Reviewing AI-Powered Rewrites: The Artifacts Reviewers Need

AI can now refactor or even rebuild large code surfaces in days, not months. The problem is that review still depends on human understanding. If reviewers cannot see how an agent got from intent to diff, teams either slow down to rebuild context or ship blind. The fix is a new set of artifacts that make AI powered rewrites explainable, testable, and safe to merge.

Key Takeaways

AI rewrites move fast, but the review bottleneck is understanding, not generation.
Linear walkthroughs and artifact packs turn huge diffs into reviewable narratives.
The minimum evidence pack should include intent, scope, tests, and rollback plans.
Slice rewrites into reviewable units with clear boundaries and ownership.
Risk tiering keeps high blast radius changes out of fast lanes.
Propel operationalizes this with automated evidence collection and policy checks.

TL;DR

AI rewrite code review is now a documentation problem. If your reviewers cannot trace the why, the boundaries, and the proof, the change will stall or ship risky. Require a linear walkthrough plus a compact evidence pack for every AI rewrite. Break the rewrite into reviewable units, then gate high risk modules with deeper checks. That is the fastest path to safe velocity.

Why AI rewrites are suddenly real

The trend line is clear: teams are using AI to do much larger rewrites than a year ago. A recent example comes from Cloudflare, which described rebuilding a major Next.js based site with AI in a week. That kind of speed changes expectations, and it raises a new review question: how do you validate an AI driven rewrite when the diff spans hundreds of files?

Cloudflare on rebuilding with AI in one week

The review gap: speed outpaces understanding

AI agents can now run longer sessions and operate on bigger scopes. Tooling like Claude Code Remote Control makes it practical to manage an agent that keeps state and iterates over time. That is great for throughput, but it increases the distance between the intent and the final diff. Reviewers need more than a commit message to trust the change.

Claude Code Remote Control

If your AI workflow can edit 200 files in a single run, you need a new review artifact that explains scope, boundaries, and verification. The diff alone is not enough.

Linear walkthroughs make large diffs reviewable

Simon Willison called out linear walkthroughs as a core agentic engineering pattern. The idea is simple: after the agent finishes, it narrates the path it took in a clear, ordered story. For AI rewrite code review, that walkthrough is the missing bridge between the intent and the diff. It tells reviewers why files changed and what the agent believed it verified.

Agentic engineering patterns: linear walkthroughs

Pair the walkthrough with an evidence pack and you get a reliable review handoff. This is similar to the evidence-first approach we use in our guide on evidence-first AI code review.

The AI rewrite review artifact stack

Think of an AI rewrite like a change request that needs more than code. These artifacts should be created automatically every time an agent works at rewrite scale.

Core artifacts

Intent brief with success criteria and non goals
Linear walkthrough that narrates the agent flow
Scope map listing touched modules and ownership
Evidence pack with tests, logs, and verification steps
Risk tier and blast radius summary
Rollback plan and safe revert steps

If you already use formal review structure, layer these on top of your existing practices. Our guide to code review documentationshows how to standardize this without bloating the process.

Minimum evidence pack for an AI rewrite

The evidence pack should be small but predictable. If it is too heavy, teams will skip it. If it is too thin, reviewers will not trust it. Use a fixed template and automate the fill wherever possible.

Evidence pack template

Goal statement and what changed at a high level
Files touched with ownership and system boundaries
Test results, including failed or skipped tests
Performance or cost notes if critical paths moved
Security and data access changes, if any
Manual verification steps and outcomes

This template aligns with the principles in our guide on agentic engineering code review guardrails.

Slice rewrites into reviewable units

AI rewrites often span multiple systems. Reviewers cannot hold that in working memory. The fix is to slice the rewrite into reviewable units with explicit boundaries. Each unit should have a single reviewer owner and a clear scope statement.

Slicing heuristics

One service or package per unit
Single data contract or API surface per unit
One failure mode per unit, such as auth or latency
Short lived feature flags to gate rollout per unit

For a deeper look at scoping and change size, see our guide on PR size and review quality.

Tests and runtime proof should lead the review

Reviewers should see verification results before they scan the diff. That matches the way GitHub positions pull request review as a structured approval step tied to evidence.

About pull request reviews on GitHub

Require test output, relevant logs, and any manual verification before review starts. That is the fastest way to reduce back and forth, especially when the diff is large.

Risk tier the rewrite before review starts

AI rewrites should never land without a risk tier. Use a simple matrix so reviewers know if they need deeper checks or a second reviewer. We recommend this as part of the review policy in our AI code review and development playbook.

Risk tier signals

Authentication, authorization, or data boundary changes
Large schema or migration work
Critical path latency or cost changes
Third party dependency upgrades
Feature flag removal or permanent rollout

Operationalize the workflow with review automation

The big unlock is automation. Once the workflow is defined, use tooling to collect evidence and enforce policies automatically. Propel turns AI rewrite code review into a repeatable pipeline by generating evidence packs, routing by risk, and capturing review feedback that helps your team improve over time.

If you are building this internally, start with our guide on automated refactoring suggestions with AI code review.

Frequently Asked Questions

What is AI rewrite code review?

It is the review process for large, AI authored refactors or rebuilds. The key difference is the need for stronger artifacts that explain intent, scope, and verification beyond the diff.

Do we need a linear walkthrough for every change?

Only for rewrite scale changes or multi module diffs. Small patches can use standard review summaries. When scope grows, the walkthrough prevents misalignment and missed edges.

How do we keep reviewers from being overwhelmed?

Slice the rewrite into reviewable units, attach a compact evidence pack, and route high risk pieces to senior reviewers. That keeps the review surface small and predictable.

What should be automated first?

Start with evidence collection: tests, logs, and scope maps. Once those are automated, layer in risk tiering and policy checks.

Ready to make AI rewrites safe to merge? Propel helps teams turn large AI changes into evidence rich reviews that ship faster with lower risk.

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Author note

I work with engineering teams that use Propel to scale code review quality while adopting AI assisted development. These patterns are the ones that keep velocity high without sacrificing safety.