Classic Theory Meets Agent Practice

Series: Classic Theory Meets Agent Practice (Part 3) Part 1: Dual-Pass Review: Why You Can’t Have Both Recall and Precision · Part 2: Strategy Genes: Pruning Review Prompts with Genetic Algorithm Thinking TL;DR: Two controlled experiments. Code review dimensions went from 8 to 11, and known-issue detection went from 1/6 to 6/6. Design review introduced axiomatic design dimensions, and detection also went from 1/6 to 6/6. But the version with a math formula proved that more dimensions are not always better — computation consumed review attention, and findings dropped 35%. Run controlled experiments with known issues as reference, and you learn which dimensions actually work. ...

Series: Classic Theory Meets Agent Practice (Part 2) Previous: Dual-Pass Review: Why Recall and Precision Cannot Both Win TL;DR: A review prompt went from 317 lines to 135 lines (-58%), and review quality improved by 29%. What I removed was not useful procedure, but redundant content the model could infer on its own. What stayed were strategy genes: irreplaceable constraints, negative examples, and tone locks. The previous post covered dual-pass review: splitting one review agent into a “find everything” pass and a “filter hard” pass. Valid find rate went from 75% to 92%. But it left one problem open: what the “find everything” pass chooses to report or ignore is still affected by prompt wording. ...

Series: Classic Theory Meets Agent Practice (Part 1) TL;DR: A design review agent needs to find every issue AND avoid false positives. One agent can’t do both. Borrowing cascade retrieval from information retrieval — a 15-year-old method — I split it into two: a Recall Pass that casts a wide net, and a Precision Pass that filters strictly. Real defects get caught earlier, and the risk of rework during development drops. ...