Building a Review Workflow for AI-Augmented Teams

If your team is using Copilot, Cursor, or Claude for code generation, your review workflow probably hasn't kept up. You're running a 2024 review process against 2026 code volume. It's not working, and throwing more hours at review isn't the answer.

What you need is a workflow redesign. Not a dramatic overhaul — a practical restructuring that accounts for the reality that a significant chunk of your code is now AI-generated and needs different handling than human-written code.

Here's how to build it.

Step 1: Acknowledge the two types of code

Your PRs now fall into roughly two categories, and pretending they're the same is where most workflows break.

AI-assisted code: The developer used AI tools to accelerate writing, but the architecture and approach are human-directed. The developer understands the code, made intentional choices, and the AI was a productivity tool.

AI-generated code: The developer prompted an AI to produce a feature or module. The developer may understand the output but didn't architect every decision. The code might be subtly wrong in ways the author wouldn't catch because they didn't make those choices deliberately.

Some PRs are a mix. That's fine. The point isn't a binary classification — it's that review depth should scale with how much the author actually understood and directed the code.

Many teams are starting to use labels or PR template checkboxes to indicate the level of AI involvement. Something as simple as a "🤖 AI-generated" label signals to reviewers that this PR needs more scrutiny, not less.

Step 2: Build a triage layer

In the pre-AI world, triaging PRs was informal. Developers eyeballed the diff size and the author's seniority and decided how carefully to review. That heuristic doesn't work when a junior developer can generate a 500-line PR with Cursor in 20 minutes.

Build an explicit triage step:

Tier 1: Fast-track (< 15 min review)

• Config changes, dependency updates, typo fixes
• Auto-generated boilerplate with passing tests
• Straightforward changes to well-tested areas

Tier 2: Standard review (15-45 min)

• Feature additions following established patterns
• AI-assisted code where the author clearly directed the approach
• Refactors within a single module

Tier 3: Deep review (45+ min)

• Changes to core business logic
• Security-sensitive code
• New patterns or architectural decisions
• Large AI-generated PRs where the author is junior or unfamiliar with the area
• Anything touching payment processing, auth, or data handling

The triage doesn't have to be one person's job. It can be as simple as the PR author self-categorizing with a label, or a lead doing a 2-minute scan of new PRs each morning to flag priority.

Step 3: Assign reviewers by expertise, not availability

Round-robin reviewer assignment treats all reviewers as interchangeable. They're not — especially for AI-generated code.

The best reviewer for an AI-generated PR is someone who deeply understands the area being changed. They'll catch the subtle issues: the AI used a pattern that doesn't fit your system, the code handles edge cases incorrectly, the approach will cause performance issues at your scale.

For AI-assisted code where the author directed the approach, a peer reviewer is often sufficient. The author already made the architectural decisions; the reviewer is checking execution.

This means your CODEOWNERS or reviewer assignment needs to be expertise-based, not just path-based:

# Domain experts for deep review
/src/billing/ @payments-experts
/src/auth/ @security-team

# General team for standard review
/src/features/ @engineering-team

Step 4: Structure the review itself

AI-generated code needs different scrutiny than human-written code. The review focus shifts from "did they implement it correctly" to "did the AI make the right choices for our system" — which requires deeper contextual knowledge from the reviewer.

For the actual review, AI-generated code needs different scrutiny than human-written code — we cover this in detail in Best Practices for Reviewing AI-Generated Code.

Step 5: Set review SLOs (not SLAs)

SLAs create compliance pressure. SLOs create goals. The difference matters.

For AI-augmented teams, reasonable SLOs:

• Tier 1 PRs: First response within 2 hours. Merged within 4 hours.
• Tier 2 PRs: First response within 4 hours. Merged within 1 business day.
• Tier 3 PRs: First response within 4 hours. Merged within 2 business days.

These are targets, not mandates. Track them to identify systemic issues, not to evaluate individuals.

The key insight: first response time is more important than total cycle time. A reviewer who says "I'll get to this by EOD" within 30 minutes of the PR being opened is more valuable than a reviewer who does a thorough review 24 hours later without warning.

Step 6: Fix the communication layer

All of the above falls apart if reviewers don't learn about PRs quickly enough to act on them.

The workflow needs to deliver PR information to reviewers in a way that:

1. Reaches them where they work — Slack DMs, not email
2. Includes triage context — PR size, tier, who else is reviewing
3. Updates in place — one message that shows current state, not a thread of events
4. Respects focus — batch notifications where possible, don't interrupt deep work for Tier 1 PRs

This communication layer is the infrastructure that makes the triage, assignment, and review steps actually work. Without it, you have a great process on paper and a pile of unreviewed PRs in practice.

Making it work in practice

Here's what a day looks like on a team running this workflow:

9:00 AM: The lead does a 5-minute triage of overnight PRs. Labels them Tier 1/2/3. This takes about 5 minutes for 8-10 PRs.

9:05 AM: Tenpace sends Slack DMs to assigned reviewers with PR details and tier labels. Reviewers see what's waiting when they open Slack.

9:30 AM: Quick reviews knock out Tier 1 PRs. Three PRs merged before standup.

10:00 AM: Standup is shorter because everyone knows the review queue state.

10:30 AM - 12:00 PM: Two developers block off time for Tier 3 reviews. Deep review of the AI-generated billing refactor.

2:00 PM: Another review block. Tier 2 PRs get attention. Authors receive feedback and can push updates before end of day.

5:00 PM: 80% of the day's PRs are either merged or have active feedback. The review queue is manageable.

No heroics. No "review day" emergencies. Just a workflow designed for the actual volume and type of code the team produces.

Tenpace handles the communication layer — getting the right PR to the right reviewer with the right context. The rest of the workflow is up to you.

If you're redesigning your review process for AI-augmented development, drop us a line at hello@tenpace.com — we're collecting these workflows and will share what we learn.