Benchmarking any2bazel - towards more efficient migrations
We’re fond of Bazel, and particularly fond of the capabilities it offers developers. Faster builds, made more efficient through remote execution and caching, made more understandable through declarative configuration, queryability, and precise logging – these deliver benefits and solutions to developers once Bazel’s all set up and integrated into their daily processes.
But it’s a rare developer who enjoys spending their time and effort in build system configuration. Wouldn’t we rather be solving our customers’ problems? And the decision to migrate from an existing build system to Bazel is one that demands consideration of huge trade-offs; the labor and opportunity costs of engineering time spent on migration and adapting internal workflows to a new build system discourage the pursuit of its advantages.
In early 2026 I became interested in AI-guided Bazel migration because the capabilities of models like Opus 4.5 and agentic tools like Claude Code seemed to offer a way of realizing those benefits without so much toil – that is, if AI-guided Bazel migration worked. I believed it would work eventually, if not at that point in time, given the rapid pace of capability growth in AI models and their harnesses. I also believed it would be possible to distill lessons learned from open-source projects’ Bazel migrations and AI-guided migration attempts into a skill. If done right, such a skill could elevate those capabilities, and the process of improving the skill with better distillation from more and more migration attempts could augment the "natural" capability growth coming from the models getting smarter.
I was particularly struck by the question, "how would we know when we arrived?" It seemed answerable, if only we had an experimental laboratory that evaluated AI-guided Bazel migration attempts.
Many months later, around the time any2bazel launched, I began work on any2bazel-lab, to bring experimental results to bear on the question of how well current AI models performed at automated Bazel migration, both with and without the any2bazel skill. Anthropic’s frontier models – a mix of Opus 5, 5.5, Fable 5.1, and a bit of Haiku 4.5 – extensively contributed to the lab’s development, and coordinated the execution of its migration experiments. Our experiments are ongoing. Here’s a summary of what we’re learning, and how.