Engineering leadership
How do I review more AI-generated PRs without slowing delivery?
Reduce review load by separating facts that machines can prove from decisions that require human judgment. Automate traceability, freshness, test, and exact-contract evidence before a reviewer opens the pull request.
Design a risk-based review lane
Not every pull request deserves the same review depth. A small change with current intent and complete exact evidence can move quickly. A change with unclear product authority or a contradictory chain needs focused human review.
Do not use AI authorship alone as the risk signal. Review the scope, affected contracts, product premise, and strength of the available evidence.
Automate facts
Collect linked requirements, changed symbols, tests, exact checks, and stale-context findings.
Route judgment
Send semantic contradictions and product tradeoffs to the accountable human.
Keep evidence reusable
Cache conclusions by exact inputs and rerun only the checks affected by a new head.
Give reviewers a conclusion map
A reviewer needs a compact report that separates blocking failures, advisory findings, unavailable checks, and satisfied contracts. Collapsing those states into one score hides the reason for action.
Helen’s Foundry assembles product-chain, specification, and code evidence around the pull request. The reviewer can spend time on the few conclusions that still need authority.
Measure the right bottleneck
Track time to resolve uncertain intent, not only time to approve code. If product context is missing, faster code review only moves the ambiguity downstream.