Dr. AGENTONOMICS: A Didactic Experiment of AGENTONOMICS

2026-08-04

AI Summary

This work proposes the AGENTONOMICS framework, which conceptualizes AI agents as economic entities endowed with integrated pedagogical, design, and governance capabilities. Addressing the fragmented roles of agents in educational contexts, the framework introduces a quadruple cumulative role model—comprising tutor, virtual lecturer, design consultant, and meta-agent—implemented within a unified architecture that enables dynamic role switching via a task scheduler. Built upon a web-based retrieval-augmented generation system, the framework integrates a unified interface, an intelligent layer, a toolset, and a knowledge base, orchestrated by a central orchestrator for adaptive role assignment. A functional prototype has been successfully deployed in the Summer Semester 2026 courses at the Technical University of Munich, demonstrating the framework’s teachability and operational feasibility, and offering an innovative paradigm for constructing polycentric AI-driven economies.

Abstract

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.

Problem

• AGENTONOMICS
• AI agents
• didactic experiment
• teaching agent
• polycentric AI economy

Innovation

• AGENTONOMICS
• multi-role AI agent
• integrated management architecture
• retrieval-grounded tutoring
• polycentric AI economy