Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies

TL;DR

This paper proposes Professional Agents (PAgents), a framework that leverages large language models to develop autonomous, specialized, and professional-level AI agents capable of evolving and integrating into complex domains.

Contribution

It introduces a novel tri-layered architecture for creating and evolving autonomous professional agents using large language models.

Findings

PAgents can develop continuously evolving expertise.

The framework supports controllable and specialized AI agents.

Potential to achieve artificial general intelligence.

Abstract

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.