversity. Motivated by these parallels, this work advocates drawing inspiration from the human brain to systematically analyze and design agent frameworks. This perspective shows that biological systems achieve general intelligence by blending specialized components (for perception, reasoning, action, etc.) in a tightly integrated fashion, an approach that could serve as a blueprint for strengthening current LLM-based agents.

Neuroscientific research reveals that the brain leverages both rational circuits (e.g., the neocortex, enabling deliberation and planning) and emotional circuits (e.g., the limbic system) to guide decision-making. Memory formation involves the hippocampus and cortical mechanisms, while reward signals, mediated by dopaminergic and other neuromodulatory pathways, reinforce behavior and learning. These biological insights inspire several design principles for AI agents, including but not limited to:

• Parallel, multi-modal processing: The brain processes visual, auditory, and other sensory inputs in parallel through specialized cortical areas, integrating them in associative regions. Similarly, AI agents benefit from parallel processing of diverse sensor streams, fusing them in later stages for coherent understanding.
• Hierarchical and distributed cognition: Reasoning, planning, emotional regulation, and motor control involve interactions between cortical and subcortical regions. Analogously, AI agents can employ modular architectures with subsystems dedicated to rational inference, emotional appraisal, and memory.
• Attention mechanisms: Human attention prioritizes sensory information based on context, goals, and emotions. AI agents can replicate this by modulating perception through learned attention policies, dynamically adjusting focus based on internal states.
• Reward and emotional integration: Emotions are not merely noise but integral to decision-making, modulating priorities, enhancing vigilance, and guiding learning. Reward-driven plasticity facilitates habit formation and skill acquisition, a concept critical to reinforcement learning in AI agents.
• Goal setting and tool usage: The human prefrontal cortex excels at setting abstract goals and planning action sequences, including tool uses. Similarly, AI agents require robust goal-management systems and adaptive action repertoires, driven by external rewards and intrinsic motivations.

These principles form the foundation of a proposed brain-inspired agent framework, where biological mechanisms serve as inspiration rather than direct replication.

Before the framework design for AI agents is formalised, let’s pause to ask a simpler question: What faculties must any truly autonomous agent possess, no matter how the modules are wired? Answering it further clarifies why each box in the forthcoming diagram is indispensable.

Next part: Section 1.3.1 – From language models to 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.