AI-driven Software Development: A Pragmatic Path to Agentic Development Processes

2026-05-20 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Advanced, extended

Summary

A pragmatic organizing framework is presented for transitioning to AI-driven software development, detailing a progression from informal, assistive AI use through integrated AI workflows to controlled agentic development processes. This framework emphasizes embedding AI technically, organizationally, and via quality assurance across the entire software development lifecycle, encompassing requirements, architecture, implementation, testing, review, operations, and maintenance. A central concept is the "harness," which connects project context, tool access, verification mechanisms, permissions, logging, and human approval. The framework is substantiated by current research, practice-oriented sources, established software engineering practices, and an exploratory case study of a mid-sized software company. This case study assesses the framework's plausibility, outlining how technical prerequisites, governance, design practices, and transformation paths can be shaped within a concrete organizational context.

Key takeaway

For Directors of AI/ML or Software Engineering Managers aiming to integrate generative AI, you should adopt a structured, phased transformation. Prioritize building a robust "harness" that connects project context, verification, and human approval across the SDLC. This ensures controlled adoption, mitigates risks from AI-generated artifacts, and shifts focus from isolated tool use to systematic process embedding, ultimately enabling measurable productivity gains and improved quality.

Key insights

Systematic AI integration across the SDLC requires a "harness" for controlled, agentic development.

Principles

• AI value emerges from process embedding, not isolated tools.
• AI-generated artifacts demand robust control and governance.
• Human oversight is critical for complex or safety-critical tasks.

Method

Transition from AI-assisted to AI-integrated, then AI-driven development, by building a "harness" connecting context, tools, verification, and governance.

In practice

• Define clear AI usage guidelines, training, and privacy reviews.
• Implement automated tests, static analysis, and security scans as quality gates.

Topics

• AI-driven Software Development
• Generative AI
• Software Development Lifecycle
• AI Agents
• Harness Engineering
• Human-AI Collaboration

Best for: CTO, VP of Engineering/Data, AI Architect, Software Engineer, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.