Qodo Solutions Engineer Cole Gannaway: Using Software Graphs to Detect Cross-Repo Blast Radius in AI Code Review
In this AI Engineer podcast episode, Qodo solutions engineer Cole Gannaway discusses how the bottleneck in AI-assisted development has shifted from code generation to review. He explains how a one-line data model change can break multiple client types (web, mobile, CLI, MCP) and demonstrates how building a software graph of repositories reveals this hidden 'blast radius.' The episode also covers turning tribal knowledge and PR comments into custom review rules and why the agent that wrote the code should not grade it.
The acceleration of AI code generation has shifted the engineering bottleneck from writing to reviewing. In the latest episode of the AI Engineer podcast, Cole Gannaway, a solutions engineer at Qodo, explores the specific challenges arising from this shift. He notes that even a minor change to a data model can quietly break web, mobile, CLI, and MCP clients if its cross-repository dependencies are not fully understood.
Gannaway proposes a method of visualizing these hidden dependencies by building a software graph of repositories, thereby revealing the 'blast radius' of code changes. Additionally, he discusses transforming team tribal knowledge and past PR comments into custom review rules, as well as the principle that the agent generating the code should not be the one grading it. For engineering teams grappling with the scaling of AI-assisted development, this episode offers practical perspectives on context management and automated review.