A practitioner names manual testing as what caps his speedup

Khramtsov sets out seven defensive layers he has seen hold defect rates flat while output rises. Six of them are ordinary quality practice with agents attached: requirements review, unit tests at high coverage, manual testing, end-to-end tests, code quality passes, pull request review, monitoring. The useful part is where he says the cost sits. Manual testing is the step that has seen the least improvement, and he calls it "the main reason that my output has increased only 2-3x instead of something like 10x." He also finds that "coding agents aren't great at following complex instructions in AGENTS.md or CLAUDE.md", and works around it by running a separate pass per concern, which he costs at 5 to 15 minutes of implementation time and no extra attention. On review, he runs both Claude and Cursor over pull requests, says each finds different problems, then adds a further pass "where another agent prunes the proposed AI-generated PR comments that aren't actually meaningful."

Every throughput number in the piece is his own estimate from his own team. He reports no instrumentation, no baseline and no measurement behind the 2-3x, and the post gives a different multiplier in its opening than in the section that explains it.

Why it matters: The pruning pass is the transferable piece here. Two review agents that disagree produce more comments than anyone will read, so what decides whether the second reviewer helps or just adds noise is whatever sits downstream filtering it. His bottleneck claim points the same way as the study above: the scarce resource is the attention needed to check the output, not the output.