Agentifying Agentic AI

TL;DR

This paper advocates for integrating formal models from the AAMAS community to develop agentic AI systems that are autonomous, transparent, and cooperative, bridging theory and practical implementation.

Contribution

It proposes leveraging existing AAMAS tools like BDI architectures and mechanism design to enhance agentic AI with structured reasoning and coordination.

Findings

Aligns adaptive approaches with structured models for better agency

Highlights the role of communication protocols in cooperation

Suggests a path toward transparent and accountable AI systems

Abstract

Agentic AI seeks to endow systems with sustained autonomy, reasoning, and interaction capabilities. To realize this vision, its assumptions about agency must be complemented by explicit models of cognition, cooperation, and governance. This paper argues that the conceptual tools developed within the Autonomous Agents and Multi-Agent Systems (AAMAS) community, such as BDI architectures, communication protocols, mechanism design, and institutional modelling, provide precisely such a foundation. By aligning adaptive, data-driven approaches with structured models of reasoning and coordination, we outline a path toward agentic systems that are not only capable and flexible, but also transparent, cooperative, and accountable. The result is a perspective on agency that bridges formal theory and practical autonomy.