From Features to Actions: Explainability in Traditional and Agentic AI Systems
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. It remains unclear how explanation approaches designed for static predictions translate to agentic settings where behaviour emerges over time. In this work, we bridge this gap by comparing attribution-based explanations with trace-based diagnostics across both settings. Our results show that while attribution methods achieve stable feature rankings in static settings (Spearman \r{ho} = 0.86), they cannot be applied reliably to diagnose execution-level failures in agentic trajectories. In contrast, trace-grounded rubric evaluation for agentic settings consistently localizes behaviour breakdowns and reveals that state tracking inconsistency is 2.7x more prevalent in failed runs and reduces success probability by 49%. These findings motivate a shift towards trajectory-level explainability for evaluating and diagnosing autonomous AI behaviour in agentic systems. Code: https://github.com/VectorInstitute/unified-xai-evaluation-framework Project page: https://vectorinstitute.github.io/unified-xai-evaluation-framework