The use of AI in software engineering is increasing, with 67% of developers reporting that AI is helping them improve their code. However, maintaining existing code, particularly through large-scale migrations, remains a challenging task. The process can be compared to cleaning the house, being tedious and time-consuming. To address this issue, Google has published research on using AI for code migrations, while companies like Slack are exploring AI-assisted code conversion projects. A conventional approach involves using Abstract Syntax Tree (AST) transformations, but these can be difficult to execute and require significant documentation. Another challenge is the lack of context and hallucinations in Large Language Models (LLMs), which can lead to inaccurate results. An assisted LLM approach, where developers work together with AI agents, has shown promise in streamlining code migrations. This approach involves defining the migration scope, searching online for references, generating initial suggestions, refining human-in-the-loop, executing at scale, and continuously learning from feedback. By offloading bulk migration work to AI while keeping humans in control, this approach can turn a painful process into a streamlined workflow, allowing developers to focus on building rather than busywork.