At the scale Autobuild operates — 6,000+ managed PRs in a single project — traditional developer tools break down. GitHub's PR interface is built for humans reviewing one PR at a time. We needed a different model: one that surfaces the work that needs human attention and treats review delays as the system failures they are.
The SLA model
We treat PR review latency the same way we'd treat API latency: as an SLA. The dashboard surfaces tasks that are breaching their review window, categorizes time-loss by root cause (long review cycles, excessive CI failures, merge conflicts), and lets engineers action PRs — including viewing captured screenshots and videos — without leaving the dashboard.
- < 1 hour review SLA target for AI-generated changes
- Time-loss analysis broken down by failure category
- Inline video and screenshot review directly in the dashboard
- Priority queue based on estimated unblock value
- Automatic re-triage when blocked PRs are resolved upstream
Why this matters
Human review is the rate-limiting step in any AI-assisted development workflow. The orchestration layer isn't about automating review — it's about making the review that does need to happen as fast and frictionless as possible. When an agent submits 500 PRs in a day, a 4-hour average review time means a 2,000 hour-hour backlog. Treating that as a system problem, not a people problem, changes how you design for it.