To maximize leverage, reframe every SDLC component—docs, tests, review agents—as a way to 'prompt inject' non-functional requirements into the agent. This approach teases out expert knowledge from engineers' heads and makes it part of the automated system, guided by the agent's mistakes.

The focus of "code review" is shifting from line-by-line checks to validating an AI's initial architectural plan. After plan approval, AI agents like OpenAI's Codex can effectively review their own generated code, a capability they have been explicitly trained for, making human code review obsolete.

When an AI model generates code, the focus of a pull request review changes. It's no longer just about whether the code works. The engineer must now explain and defend the architectural choices the model made, demonstrating they understand the implications and haven't just accepted a default, suboptimal solution.

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.

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.

As AI writes most of the code, the highest-leverage human activity will shift from reviewing pull requests to reviewing the AI's research and implementation plans. Collaborating on the plan provides a narrative journey of the upcoming changes, allowing for high-level course correction before hundreds of lines of bad code are ever generated.

Borrowing from classic management theory, the most effective way to use AI agents is to fix problems at the earliest 'lowest value stage'. This means rigorously reviewing the agent's proposed plan *before* it writes any code, preventing costly rework later on.

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.

It's infeasible for humans to manually review thousands of lines of AI-generated code. The abstraction of review is moving up the stack. Instead of checking syntax, developers will validate high-level plans, two-sentence summaries, and behavioral outcomes in a testing environment.