From Features to Actions: Explainability in Traditional and Agentic AI Systems

Researchers and AI developers published a novel perspective on artificial intelligence interpretability on the arXiv preprint server on February 12, 2025, to address the critical shift from traditional feature-based explanations to action-oriented accountability in agentic systems. As large language models (LLMs) increasingly power autonomous agents capable of complex reasoning, the paper argues that the industry's historical reliance on post-hoc explanations for single predictions is no longer sufficient for systems that operate through multi-step trajectories and sequential decision-making. This transition is essential because the performance of modern AI agents is defined by long-form behavior rather than static inputs and outputs. The report highlights that for the past decade, field of Explainable AI (XAI) has been dominated by methods that justify specific, isolated model outputs by highlighting which input features influenced the result. This 'fixed decision structure' worked well for classification tasks or simple predictions but fails to capture the logic behind an autonomous agent's evolving strategy. As agents take on more significant roles in software engineering, scientific research, and customer service, the ability to trace a sequence of choices is becoming a regulatory and safety necessity. According to the abstract, the shift toward agentic AI introduces a dynamic where success or failure is cumulative. Unlike traditional models where an error is tied to a single data point, an agentic system might fail due to a logical misstep early in a multi-stage process that compounds over time. The researchers propose that interpretability must now focus on 'actions' and 'trajectories,' providing a framework that allows human supervisors to understand not just what a model predicted, but why it chose a specific path of action across a complex timeline.