This paper provides a comprehensive review of Agentic AI, exploring the architectural shift from static text generation to autonomous systems that perceive, reason, plan, and act. It proposes a unified taxonomy and evaluates current practices, highlighting key challenges and future research directions for robust LLM agents.

The paper introduces a comprehensive method and ecosystem (NexAU, NexA4A, NexGAP) to overcome limitations in scaling interactive environments for training agentic Large Language Models (LLMs). This infrastructure enables the systematic generation of diverse, complex, and realistically grounded interaction trajectories for LLMs.

This paper introduces a logic programming-based framework for autonomous agents to reason about potential penalties for non-compliance with policies. It enables agents to achieve high-stakes goals by identifying optimal non-compliant plans that minimize repercussions, and also assists policymakers by simulating human decision-making under policy constraints.