The key point here is that AI review shouldn’t become a second model looking at the same assumptions the first model made. The strongest review pipeline is layered and independent: deterministic checks establish what can be verified mechanically, repository-aware analysis looks for architectural drift, and humans validate intent and risk.
One addition I’d make is to treat AI-generated changes as having a different verification profile, not simply another PR. At IT Path Solutions, I’d want provenance, affected boundaries, new dependencies, permission changes, and failure-path coverage visible before deciding how much review a change needs.
The “merge gate, not another review queue” framing is especially useful. If AI increases PR volume faster than human review capacity, adding another stream of AI comments doesn’t solve the bottleneck. The goal should be to eliminate predictable failures automatically and reserve human attention for the decisions that require system and business context.
The key point here is that AI review shouldn’t become a second model looking at the same assumptions the first model made. The strongest review pipeline is layered and independent: deterministic checks establish what can be verified mechanically, repository-aware analysis looks for architectural drift, and humans validate intent and risk.
One addition I’d make is to treat AI-generated changes as having a different verification profile, not simply another PR. At IT Path Solutions, I’d want provenance, affected boundaries, new dependencies, permission changes, and failure-path coverage visible before deciding how much review a change needs.
The “merge gate, not another review queue” framing is especially useful. If AI increases PR volume faster than human review capacity, adding another stream of AI comments doesn’t solve the bottleneck. The goal should be to eliminate predictable failures automatically and reserve human attention for the decisions that require system and business context.