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This research introduces AutoTraceGT, a scalable pipeline that applies grounded theory to analyze agent trajectories at scale, addressing the limitations of pre-built classifiers in long or unfamiliar tasks. By automating open, axial, and theoretical coding until saturation, the method generates behavioral taxonomies that recover high percentages of known failure modes while surfacing novel patterns missed by human annotations. The resulting codebooks serve as effective deductive feature spaces that outperform zero-shot and few-shot LLM baselines for predicting agent failures.
Using Grounded Theory for Agent Behavior Analysis at Scale
arXiv:2608.30391v1 Announce Type: cross Abstract: Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, often unfamiliar tasks where pre-built classifiers fall short. We propose to bring grounded theory into agent trajectory analysis: a six-decade-old qualitative method from the social sciences, with a principled saturation criterion and an auditable trail from data to theory. We propose AutoTraceGT (Automated Trace analysis through Grounded Theory), the first multi-agent pipeline that automates grounded theory on agent trajectories. It iteratively performs open, axial, and theoretical coding until saturation, producing a behavioral taxonomy tailored to each task. Across six trajectory corpora, AutoTraceGT produces codebooks that recover 73-91 percent of the failure modes in human-annotated taxonomies and surface additional patterns that those taxonomies miss. The emergent theoretical narrative aligns with prior expert accounts. Used as a deductive feature space, the codebook outperforms zero-shot and few-shot LLM baselines on downstream failure prediction. These results suggest Grounded Theory offers a scalable analytic tool for ML researchers and agent developers studying what agents actually do.
Abstract:Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, often unfamiliar tasks where pre-built classifiers fall short. We propose to bring grounded theory into agent trajectory analysis: a six-decade-old qualitative method from the social sciences, with a principled saturation criterion and an auditable trail from data to theory. We propose AutoTraceGT (Automated Trace analysis through Grounded Theory), the first multi-agent pipeline that automates grounded theory on agent trajectories. It iteratively performs open, axial, and theoretical coding until saturation, producing a behavioral taxonomy tailored to each task. Across six trajectory corpora, AutoTraceGT produces codebooks that recover 73-91 percent of the failure modes in human-annotated taxonomies and surface additional patterns that those taxonomies miss. The emergent theoretical narrative aligns with prior expert accounts. Used as a deductive feature space, the codebook outperforms zero-shot and few-shot LLM baselines on downstream failure prediction. These results suggest Grounded Theory offers a scalable analytic tool for ML researchers and agent developers studying what agents actually do.
33 pages. Accepted to the Findings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.30391 [cs.CL]
(or arXiv:2608.30391v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.30391 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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