Shrey Somaiya

Your Coding Agent Is Fast. Your Codebase Is the Bottleneck.

AI agents can write code faster than teams can land it. Jira’s frontend codebase contains over 15 million lines of code, supports thousands of contributors, and doubles in size roughly every two years.

At this scale, verification, slow CI, defense against legacy patterns had become the bottleneck. AI is great at generation of code - but design? Architechture? Data flow?

Any small change could affect a large part of the dependency graph. A small change could build, check (lint/typecheck), and test hundreds of thousands of files - even if completely unrelated to the change. Increasing the number of agent-generated pull requests increases queueing, bugs, slow feedback loops and rework without improving delivery speed.

We addressed the underlying problem: codebase coupling. We invested in a incremental static-analysis platform that parses changed files once and stores facts about their dependencies, structure, and behaviour in a shared cache. We overhauled CI tools to query this data to identify the code affected by each change. Their cost now scales with the change rather than the repository.

This platform powers affected-test selection, dependency analysis, caching, automated migrations, and architectural checks. Through an initiative called Thunderstone, we used these capabilities to restructure hundreds of thousands of files to coupling while teams continued shipping features.

This talk covers the platform’s architecture, its adoption costs, and the decisions we made, and would change! It also examines where coding agents helped with large migrations, where they failed to reason reliably about the whole repository, and why deterministic program analysis remains an essential partner in an agentic development workflow :D Thanks :)