Benchmarking AI agent retrieval strategies on Kubernetes bug fixes

I’ve been using AI coding agents as part of my daily engineering workflow and wanted to understand how well they actually perform on real-world bugs. To test this, I ran a series of structured experiments using bug reports from the Kubernetes repository, evaluating whether agents could produce correct, complete fixes without guidance in a large, multi-million-line codebase. My initial assumption was simple. Success would largely depend on retrieval. Whether via retrieval-augmented generation (RAG) or filesystem search, a model that finds the right code should be able to generate the right fix.