aiqbench analysis
This paper posits that AI agents represent a fundamental paradigm shift where code transitions from a static carrier of logic to ephemeral tooling within an LLM-driven reasoning loop. By analyzing benchmarks like SWE-bench Verified and EvoClaw, the authors argue that this evolution necessitates a new discipline, "Agentic Engineering," distinct from traditional software engineering in its control models and human roles. The work outlines a roadmap toward self-evolving agent ecosystems, suggesting that the industry is moving from transferring complexity to users (as seen in the SaaS era) to a new phase of Agent-as-a-Service.
The End of Software Engineering: How AI Agents Are Fundamentally Restructuring the Software Paradigm
arXiv:2606.05608v1 Announce Type: cross 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 not an incremental improvement but a fundamental restructuring of the software paradigm. Drawing on first-principles analysis of complexity scaling, we formalize the distinction between traditional software (where code is the carrier of decision logic) and agentic systems (where code is ephemeral tooling for an LLM-driven reasoning loop). We trace the historical arc from licensed software to SaaS to what we term Agent-as-a-Service (AaaS), showing that each shift transferred additional complexity away from end-users. We introduce the concept of Agentic Engineering as an emergent discipline -- distinct from software engineering in its core object of study, control model, and human role. 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.
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 not an incremental improvement but a fundamental restructuring of the software paradigm. Drawing on first-principles analysis of complexity scaling, we formalize the distinction between traditional software (where code is the carrier of decision logic) and agentic systems (where code is ephemeral tooling for an LLM-driven reasoning loop). We trace the historical arc from licensed software to SaaS to what we term Agent-as-a-Service (AaaS), showing that each shift transferred additional complexity away from end-users. We introduce the concept of Agentic Engineering as an emergent discipline -- distinct from software engineering in its core object of study, control model, and human role. 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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
D.2.0; I.2.11; I.2.7
arXiv:2606.05608 [cs.SE]
(or arXiv:2606.05608v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2606.05608 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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