Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
Summary
Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production.
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arXiv:2607.01087v1 (cs)
\Submitted on 1 Jul 2026 (this version), _latest version 4 Jul 2026_ ( [v2)]
Title:Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
Authors:James C. Davis,Paschal C. Amusuo,Tanmay Singla,Berk Çakar,Kirsten A. Davis
View a PDF of the paper titled Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering, by James C. Davis and 4 other authors
Abstract:Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production. This shift changes the central engineering problem: not whether AI can generate useful code, but how engineers organize architectures, tools, evidence, and feedback loops so that AI-mediated development remains inspectable, correctable, and maintainable.
We study this problem through a first-person case study: a 12-week development effort in which a single expert software engineer used frontier AI coding agents to build a document accessibility remediation system. The empirical record comprises 88 contemporaneous field notes, 420 KLOC of production code, and 1.16 MLOC of tests, lints, supporting documentation, and agent tooling. From this record, we develop a candidate middle-range theory of governance conversion, expressed as a process model explaining how high-velocity agentic implementation becomes governable. The model explains how agentic implementation velocity surfaces recurring structural failure classes, and how engineering judgment sustains velocity by converting those failures into durable governance mechanisms. In contrast to existing governance models that derive controls from known obligations, governance conversion explains how controls are discovered from failures that become visible only during agentic work. We use our model to make testable predictions and to describe implications for software engineering research and practice.
| | | | --- | --- | | Comments: | 12 pages | | Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) | | Cite as: |arXiv:2607.01087[cs.SE] | | | (orarXiv:2607.01087v1[cs.SE] for this version) | | |https://doi.org/10.48550/arXiv.2607.01087<br>Focus to learn more<br>arXiv-issued DOI via DataCite |
Submission history
From: James Davis \[view email]
[v1] Wed, 1 Jul 2026 15:44:15 UTC (240 KB)
[[v2]](https://arxiv.org/abs/2607.01087v2) Sat, 4 Jul 2026 20:01:57 UTC (240 KB)
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