AI Code Review for Platform Engineering Teams

What problems do Platform Engineering Teams face in code review?

• Platform teams maintain shared libraries and infrastructure that product teams consume — a breaking change cascades across every team
• API contract changes get reviewed in isolation and break consumers that the reviewer didn't know about
• Cross-team PRs that modify platform interfaces need special scrutiny, but reviewers don't always know which changes are platform-critical
• Deprecation paths are hard to enforce through review alone — 'we deprecated this function' doesn't stop people from using it

How does Argus fit your workflow?

• Before review, Argus looks up files that depend on the changed code in a graph of the default branch (two levels, up to 50) and gives them to the reviewer model with the source of up to 3 direct dependents. It does not assign reviewers or trace consumers in other repos
• Link related PRs in the description (URL or owner/repo#N, up to 5 by default) and Argus checks the combined change for risks such as serialization contract changes, type drift, and deploy ordering
• Rules you add in the dashboard and patterns taught with @argus-eye remember --org (the handle is your GitHub App's slug; argus-eye is the default) are shared across the organization's repos. Up to 10 enabled rules (about 1,200 characters in all), highest priority first, reach each single-pass review call; specialist passes get none
• A changed file that 5 or more other files in the repo depend on is flagged as a choke point in its review prompt. Consumers in other repos are not counted

Which Argus features matter most for your team?

Dependents in the review prompt

The reviewer model sees which files in the same repo call or import the changed code. Cross-repo dependents are not included

Cross-repo PR checks

For PRs linked in the description, one LLM call looks for risks that appear only when the changes combine, across 9 categories including schema races, type drift, and deploy ordering. Results are informational

Org rules and patterns

Write a rule once, such as 'use the new client factory, not the deprecated one', and it can reach review prompts in every repo of the installation. A finding that closely matches a rule cites it

Memory → Files

A file-level dependency map of one repo, with fan-in, bug density, change frequency, and co-change coupling from past reviews