@inproceedings{zhao-etal-2026-agenttac,
title = "{A}gent{TAC}: Mining Temporal Action-Context Error Patterns from Agent Traces",
author = "Zhao, Chenchen and
Chen, Muxi and
Shu, Jianhai and
Zhang, Zhaohua and
Jiang, Wenyu and
Jiang, Tianwen and
Deng, Hexuan and
Zhu, Sa and
Yang, Haolin and
Zhu, Geyuan and
Yang, Cheng and
Xiao, Qiuyong and
Zhuo, Jianhuan and
Zhang, Jihong and
Xu, Qiang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1637/",
pages = "37202--37222",
ISBN = "979-8-89176-459-0",
abstract = "Interactive agents increasingly fail on long task processes involving user turns, tool calls, and evolving environment contexts. Existing agent debugging methods provide useful trace-level diagnosis, but generally return explanations for individual failed traces rather than recurring, fine-grained action-context patterns across runs. To offer such corpus-level analyses, we present AgentTAC (Temporal Action-Context), an automated framework for mining erroneous and generalizable temporal sequences of action and context properties from agent traces. AgentTAC first localizes all distinct errors in each involved agent run. Then, it constructs and maintains a dynamic pool of attributes over the involved tasks, actions, and contexts to summarize their intrinsic properties and contextual relationships. Finally, it extracts temporally contiguous attribute patterns anchored at the localized errors. The resulting error patterns describe failures as interpretable sequential combinations of context and actions, such as making decisions under a missing-constraint context or taking an action after an incompatible prior decision. Extensive evaluations of AgentTAC on multiple benchmarks and agents validate its capabilities in error localization and error pattern mining, and provide preliminary evidence for error-pattern-driven test-time repair."
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<abstract>Interactive agents increasingly fail on long task processes involving user turns, tool calls, and evolving environment contexts. Existing agent debugging methods provide useful trace-level diagnosis, but generally return explanations for individual failed traces rather than recurring, fine-grained action-context patterns across runs. To offer such corpus-level analyses, we present AgentTAC (Temporal Action-Context), an automated framework for mining erroneous and generalizable temporal sequences of action and context properties from agent traces. AgentTAC first localizes all distinct errors in each involved agent run. Then, it constructs and maintains a dynamic pool of attributes over the involved tasks, actions, and contexts to summarize their intrinsic properties and contextual relationships. Finally, it extracts temporally contiguous attribute patterns anchored at the localized errors. The resulting error patterns describe failures as interpretable sequential combinations of context and actions, such as making decisions under a missing-constraint context or taking an action after an incompatible prior decision. Extensive evaluations of AgentTAC on multiple benchmarks and agents validate its capabilities in error localization and error pattern mining, and provide preliminary evidence for error-pattern-driven test-time repair.</abstract>
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%0 Conference Proceedings
%T AgentTAC: Mining Temporal Action-Context Error Patterns from Agent Traces
%A Zhao, Chenchen
%A Chen, Muxi
%A Shu, Jianhai
%A Zhang, Zhaohua
%A Jiang, Wenyu
%A Jiang, Tianwen
%A Deng, Hexuan
%A Zhu, Sa
%A Yang, Haolin
%A Zhu, Geyuan
%A Yang, Cheng
%A Xiao, Qiuyong
%A Zhuo, Jianhuan
%A Zhang, Jihong
%A Xu, Qiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhao-etal-2026-agenttac
%X Interactive agents increasingly fail on long task processes involving user turns, tool calls, and evolving environment contexts. Existing agent debugging methods provide useful trace-level diagnosis, but generally return explanations for individual failed traces rather than recurring, fine-grained action-context patterns across runs. To offer such corpus-level analyses, we present AgentTAC (Temporal Action-Context), an automated framework for mining erroneous and generalizable temporal sequences of action and context properties from agent traces. AgentTAC first localizes all distinct errors in each involved agent run. Then, it constructs and maintains a dynamic pool of attributes over the involved tasks, actions, and contexts to summarize their intrinsic properties and contextual relationships. Finally, it extracts temporally contiguous attribute patterns anchored at the localized errors. The resulting error patterns describe failures as interpretable sequential combinations of context and actions, such as making decisions under a missing-constraint context or taking an action after an incompatible prior decision. Extensive evaluations of AgentTAC on multiple benchmarks and agents validate its capabilities in error localization and error pattern mining, and provide preliminary evidence for error-pattern-driven test-time repair.
%U https://aclanthology.org/2026.emnlp-main.1637/
%P 37202-37222
Markdown (Informal)
[AgentTAC: Mining Temporal Action-Context Error Patterns from Agent Traces](https://aclanthology.org/2026.emnlp-main.1637/) (Zhao et al., EMNLP 2026)
ACL
- Chenchen Zhao, Muxi Chen, Jianhai Shu, Zhaohua Zhang, Wenyu Jiang, Tianwen Jiang, Hexuan Deng, Sa Zhu, Haolin Yang, Geyuan Zhu, Cheng Yang, Qiuyong Xiao, Jianhuan Zhuo, Jihong Zhang, and Qiang Xu. 2026. AgentTAC: Mining Temporal Action-Context Error Patterns from Agent Traces. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37202–37222, Budapest, Hungary. Association for Computational Linguistics.