Professional Agents -- Evolving Large Language Models into Autonomous
  Experts with Human-Level Competencies
Zhixuan Chu Yan Wang Feng Zhu Lu Yu Longfei Li Jinjie Gu
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4
has catalyzed remarkable advances in natural language processing, demonstrating
human-like language fluency and reasoning capacities. This position paper
introduces the concept of Professional Agents (PAgents), an application
framework harnessing LLM capabilities to create autonomous agents with
controllable, specialized, interactive, and professional-level competencies. We
posit that PAgents can reshape professional services through continuously
developed expertise. Our proposed PAgents framework entails a tri-layered
architecture for genesis, evolution, and synergy: a base tool layer, a middle
agent layer, and a top synergy layer. This paper aims to spur discourse on
promising real-world applications of LLMs. We argue the increasing
sophistication and integration of PAgents could lead to AI systems exhibiting
professional mastery over complex domains, serving critical needs, and
potentially achieving artificial general intelligence.