# Rethinking Software Engineering for Agentic AI Systems

arXiv: https://arxiv.org/abs/2604.10599 · HTML: https://mlsanity.com/paper/2604.10599/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Built on by (most cited first, 4 of 4)

- [Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development](https://mlsanity.com/paper/2605.20456/) (2026)
- [Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domains](https://mlsanity.com/paper/2606.22484/) (2026)
- [Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing](https://mlsanity.com/paper/2606.08806/) (2026)
- [The Work Behind Delegation: A Framework for Supervising AI Coding Agents](https://mlsanity.com/paper/2609.24234/) (2026)

## Abstract (arXiv)

The rapid proliferation of large language models (LLMs) and agentic AI systems has created an unprecedented abundance of automatically generated code, challenging the traditional software engineering paradigm centered on manual authorship. This paper examines whether the discipline should be reoriented around orchestration, verification, and human-AI collaboration, and what implications this shift holds for education, tools, processes, and professional practice. Drawing on a structured synthesis of relevant literature and emerging industry perspectives, we analyze four key dimensions: the evolving role of the engineer in agentic workflows, verification as a critical quality bottleneck, observed impacts on productivity and maintainability, and broader implications for the discipline. Our analysis indicates that code is transitioning from a scarce, carefully crafted artifact to an abundant and increasingly disposable commodity. As a result, software engineering must reorganize around three core competencies: effective orchestration of multi-agent systems, rigorous verification of AI-generated outputs, and structured human-AI collaboration. We propose a conceptual framework outlining the transformations required across curricula, development tooling, lifecycle processes, and governance models. Rather than diminishing the role of engineers, this shift elevates their responsibilities toward system-level design, semantic validation, and accountable oversight. The paper concludes by highlighting key research challenges, including verification-first lifecycles, prompt traceability, and the long-term evolution of the engineering workforce.