Engineering Teams Using AI Agents - PR Review Capacity Strain

AI made writing code cheap. It did not make engineering judgment cheap. As coding agents get better, I keep seeing the same bottleneck move downstream: More code → more pull requests → the same number of humans capable of reviewing them. I’ve experienced this firsthand while working on a cybersecurity product used in environments where reliability and security genuinely matter, including critical infrastructure. There was understandable pressure to turn the productivity gains from AI-assisted development into dramatically faster delivery. But that eventually leads to a much harder question: Who takes responsibility for code that no human has ever reviewed? Having one agent write the code and another approve it may make the throughput numbers look fantastic. It does not magically transfer accountability. I’ve since had similar conversations with people at other companies facing the same problem. The challenge is increasingly not “how do we get agents to write more code?” It is: How do we safely process all the code they already produce? In my latest article I explore what I think the next generation of the review pipeline needs: • deterministic automated verification • independent agent review • risk-based classification • human attention focused where judgment actually matters • smaller, reviewable changes • merge queues • WIP limits when the review queue is already full I also go into what I’m experimenting with in vincent and, more importantly, the parts I still haven’t solved. Coding faster is not the same thing as delivering faster. Code is getting cheap. Judgment isn’t. And trust may become the most expensive resource in the entire software lifecycle. 👉 https://lnkd.in/djJK9dnQ I’d especially like to hear from teams already dealing with this: are agent-generated PRs starting to overwhelm your review capacity? #SoftwareEngineering #AI #AgenticAI #CodingAgents #DevTools #EngineeringLeadership #SoftwareArchitecture