---
source: arxiv
url: http://arxiv.org/abs/2604.10599v1
published_at: '2026-04-12T12:08:53'
authors:
- Mamdouh Alenezi
topics:
- agentic-ai
- software-engineering
- multi-agent-orchestration
- code-verification
- human-ai-collaboration
relevance_score: 0.95
run_id: materialize-outputs
language_code: en
---

# Rethinking Software Engineering for Agentic AI Systems

## Summary
This paper argues that software engineering should shift from manual code writing toward orchestrating AI agents, verifying AI-generated outputs, and keeping humans accountable for decisions. It is a structured literature review and position paper, not a new algorithm or benchmark study.

## Problem
- LLMs and agentic systems can generate large amounts of working code, so manual code authorship is no longer the main bottleneck in many workflows.
- That creates a new bottleneck: checking whether AI-generated code is correct, secure, maintainable, and aligned with requirements.
- If software engineering practice, education, and governance stay centered on writing code, they will mismatch how AI-assisted development actually works.

## Approach
- The paper conducts a structured multivocal literature review of **23** recent sources, including peer-reviewed studies, preprints, and expert surveys.
- It organizes the evidence into four themes: engineer role changes, verification challenges, empirical effects on productivity and quality, and implications for education and practice.
- From that synthesis, it proposes a conceptual model of software engineering built around four competencies: **intent articulation, systematic verification, multi-agent orchestration, and accountable human judgment**.
- It also outlines changes to curricula, tools, lifecycle processes, and professional governance so teams can operate in a verification-first, human-in-the-loop development model.

## Results
- The main claim is conceptual: software engineering should be reorganized around orchestration, verification, and human-AI collaboration because code generation is becoming abundant while semantic validation remains hard.
- The paper itself does **not** report new experimental results or a new benchmark. Its evidence comes from prior studies.
- Cited evidence includes a controlled experiment with **151 professional developers** where AI assistance reduced task completion time by a **30.7% median** in the initial phase, but later maintenance showed **no significant difference** in completion time or code quality versus control.
- A cited evaluation found that systems with similar success rates could differ by **53×** in cost and by tool-call counts of **917 vs. 3**, which the authors use to argue that orchestration quality matters.
- A cited human-AI collaboration study reports that humans or AI alone solved complex tasks at **0.67% to 18.89%** success, while collaboration reached **31.11%**.
- Based on this synthesis, the paper claims the durable role of engineers shifts upward toward system design, verification infrastructure, risk ownership, and oversight of multi-agent workflows.

## Link
- [http://arxiv.org/abs/2604.10599v1](http://arxiv.org/abs/2604.10599v1)