The AI Coding Paradox: Shifting Bottlenecks from Engineering to Product
My engineering team has fully transitioned to an AI-first workflow. Recently, we have focused heavily on building custom coding agents and optimizing our test harness loops to deliver features straight from Product Requirement Documents (PRDs).
When analyzing our dashboard—tracking assigned tasks and merged Pull Requests (PRs)—I noticed a clear trend: the total number of PRs is dropping significantly.
This drop actually highlights two major wins:
- Higher Agent Quality: Our AI agents are becoming much smarter. We see fewer but higher-quality PRs, which drastically reduces the need for back-and-forth code reviews or hotfix PRs.
- Clearer Requirements: Refining PRDs directly with AI agents eliminates unnecessary, ambiguous tasks early on, leading to fewer unnecessary code changes.
However, this massive leap in engineering velocity has created an unexpected new bottleneck: the Product team cannot keep up.
Our software is being built faster than the product team can test, validate, and accept the delivered features. The idle time spent waiting for product sign-off is growing larger every day. As an Engineering Manager, solving this cross-functional lag is my next big challenge.
To keep the engineering team highly productive during these waiting windows, I am looking at two strategic paths:
- Tackling Technical Debt (The Best Solution): Directing our engineering bandwidth toward the backlog to refactor legacy code, optimize databases, and improve infrastructure—tasks that are completely independent of business team validation.
- Continuous Agent R&D: Pushing our harness loops even further and researching advanced techniques to maximize the efficiency, speed, and accuracy of our AI coding agents.
In the era of AI-driven development, the constraints are no longer found in writing code, but in managing the flow of value across the entire organization.