When AI Writes the Code, Who Keeps Production Running?

The rapid adoption of AI-assisted development tools like Claude Code, Cursor, and GitHub Copilot has significantly accelerated feature deployment, delighting product managers and business stakeholders but creating challenges for Site Reliability Engineers (SREs) who struggle to manage the increased volume and complexity of AI-generated code. This disparity has disrupted the traditional balance between code velocity and operational capacity, as SREs must address frequent incidents without the benefit of updated tools or a deep understanding of the AI-generated code. A 2025 report from Komodor highlights that 44% of organizations now deploy to production multiple times daily, and 43% of platform engineering teams spend over half their time on reactive troubleshooting. The complexity of modern cloud environments exacerbates these issues, with incidents often revealing a lack of code comprehension and responsibility ambiguity. Gartner predicts a significant shift towards AI SRE tooling, expecting 85% of enterprises to adopt such technologies by 2029 to manage reliability demands. These tools aim to automate the heavy lifting of incident management, allowing SREs to focus on proactive improvements, bridging the gap between development velocity and operational capacity.