Developers once spent more time waiting for code reviews than writing code, but today's AI coding agents produce far more output. This shift hasn't eliminated the review burden—it has intensified it. Reviewing AI-generated code feels particularly draining because it disrupts the natural flow of understanding.

The core problem is building a mental model from a finished changeset. Reading scattered code fragments is far less intuitive than watching logic unfold sequentially. An AI agent should act like a pair programmer, narrating its process as it builds, not just dumping a take-home assignment on your desk.

Imagine an agent that explains its work in a logical story, with live commentary. This approach matches how humans learn, reduces context switching, and could scale to orchestrate multiple agents. The next step is enabling real-time feedback, creating a true collaborative loop with AI.