Study Finds Static Skill Inconsistencies Do Not Predict LLM Agent Runtime Failures

LLM agent frameworks depend on external skill assets to standardize tool usage, define workflows, and provide executable resources. These multi‑component skill packages often exhibit cross‑artifact inconsistencies, where documentation, scripts, and configuration files conflict with one another. Static auditing can reveal such contractual mismatches, but the sheer number of static defects does not reliably predict how agents will behave at runtime. The authors therefore treat static defect volume as an early‑stage diagnostic rather than a definitive risk metric. They argue that runtime evaluation remains essential for accurate assessment of agent performance.

To quantify the prevalence of inconsistencies, the researchers audited 575 community and benchmark skill packages. Human reviewers manually validated each identified issue, confirming a total of 1,182 contract inconsistencies across the sample. The audit’s manual validation ensured high confidence in the identified mismatches. This large‑scale audit provides a comprehensive view of the static defect landscape in current LLM agent ecosystems. Nevertheless, the authors caution that these static numbers alone cannot serve as a reliable risk indicator.

For runtime impact assessment, the team built a controlled evaluation pipeline that runs 49 executable agent tasks. Three representative large language models were executed under three skill conditions: NoSkill (no external skill), OriginalSkill (the unmodified skill with inconsistencies), and FixedSkill (the skill after defects were repaired). Each task was run with every LLM across all three conditions, generating a comprehensive dataset of agent executions. The experimental design produced 441 paired execution trajectories, pairing each original‑skill run with its fixed‑skill counterpart. These paired trajectories allowed direct comparison of agent behavior before and after defect resolution.

Fine‑grained behavioral analysis of the 441 paired trajectories quantified the performance differences attributable to the corrected inconsistencies. The analysis quantified how fixing defects changed agent performance across the evaluated tasks. The quantified impacts provide empirical evidence of the relationship between static defects and runtime behavior. The study therefore concludes that static defect counts are a poor predictor of runtime degradation. The findings highlight the limited predictive power of static defect counts for runtime performance.

Overall, the research highlights that static skill mismatches do not necessarily translate into runtime failures, emphasizing the value of dynamic evaluation for LLM agents. The study’s results can inform builder strategies for evaluating LLM agents with a balanced focus on both static and runtime testing.