Snowflake's internal data team reports that its agent takes under 16 minutes on average to go from incident detection to a pull request ready for review. Over five months, across the managed pipelines the team owns, it reports hundreds of merged pull requests and at least 500 pipelines improved.
The authors, Zachary Blackwood and Sebastian Cattes, call the human decision their most important requirement: "The agent proposes the repair. A person still decides what ships."
That is the detail worth carrying into another team's implementation. The reported time ends when a proposal is ready. A team adopting this approach still needs to decide what evidence makes that proposal acceptable and who can approve it.
What the speed figure leaves open
Snowflake describes a workload growing faster than its team. More pipelines brought more failures, and engineers had to investigate, diagnose, write a fix, test it and open a pull request. The agent now takes on that work. This addresses the capacity problem the authors describe.
The workflow includes testing proposed fixes before anything touches production. The article does not describe those tests or the criteria people use to approve a change. Its reported near-complete triage coverage applies to monitored pipeline failures. It should be read within that scope.
For a reviewer, I would make the question explicit: does this proposed change repair the failure while preserving the dataset's intended use? If it would change which records are included or what a value means, that consequence should be explained in the review. The person approving it should understand why the change is acceptable.
A reviewer who can judge the code may need help answering that question. The review process should provide a route to someone who understands the dataset's use.
Nothing in the article establishes that review has become the bottleneck. Faster preparation of repairs gives a team a reason to examine review capacity, but the constraint could remain elsewhere. Measure what happens after the pull request opens before deciding where more effort belongs.
What the practice has to supply
The configuration offers a useful starting point. Snowflake's example names an owner, identifies repositories and a branch, sets a limit of 50 agent turns and a 30-minute timeout, and specifies notification destinations.
Those fields make parts of the workflow explicit. The example does not define the owner's approval authority or show the criteria for accepting a repair. A team borrowing this pattern should define what that responsibility includes.
I would put the approval requirements alongside the agent's instructions: what the proposed repair must explain, which checks it must pass, and who can resolve a question about its effect on the data. These requirements should help both the agent prepare a useful proposal and the reviewer assess it.
Then track time through review and merge, including why proposals wait or are rejected. If review becomes the queue, investigate whether people lack time to review or lack the information needed to decide. Adding reviewers would not necessarily resolve the second problem.
If a reviewer cannot tell whether the repair preserves the dataset's intended use, the next step is to resolve that uncertainty before approving the change.