Human-Centered Explainable AI (HCXAI): Re-examining Explainability in the Era of Agentic AI

Making AI explainable requires more than algorithmic transparency: it demands understanding who needs explanations and why. In our sixth CHI workshop on Human-Centered XAI (HCXAI), we shift focus to agentic AI systems. LLM-based agents foundationally challenge existing explainability paradigms. Unlike traditional AI that produces single outputs, agents plan multi-step strategies, invoke tools with real-world consequences, and coordinate with other systems; yet current XAI approaches fail to address these complexities. Users need to understand not just what an agent might do, but the cascade of actions it could trigger, the risks involved, and why responses take time. Even our expanded HCXAI frameworks struggle with these new demands. Through our workshop series, we have built a strong community making important conceptual, methodological, and technical impact. This year, we re-examine what human-centered explainable AI means in the agentic era, bringing together researchers and practitioners to shape explainability for both users and developers of these systems.