AI Agents Represent Fundamental Shift in Software Engineering Paradigm, Not Incremental Tool Improvement
A new arXiv paper argues that AI agents—systems where large language models dynamically generate code at runtime—constitute a fundamental restructuring of software engineering rather than an incremental advancement. Historically, software has relied on human engineers writing static code with pre-encoded logic, but agentic software transfers decision-making complexity from humans to AI systems operating as the primary reasoning engine. This shift matters because it redefines the role of software engineers from code authors to 'intent architects' and raises questions about control, reliability, and the future of software development practices.
Researchers propose that the emergence of AI agents marks a paradigm shift comparable to previous transitions from licensed software to SaaS to cloud services. In traditional software, code serves as the carrier of pre-written decision logic that humans manually update; in agentic software, the agent itself becomes the software, generating and discarding code dynamically as needed. The paper formalizes this distinction and introduces 'Agentic Engineering' as a new discipline with different objects of study (agent systems rather than static code), control models (LLM-driven rather than human-predefined), and human roles. The authors analyze recent benchmarks including SWE-bench Verified and EvoClaw to demonstrate both the transformative potential and current limitations of this approach. They conclude with a roadmap toward self-evolving agent ecosystems and practical recommendations for practitioners adapting to this transition.
What's missing
The paper does not discuss potential risks or limitations of transferring decision-making complexity to AI systems, such as interpretability challenges, failure modes in critical systems, or governance frameworks needed for agent autonomy. Additionally, the practical maturity level of these systems in production environments and comparative analysis with traditional software engineering outcomes on real-world projects are not detailed in the abstract.
What different sources said
- arXiv cs.AICenter
Agentic Software: How AI Agents Are Restructuring the Software Paradigm
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