From Symbols to Synapses: The Reemergence of Agency in the Large Language Model Era

Building intelligent agents capable for autonomously perceiving, reasoning, and acting to achieve goals has been a central pursuit of artificial intelligence (AI) since its inception. For decades, the notion of agency was dominated by symbolic architectures that represent information as formal knowledge and use deliberative reasoning to derive rational actions, offering reliability but at the cost of brittleness and limited generalizability. The recent advancements in large language models (LLMs) and their integration into tool-using, environment-interacting “agentic” systems have reignited interest in AI agents. However, while LLM-based agents provide the flexibility that symbolic systems lacked, they introduce new challenges in reliability and control. We posit that the future of AI agents lies not in indefinitely scaling the model size, but in synthesizing the methods and theories developed by the autonomous agents and multiagent systems community with modern neural architectures to create neuro-symbolic agents capable of trustworthy autonomy.