Automated Code Remediation at Scale, 2nd Edition

Chapter 4. The Role of AI Agents in Mass-Scale Auto-Remediation

The rise of AI-assisted development has opened up new possibilities in software creation. Developers now have tools that can generate code faster, explore frameworks more easily, and accelerate feature delivery. But with this increased velocity comes an equally fast-growing burden of maintenance. Every line of code, every dependency, every library introduced today becomes a long-term responsibility—one that must be kept secure, compliant, and compatible.

For enterprises managing thousands of repositories, many of which are deeply interconnected through shared libraries and legacy integrations, the challenge isn’t just about writing code—it’s about maintaining and evolving code at scale. When a security vulnerability arises or a framework update is required, it’s no longer sufficient to address one repository at a time. Organizations must be able to analyze, refactor, and validate changes across their entire codebase, all while ensuring consistency and minimizing disruption.

The next evolution in AI isn’t about writing more code—it’s about making existing code better, intelligently and at scale.

This chapter explores how AI agents, when paired with deterministic tools and structured code data, can deliver safe, reliable, and scalable code remediation. For this discussion, we’ll be referencing how the Moderne AI agent Moddy works to remediate at scale. We’ll examine how AI shifts from suggestion engines to orchestrators ...