Advances in Agentic AI: Back to the Future

TL;DR

This paper clarifies the concept of Agentic AI, reviews its evolution, introduces the first and second Machines in Machine Learning, and proposes a future research agenda for advancing Agentic AI systems.

Contribution

It provides a structured framework for understanding Agentic AI, defines M1 and M2, and introduces the first implementation of M2, advancing the conceptual and technical landscape.

Findings

Defined key notions of intelligence and Agentic AI.

Introduced the concepts of M1 and M2 in Machine Learning.

Presented the first realization of an M2 system.

Abstract

In light of the recent convergence between Agentic AI and our field of Algorithmization, this paper seeks to restore conceptual clarity and provide a structured analytical framework for an increasingly fragmented discourse. First, (a) it examines the contemporary landscape and proposes precise definitions for the key notions involved, ranging from intelligence to Agentic AI. Second, (b) it reviews our prior body of work to contextualize the evolution of methodologies and technological advances developed over the past decade, highlighting their interdependencies and cumulative trajectory. Third, (c) by distinguishing Machine and Learning efforts within the field of Machine Learning (d) it introduces the first Machine in Machine Learning (M1) as the underlying platform enabling today's LLM-based Agentic AI, conceptualized as an extension of B2C information-retrieval user experiences now being repurposed for B2B transformation. Building on this distinction, (e) the white paper develops the notion of the second Machine in Machine Learning (M2) as the architectural prerequisite for holistic, production-grade B2B transformation, characterizing it as Strategies-based Agentic AI and grounding its definition in the structural barriers-to-entry that such systems must overcome to be operationally viable. Further, (f) it offers conceptual and technical insight into what appears to be the first fully realized implementation of an M2. Finally, drawing on the demonstrated accuracy of the two previous decades of professional and academic experience in developing the foundational architectures of Algorithmization, (g) it outlines a forward-looking research and transformation agenda for the coming two decades.