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arXiv — NLP / Computation & Language · · 3 min read

How Consistent Are LLM Agents? Measuring Behavioral Reproducibility in Multi-Step Tool-Calling Pipelines

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Computer Science > Computation and Language

arXiv:2605.28840 (cs)
[Submitted on 23 Apr 2026]

Title:How Consistent Are LLM Agents? Measuring Behavioral Reproducibility in Multi-Step Tool-Calling Pipelines

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Abstract:Large language model (LLM) agents with tool-calling capabilities are increasingly deployed in production systems, yet a fundamental reliability question remains under-explored: does the same agent behave the same way twice? We present a systematic empirical study of behavioral consistency in multi-step tool-calling agents, measuring whether agents select the same tools, in the same order, with the same arguments, across repeated identical invocations. Unlike prior work on consistency in ReAct-style agents(search-only, free-text actions), we study the richer setting of structured tool-calling interfaces with typed parameters and consequential side effects.
Comments: 16 pages, 6 figures
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2605.28840 [cs.CL]
  (or arXiv:2605.28840v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.28840
arXiv-issued DOI via DataCite

Submission history

From: Abel Yagubyan [view email]
[v1] Thu, 23 Apr 2026 16:06:03 UTC (93 KB)
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