Where LLM Agents Fail and How They can Learn From Failures
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
68dc7b334159d1f2418f99ce
Kunlun Zhu
68dc7b334159d1f2418f99cf
Zijia Liu
claimed_verified
2025-10-02T03:09:02.399Z
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Zijia Liu
user
m-serious
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Bingxuan Li
68dc7b334159d1f2418f99d1
Muxin Tian
68dc7b334159d1f2418f99d2
Yingxuan Yang
68dc7b334159d1f2418f99d3
Jiaxun Zhang
68dc7b334159d1f2418f99d4
Pengrui Han
68dc7b334159d1f2418f99d5
Qipeng Xie
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Fuyang Cui
claimed_verified
2025-10-02T13:56:26.285Z
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Fuyang Cui
user
scottcfy
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Weijia Zhang
68dc7b334159d1f2418f99d8
Xiaoteng Ma
68dc7b334159d1f2418f99d9
Xiaodong Yu
68dc7b334159d1f2418f99da
Gowtham Ramesh
68dc7b334159d1f2418f99db
Jialian Wu
68dc7b334159d1f2418f99dc
Zicheng Liu
68dc7b334159d1f2418f99dd
Pan Lu
claimed_verified
2025-10-09T06:52:22.788Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/60f5f68fa7fd83d025749234/gCeJAZfzaANAcEvI6v5-P.jpeg
Pan Lu
user
lupantech
68dc7b334159d1f2418f99de
James Zou
68dc7b334159d1f2418f99df
Jiaxuan You
Where LLM Agents Fail and How They can Learn From Failures
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
A modular taxonomy and debugging framework improve error detection and recovery in large language model agents, enhancing task success.
AgentErrorTaxonomy AgentErrorBench AgentDebug failure modes memory reflection planning action system-level operations ALFWorld GAIA WebShop all-correct accuracy step accuracy task success