Agentic Workflows With Tool Use: Failure Taxonomy From a Year of Production Traces

Authors

  • Ondrej Skovranek Department of Machine Learning Engineering, Zahorie Institute of Applied Artificial Intelligence, Malacky, Slovakia Author
  • Meredith Ockendon Department of Machine Learning Engineering, Orkney Institute of Data Science, Kirkwall, United Kingdom Author
  • Roxana Constantinescu Department of Software Engineering, Baragan Centre for Machine Intelligence, Slobozia, Romania Author

DOI:

https://doi.org/10.5281/zenodo.22660726

Keywords:

Agentic Workflows With Tool Use; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Agentic Workflows With Tool Use is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for agentic workflows with tool use,
spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged and
where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude that
selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or avoidance.

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Hendriksen W, Wojcik H, et al. "Agentic Workflows With Tool Use: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.3389/fdgth.2026.1834800

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Jankowiak G, Grabowski J, et al. "Agentic Workflows With Tool Use: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.7759/cureus.46454

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Published

2024-07-13

Issue

Vol. 3 No. 2 (2024): vol 3 issue 2 2024

Section

Articles

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