AI Agents Are Fundamentally Restructuring the Software Paradigm

A paper submitted to arXiv on June 4, 2026, and revised June 10, 2026, argues that AI agents—systems where large language models serve as the primary reasoning engine—constitute a fundamental restructuring of software, not an incremental tool improvement. The authors formalize the distinction between traditional deterministic software and agentic software, introduce Agentic Engineering as a new paradigm, and cite benchmarks including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies to demonstrate transformative potential and current limitations. The paper proposes a four-stage roadmap toward self-evolving agent ecosystems.

Computer Science Software Engineering Submitted on 4 Jun 2026 v1 https://arxiv.org/abs/2606.05608v1 , last revised 10 Jun 2026 this version, v2 Title:Agentic Software: How AI Agents Are Restructuring the Software Paradigm View PDF /pdf/2606.05608 HTML experimental https://arxiv.org/html/2606.05608v2 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. Submission history From: Zhenfeng Cao view email /show-email/61cec8d4/2606.05608 Thu, 4 Jun 2026 02:30:06 UTC 14 KB v1 /abs/2606.05608v1 v2 Wed, 10 Jun 2026 02:11:19 UTC 15 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender 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 https://info.arxiv.org/labs/index.html .