Our team averages 10.8 PRs per engineer per week, 2.5x the 90th percentile according to DX benchmarks. But every one of those PRs still needed a human to look at it, and the review queue became the constraint.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at The problem: the review queue.
Ona sells AI or agent tools or services, so it has a reason to publish flattering numbers about AI. The passage stays in the archive; read it with that in mind.
Checked against the source by a first reader, then confirmed independently by a second reader who fetched the source without seeing the first reader's notes (Independent review agent (Claude Sonnet), blind second pass from passage text and citation only, no repository access, 2026-11-04).
Matches once a link-tag artifact is removed. The stripped text had 'week , 2.5x' with a stray space, which counts as whitespace. It is in The problem: the review queue section, in Ona's own voice.
Ona, “How auto-approving low-risk PRs with AI cut our lead time by 74%”, Ona blog, 2026-04-09, at The problem: the review queue. https://ona.com/stories/auto-approving-low-risk-prs Archived at https://the-ai-record.com/passages/ona-team-averages-10-8-prs-per-engineer-per-week-2026
The AI agent can approve the review, but a person always clicks the merge button. That merge is the traceable approval event for SOC 2: the agent handles the review workload, the human controls what ships.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at Governance.
We already had Ona's code review automation running on every PR. The agent reviewed diffs, ran the code in a full environment, cross-referenced tickets, and left inline comments with an 88% acceptance rate and a 50% reduction in time spent on pull requests.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at The experiment.
The team was roughly the same size throughout (39 unique contributors before, 43 after), but each developer went from merging 34 PRs per month to 96: a 2.9x increase in individual throughput with no headcount change.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at The results.
First, engineers cannot self-classify a change as low-risk. An Ona Automation evaluates every PR against objective criteria automatically, which removes the temptation to game the system and makes the boundary auditable.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at The policy.
Our engineers waited a median of 2 hours 49 minutes for their first PR approval. Four weeks after we let an AI agent approve low-risk PRs, that dropped to 3.8 minutes, lead time fell 74%, and deploys tripled, all with the same size team.
OnaPublished by the organization itself (post), no named author
Post: How auto-approving low-risk PRs with AI cut our lead time by 74%, Ona blog, April 9, 2026, at Opening.
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