July 23, 2026

How to Maintain Judgment Criteria When Delegating Code Review to Another AI

You can explain code structure to one AI and delegate security or performance review to another.

But if the new AI does not know the preservation conditions and evidence state of the current work, it may re-suggest already excluded solutions or widen the scope.

What needs to be connected in code review is not just code snippets but confirmed facts, prohibited conditions, and modification approval boundaries.

Information Lost When Only Code Is Transferred

Even looking at the same diff, without the following information, judgments can differ.

The actual issue the user experienced
The boundary where expected and actual values first diverged
Already confirmed non-causes
Existing features that must be preserved
Files and functions where modification is allowed
The state where the cause has not yet been confirmed
STOP conditions for deployment and verification

Leaving Evidence with Saved

Important log interpretations, code branches, and risk warnings can be left as Saved.

Saved is not a feature that confirms AI responses as correct answers. It is a coordinate for returning to specific questions and answers the next reviewer needs to re-check.

Creating a Review Contract with Anchor

Before handing off to another AI, the following can be organized as an Anchor.

Current problem definition
Confirmed facts
ROOT_CAUSE_PROVEN status
Existing feature preservation contract
Approved modification scope
Prohibited changes
Next verification tasks

Anchor does not replace the entire implementation specification but provides the criteria from which review should start.

Separating Roles with Handoff

One AI can be assigned read-only evidence review while another is assigned limited diff review.

Using Handoff, you can receive different perspectives by role without re-explaining the same criteria each time.

The new AI does not unconditionally follow previous conclusions. It reviews independently based on preservation conditions and evidence.

Preventing Solutions from Mixing In Before Cause Confirmation

The most dangerous thing in code review is treating a possible cause as the actual cause.

By clearly leaving the current evidence state in an Anchor, you can reduce the next AI from moving to arbitrary modifications, rollbacks, and refactoring.

5BY.AI does not automatically approve work. It connects the Gate and STOP conditions the user decided to the next conversation.

Viewing Review Flow in Graph View

In 5BY.AI Graph View, you can examine how the cause investigation Pack, important Saved, modification approval Anchor, and subsequent review Pack connected.

You can re-examine the following that disappears with only the final commit.

Why only that file was modified
Why certain alternatives were excluded
After which evidence modification was approved
What was reviewed before deployment

A Real-World Workflow

Investigate the issue and code flow in the first AI.
Leave important inconsistency boundaries as Saved.
Create the cause and preservation contract as an Anchor.
Handoff to another AI to delegate read-only falsification review.
If the cause holds, confirm the exact patch scope.
After implementation, perform limited diff review in a new AI.
Record results and remaining risks in a new Anchor.

It Does Not Replace Official Development Records

Code repositories, issues, test results, and deployment records should remain in official systems.

5BY.AI does not replace those records but helps rediscover the flow of judgments made while reviewing with AI.

What is needed when delegating code review to another AI is not copying many explanations.

It is maintaining what has been proven so far and what has not yet been proven, features to preserve, and approved scope as the same starting point.

5BY.AI's Saved, Anchor, and Handoff help those judgment criteria not disappear across conversation windows and AI services.