翻訳待ち：AI Agents Are Fundamentally Restructuring the Software Paradigm

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 4 Jun 2026 (v1), last revised 10 Jun 2026 (this version, v2)] Title:Agentic Software: How AI Agents Are Restructuring the Software Paradigm View a PDF of the paper titled Agentic Software: How AI Agents Are Restructuring the Software Paradigm, by Zhenfeng Cao View PDF HTML (experimental) Abstract:For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve. This paper argues that the emergence of AI agents -- systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource -- constitutes a fundamental restructuring of what software is, not an incremental tool improvement. We formalize the distinction between traditional deterministic software and agentic software: in the former, code is the carrier of pre-written decision logic; in the latter, the agent itself is the software, and its decision logic is generated at runtime. We trace the historical arc from licensed software to SaaS to Agent-as-a-Service (AaaS), showing that each shift transferred additional complexity away from end-users -- with the agentic shift transferring not just operational complexity but decision-making complexity itself. We introduce Agentic Engineering as an expansion of the software engineering discipline into a new paradigm, distinct in its core object of study (agent systems rather than static source code), its control model (LLM-driven rather than human-predefined), and its human role (intent architect rather than code author). Through analysis of recent benchmark evidence including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies, we demonstrate both the transformative potential of the agentic paradigm and its current limitations. We conclude with a four-stage roadmap toward self-evolving agent ecosystems and concrete recommendations for practitioners navigating this transition. Comments: 15 pages, 2 figures, and 3 tables Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) ACM classes: D.2.0; I.2.11; I.2.7 Cite as: arXiv:2606.05608 [cs.SE] (or arXiv:2606.05608v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2606.05608 arXiv-issued DOI via DataCite Submission history From: Zhenfeng Cao [view email] [v1] Thu, 4 Jun 2026 02:30:06 UTC (14 KB) [v2] Wed, 10 Jun 2026 02:11:19 UTC (15 KB) Full-text links: Access Paper: View a PDF of the paper titled Agentic Software: How AI Agents Are Restructuring the Software Paradigm, by Zhenfeng Cao View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE new | recent | 2026-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)