Agentifying Agentic AI
This work addresses the need for more robust and governable agentic AI systems, offering a conceptual framework that bridges theory and practice, though it appears incremental in building on existing AAMAS tools.
The paper tackles the challenge of realizing agentic AI by proposing to integrate structured models from the AAMAS community, such as BDI architectures and mechanism design, with adaptive data-driven approaches to enhance transparency, cooperation, and accountability in autonomous systems.
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.