用AI助教演示如何像管理经济实体一样设计和管理智能体。
Dr. AGENTONOMICS: A Didactic Experiment of AGENTONOMICS
- 将智能体视为可管理的经济主体,构建统一架构进行教学与应用。
- 原型支持问答、多模态授课、设计指导和自动生成智能体,角色可叠加。
- 适合教育者、智能体开发者及未来自主系统研究者参考。
AGENTONOMICS 是一个将人工智能代理视为可设计、管理与治理的经济实体的框架。本文介绍其首个应用——Dr. AGENTONOMICS,即在慕尼黑工业大学(TUM)AI代理商业管理课程中开发的讲座代理。该代理于2025/26学年冬季学期构思,2026年夏季学期首次面向学生推出。它既是学生学习的对象,也是教学的媒介。当前原型为基于检索的网页版导师,可解释AGENTONOMICS概念并回应学生提问。报告主张该系统可逐步扩展至三种累积性角色:多模态授课的虚拟讲师、引导学生使用代理设计与管理参考框架(ADMRF)的设计顾问,以及帮助构建学生所指定代理的元代理。这些角色共享同一界面、智能层、工具、知识库与生态连接,由协调器根据任务选择相应算法。本文展示原型架构,规划发展路线,并探讨其对多元中心化人工智能经济的影响。本报告旨在引发关于智能体如何自我教学、应用并最终复制其自身设计框架的进一步讨论。
原文摘要 · Abstract (English)
AGENTONOMICS is a framework that treats AI agents as economic entities that can be designed, managed, and governed through an integrated management architecture. Dr. AGENTONOMICS is its first application: a lecture agent developed in the context of the TUM course on AI agents in business administration. Conceived during the winter semester 2025/26 and first introduced to students in the summer semester 2026, it serves as a didactic experiment in which the agent is both the object that students study and the medium through which they learn and apply the framework. The current prototype is a web-based, retrieval-grounded tutor that explains AGENTONOMICS concepts and supports student questions. This report argues that the same system can grow beyond tutoring into three additional cumulative roles: an avatar lecturer that delivers multimodal instruction, a design consultant that guides students through the AGENTONOMICS Design & Management Reference Framework (ADMRF), and a meta-agent that helps construct the agents students have specified. These roles are cumulative because they share the same interface, intelligence layer, tools, knowledge base, and ecosystem connection, while an orchestrator selects the role-specific algorithm required for each task. We present the architecture of the prototype, outline its development roadmap, and discuss its implications for a polycentric AI economy. This report is intended to invite further discussion on how agents can teach, apply, and eventually reproduce the frameworks by which they are designed.
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