Instead of relying on engineers to remember documented procedures (e.g., pre-commit checklists), encode these processes into custom AI skills. This turns static best-practice documents into automated, executable tools that enforce standards and reduce toil.

As AI coding agents generate vast amounts of code, the most tedious part of a developer's job shifts from writing code to reviewing it. This creates a new product opportunity: building tools that help developers validate and build confidence in AI-written code, making the review process less of a chore.

Most developers admit to giving pull requests only a cursory glance rather than pulling down the code, testing it, and reviewing every line. AI agents are perfectly suited for this meticulous, time-consuming task, promising a new level of rigor in the code review process.

With AI agents capable of generating code and designs at an unprecedented rate, the new chokepoint in workflows is human review. The primary challenge is no longer production but scaling the evaluation process to ensure AI-generated output aligns with quality standards and company values.

Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.

In an AI-assisted workflow, Spec-Driven Development (SDD) redefines the engineer's role. Instead of reviewing code implementation, teams focus on creating and approving detailed specifications as the primary collaborative artifact, leaving technical checks to specialized agents.

Chris Fregley argues that manually reviewing AI-generated code is slow and ineffective. He has replaced traditional code reviews and unit tests with a focus on robust, continuous evaluation frameworks ("evals") and correctness checks that run in the background, allowing for faster and safer code deployment.

A new paradigm for AI-driven development is emerging where developers shift from meticulously reviewing every line of generated code to trusting robust systems they've built. By focusing on automated testing and review loops, they manage outcomes rather than micromanaging implementation.

Treat code reviews like a system to be automated. Tally every piece of feedback you give in a spreadsheet. Once a specific comment appears a few times, write a custom lint rule to automate that check for everyone. This scales your impact and frees you up for higher-level feedback.