Visdom Code Review

Engineering Control for AI-Generated Code

Agents generate code. Humans stay in control.

The gap

AI accelerates code creation, but it also multiplies review volume. Human reviewers become the bottleneck, especially for agent-authored changes that pass CI but are still wrong.

Issues standard code review misses

Circular Tests

Tests that validate what the code does instead of what it should do.

Convention Drift

"Reasonable" patterns that don't match how code is actually written inside your organization.

Defensive Over-engineering

Unnecessary wrappers, null checks, exception handling, and abstractions that add complexity without adding value.

How it works

A multi-layered review process for AI-generated code

Visdom Code Review does not evaluate code in isolation. Every PR passes through layers of increasing depth - fast and cheap for trivial changes, thorough for risky ones.

Code Quality Standards
Architecture Consistency
Ownership Boundaries
Engineering Conventions
Security and Maintainability Risks
Production Impact

What you get

Deeper Review Where It Matters

Context-Aware Review. Review code in the context of your organization.

Enriches every review with codebase context - ownership, dependencies, history, conventions, and test reliability - so feedback reflects how your systems are actually built.

Risk-based prioritization. Apply scrutiny where it matters most.

Classifies every change by risk, impact, and system criticality - from fast deterministic checks to deep analysis and mandatory human approval

Multi-Layered Review. Build confidence layer by layer.

Every PR passes through layers of increasing depth. Fast and cheap for trivial changes, thorough for risky ones. Measured across a 38-PR run: ~22 seconds per PR on average.

Why it matters