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This paper argues that despite the rise of AI coding agents, software engineering remains indispensable because LLMs' probabilistic nature creates a structural vacuum requiring human methodologists to ensure software trustworthiness. It posits that engineers must act as custodians who reify critical knowledge levers—methodological, domain, design, and process choices—into persistent artifacts to counteract AI limitations like semantic statelessness. The work directly addresses the evolving role of professionals in an AI-assisted development landscape, making it central to discussions on AI's impact on software creation.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Why Software Engineering Is Indispensable in the Age of Coding Agents

arXiv2 min read

arXiv:2610.10226v1 Announce Type: cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? This paper argues that the rise of capable AI coding agents makes SE and software engineers essential, not obsolete: the missing foundation without which AI-assisted development produces misleadingly plausible, unverifiable, and ultimately untrustworthy software. Three structural properties of large language models (probabilistic generation, agnosticism, and semantic statelessness) create a structural vacuum that no amount of training can eliminate. Filling it requires four knowledge levers: methodological knowledge, domain knowledge, design choices, and process choices. All four must be reified as persistent artifacts, and each requires the software engineer as methodologist, mediator, and custodian.

Abstract:Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? This paper argues that the rise of capable AI coding agents makes SE and software engineers essential, not obsolete: the missing foundation without which AI-assisted development produces misleadingly plausible, unverifiable, and ultimately untrustworthy software. Three structural properties of large language models (probabilistic generation, agnosticism, and semantic statelessness) create a structural vacuum that no amount of training can eliminate. Filling it requires four knowledge levers: methodological knowledge, domain knowledge, design choices, and process choices. All four must be reified as persistent artifacts, and each requires the software engineer as methodologist, mediator, and custodian.

Accepted for publication in Communications of the ACM. 5 pages

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2610.10226 [cs.SE]

(or arXiv:2610.10226v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2610.10226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 221 words

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