TUM
AGENTONOMICS
AI-Agents in Economics

TUM · Chair of Robotics, AI & Real-time Systems

AGENTONOMICS

The Economic Framework for Autonomous AI Agents

AGENTONOMICS is a management and governance framework developed at TUM that defines how autonomous AI agents are designed, operated, and governed as economic actors in organizations and markets. It is the foundation of the TUM lecture "AI-Agents in Economics".

Key Principle

AI agents are becoming economic subjects.

AGENTONOMICS provides the framework to design and govern them systematically.

Explore the FrameworkDownload Whitepaper ↓

AI Chatbot for the Lecture

Docent Dr. AGENTONOMICS is an AI assistant trained on the lecture materials. Ask questions about the AGENTONOMICS framework, exam content, or concepts from the course.

  • The 6 ADMRF modules and how they relate
  • AI agents as autonomous economic subjects
  • Polycentric AI networks vs. centralized AGI
  • PuK-Instrument and economic performance measurement
  • Organizational transformation through AI agents
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Core Framework

The ADMRF — AGENTONOMICS Development & Management Reference Framework

Defines the complete lifecycle of an AI agent from conceptual definition to economic operation and governance.

AGENTONOMICS Development and Management Reference Framework (ADMRF)
Source: AGENTONOMICS Whitepaper
Agent Definition Frame
Identity, autonomy, capabilities, and governance parameters of the AI agent.
Technical Building Blocks
Architecture, models, interfaces, memory, and tool integrations.
Network Intermediary
Governance and coordination within multi-agent networks.
Management Concept (AI-CEO)
Strategic and operational management logic for AI-driven units.
Economic Planning & Control
Financial modeling, KPIs, and performance measurement (PuK).
Stakeholder Communication
Interaction with investors, partners, users, and markets.

TUM Lecture

AI-Agents in Economics

CIT633000

The lecture addresses how AI agents — acting not merely as tools but as autonomous, adaptive, and goal-directed economic actors — require new management models and governance frameworks.

Students learn to conceptualize, design, implement, and economically govern AI agents across the full ADMRF lifecycle — bridging technical architecture with business management.

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What the lecture covers

  • AI agents as autonomous economic subjects — beyond technical tools
  • Historical emergence: from information to AI capabilities economy
  • The ADMRF: 6 management modules from Definition Frame to Pitch-Tool
  • Polycentric AI networks as alternative to centralized AGI
  • Technical building blocks: algorithm, interface, model, tools, ecosystem
  • Organizational transformation through AI agent networks

Applications

What AGENTONOMICS enables

Organizational Transformation
Redesigning traditional organizations into AI agent network organizations.
Polycentric AI Networks
Building decentralized agent ecosystems as alternative to centralized AGI.
AI-CEO Management
Autonomous business unit management — from strategic vision to operational control.
AI Growth Potential
Identifying and realizing AI-based growth opportunities within existing organizations.
Data Trusteeship & Intermediary
Designing governance for data-sharing and agent coordination in networks.
Agent-based Ventures
Building new business models where AI agents operate as economic actors.

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