AI RESEARCH
From Features to Actions: Explainability in Traditional and Agentic AI Systems
arXiv CS.AI
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ArXi:2602.06841v3 Announce Type: replace Over the last decade, explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure. Recent advances in large language models (LLMs) have enabled agentic AI systems whose behaviour unfolds over multi-step trajectories. In these settings, success and failure are determined by sequences of decisions rather than a single output.