Agentifying Agentic AI

2025-11-21

AI Summary

This study addresses the dual challenges of advancing autonomy, reasoning, and interactive capabilities—while ensuring trustworthiness—in agentic AI systems. Methodologically, it introduces a novel paradigm integrating data-driven learning with structured cognitive modeling, marking the first systematic incorporation of BDI cognitive architectures, multi-agent communication protocols, mechanism design, and institutional modeling from the AAMAS community, augmented with adaptive learning and collaborative reasoning mechanisms. The core contribution is a principled agent framework that balances flexibility, interpretability, and socio-technical embeddability: formal theoretical foundations ensure transparency and accountability of autonomous behavior, while dynamic collaboration mechanisms enable trustworthy human–agent and multi-agent interaction. This framework establishes a unified theoretical pathway and scalable practical foundation for developing sustainable, comprehensible, and governable next-generation autonomous systems.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Developing autonomous AI with reasoning and interaction capabilities
• Integrating cognitive models and governance for agentic systems
• Bridging formal theory with practical cooperative autonomy

Innovation

• BDI architectures for cognitive modeling
• Communication protocols enabling agent interaction
• Mechanism design ensuring cooperative governance