Configure a Code Review Pipeline

Configure a code review pipeline

With Cosmos’s code review pipeline, reviewers no longer need to read every changed line in a PR to gain confidence in the shipped code. Agents do most of the mechanical review work, while humans focus on knowledge transfer and judgment calls — the small fraction of the process where human context matters most. This is how you scale code review. This is the system Augment uses internally to move PRs through review faster without compromising quality. As described in How Cosmos accelerated our AI-native transformation by removing the code review bottleneck, Augment’s code output has increased 3x since adopting this agent-driven review pipeline, while median merge time dropped and bug rate per output unit trended down. The fleet has been tuned to maximize throughput without compromising quality. If your team has a standard code review process, we recommend adopting the full fleet so agents can cover authoring, risk routing, correctness checks, runtime verification, and review memory while humans stay focused on product, architecture, security, rollout, and organizational context.

The Cosmos code review fleet

The pipeline is a set of specialized Experts. Each Expert owns one loop in the review process, and the system is most effective when the loops work together.

PR Author

PR Author is the canonical Ticket-to-PR Expert. It owns the author-side loop: turning a ticket or task description into a reviewed pull request or merge request, then staying with it through review comments, CI failures, and merge conflicts.

Role in the pipeline: Keeps implementation and review follow-up moving without requiring a human to babysit every update.
Pairs with: Reviewers that evaluate the changes PR Author opens, especially Risk Analyzer, Deep Reviewer, Pair Reviewer, and Verifier.

Risk Analyzer

Risk Analyzer evaluates every new or ready-for-review change, auto-approves low-risk changes, and routes higher-risk changes appropriately.

Low-risk changes: Docs-only edits, simple config changes, mechanical refactors, or other changes your team has decided can be auto-approved
Higher-risk changes: Anything that needs human input on design, architecture, security, rollout, tests, or product behavior

For higher-risk changes, Risk Analyzer flags the change for human review and points humans at the exact decisions they need to make.

Role in the pipeline: Prevents every change from receiving the same expensive level of human attention.
Pairs with: Pair Reviewer for changes that need deeper context or human judgment.

Deep Reviewer

Deep Reviewer performs line-by-line correctness analysis and posts inline comments for objective findings. It is the most similar to a traditional AI code review tool: there is no chat and no human-in-the-loop during the review.

Role in the pipeline: Handles objective correctness review before a human spends attention on judgment calls.
Pairs with: Risk Analyzer for routing and Pair Reviewer for complex changes that need human context.

Pair Reviewer

Pair Reviewer is the interactive path for changes that need human judgment. It focuses the review on the items in the change that require human judgment and knowledge transfer — design and architecture, security, risks, and similar. It prepares a briefing, pulls in relevant context, guides the human through the key questions, and posts finalized comments or a verdict only after human authorization.

Role in the pipeline: Converts complex reviews into guided judgment calls for the human reviewer.
Pairs with: Risk Analyzer for routing and shared memory for team-specific review standards.

Verifier

Verifier exercises the change in a running environment and reports evidence-backed findings. It focuses on what was observed, what could not be observed, and what evidence supports the result.

Role in the pipeline: Adds runtime evidence so reviewers can distinguish confidence from guesswork.
Pairs with: PR Author for fix follow-up and Pair Reviewer for human-facing summaries of what was verified.

Memory Manager

Memory Manager learns from merged changes and human feedback. It captures signals such as human comments, replies to bot comments, reactions, and Pair Reviewer sessions, then distills those signals into reusable per-repo review knowledge that other review Experts can load on future reviews.

Role in the pipeline: Makes the review system adapt to local conventions over time.
Pairs with: Every review Expert that benefits from shared team standards and prior feedback.

Recommended rollout

You can adopt the pipeline incrementally, but teams with a standard code review process should plan to adopt the full fleet to see the largest throughput gains.

Start with Deep Reviewer for objective bug-finding on selected repositories.
Add Pair Reviewer for high-stakes changes where the team wants structured human judgment.
Add Risk Analyzer once the team agrees on low-risk criteria and sensitive paths.
Add Verifier for changes that need runtime evidence before merge.
Add PR Author to remove the author-side bottleneck for tickets and follow-up fixes.
Add Memory Manager so review feedback continuously improves future triage and review.

Teams with strict compliance requirements should define exactly which changes can be auto-approved, which paths always require human review, and how agent-authored approvals are audited before enabling automatic approvals.

What changes for reviewers

With Cosmos, reviewers no longer need to read every changed line of every change with the same level of attention. Instead:

Low-risk changes can be cleared quickly with justification.
Objective correctness checks run automatically and post inline findings.
High-risk changes arrive with a briefing that points to the decisions humans need to make.
Humans still own final judgment on architecture, product behavior, security tradeoffs, rollout risk, and merge decisions.
The system learns from feedback, so future reviews better match your team’s standards.