AI coding assistants have moved the bottleneck. Two years of Copilot and Cursor gave teams more code than ever, and reviewing it has become a nightmare. Senior engineers have to skim 400-line diffs for style while security and correctness questions slip past them into production.
Writing for CIO, Vittesh Sahni and Vladimir Malko argue that the answer is to split the review job rather than trust one reviewer, human or AI, to catch everything.
The more autonomy you give AI, the more boundaries you have to set. On any high-blast-radius path, that last step stays human.
The authors set out for CIO the following:
- Why "almost right" AI output is the dangerous kind, and how it ships bugs that pass every test
Why "almost right" AI output is the dangerous kind, and how it ships bugs that pass every test
- How to split the review into a requirements check and a quality check running in parallel
How to split the review into a requirements check and a quality check running in parallel
- What a service catalog and architect-written solution notes give the review agent to check against
What a service catalog and architect-written solution notes give the review agent to check against
- When to expand AI scope, and the two signals that must move together before you do
When to expand AI scope, and the two signals that must move together before you do
Ship faster, break more, unless you rebuild
AI writes bigger changes, so every merge carries more risk. The teams that stay ahead rebuild review into a real operating model, one where humans still own the high-stakes merges, instead of bolting AI onto the old lifecycle and hoping speed doesn't turn into breakage.
Read the full story at CIO.com.
