Analyzing Bottlenecks and Efficiency Gains for AI Agents in Chip Design RTL Debugging

The author reproduced a historical RISC-V core vulnerability in the Caliptra open-source hardware root-of-trust project to evaluate how AI coding agents perform in real-world RTL debugging. Experiments showed that while agents could typically locate buggy lines of code within five minutes, reproducing and verifying fixes was exceptionally time-consuming. Key bottlenecks included internal simulation signals being hidden by default, blind polling waits caused by lengthy simulations, and environment variables being lost across shell invocations.