Code review without reviewing code

While authoring code has become virtually instantaneous, reviewing and verifying that code in an enterprise environment remains constrained by human cognitive capacity. At scale, this becomes a painful bottleneck: pull request volume and size are growing rapidly, review latency is increasing, and 10,000-line diffs can all too easily burn through finite reserves of human attention.

How do engineering organizations scale trust and quality at the speed of AI generation?

I won’t pretend we’ve fully slain this dragon, but in this session I’ll walk through a few different approaches we’re taking and what we’ve learned within Atlassian:

Inner Loop: Building confidence through automated local AI review and skills for catching anti-patterns and contract violations before code ever reaches a human teammate.

Outer Loop: How tools like Taro spin up live environments, simulate user interactions, and provide tangible evidence (narrated videos) directly to reviewers.

A New Lens on Code: Aggregating quality signals like CI, agentic evaluations, and risk scoring to allow reviewers to “zoom out” and reason about the value and implications of a change without having to read the diff line-by-line.

Rethinking the Unit of Change: Why the traditional single-ticket micro-PR is an artifact of human typing limits, and how we can shift human expertise to where it matters most.

Attendees will leave with concrete architectural patterns, lessons learned from real-world rollouts, and a blueprint for evolving their team’s code review practices for the AI era.