From Models to Systems: A Survey of Explainability for Tool-Augmented Language Models and AI Agents

Abstract

Large language models (LLMs) are increasingly being used as part of complex agentic systems that orchestrate the use of external tools, such as retrieval mechanisms or code interpreters. In this survey, we argue that this development necessitates a rethinking of the goals of explainable artificial intelligence (XAI): Rather than focusing on providing users with explanations for monolithic machine learning models, we need system-level explanations that also provide information about which and how tools are used, as well as how external execution traces causally influence system behavior. We provide an overview of the existing methods in explainable AI and discuss the limitations of monolithic XAI methods in agentic contexts. Finally, we highlight open challenges in providing faithful explanations for LLM-based systems.

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