Where LLM Agents Fail and How They can Learn From Failures
Authors: name: Kunlun Zhu, name: Zijia Liu, name: Bingxuan Li, name: Muxin Tian, name: Yingxuan Yang, name: Jiaxun Zhang, name: Pengrui Han, name: Qipeng Xie, name: Fuyang Cui, name: Weijia Zhang, name: Xiaoteng Ma, name: Xiaodong Yu, name: Gowtham Ramesh, name: Jialian Wu, name: Zicheng Liu, name: Pan Lu, name: James Zou, name: Jiaxuan You
Organization: University of Illinois at Urbana-Champaign

Large Language Model (LLM) agents, which integrate planning, memory,
reflection, and tool-use modules, have shown promise in solving complex,
multi-step tasks. Yet their sophisticated architectures amplify vulnerability
to cascading failures, where a single root-cause error propagates through
subsequent decisions, leading to task failure. Current systems lack a framework
that can comprehensively understand agent error in a modular and systemic way,
and therefore fail to detect these errors accordingly. We address this gap with
three contributions. First, we introduce the AgentErrorTaxonomy, a modular
classification of failure modes spanning memory, reflection, planning, action,
and system-level operations. Second, we construct AgentErrorBench, the first
dataset of systematically annotated failure trajectories from ALFWorld, GAIA,
and WebShop, grounding error analysis in real-world agent rollouts. Third, we
propose AgentDebug, a debugging framework that isolates root-cause failures and
provides corrective feedback, enabling agents to recover and iteratively
improve. Experiments on AgentErrorBench show that AgentDebug achieves 24%
higher all-correct accuracy and 17% higher step accuracy compared to the
strongest baseline. Beyond detection, the targeted feedback generated by
AgentDebug enables LLM agents to iteratively recover from failures, yielding up
to 26% relative improvements in task success across ALFWorld, GAIA, and
WebShop. These results establish principled debugging as a pathway to more
reliable and adaptive LLM agents. The code and data will be available at
https://github.com/ulab-uiuc/AgentDebug