Context Isolation in AI Code Review

I use Claude Code with the Oh My ClaudeCode plugin for my daily coding tasks. Its orchestration workflows have AI agents review their own output in a loop, catching issues and verifying fixes automatically.

But after using it for a while, I noticed a recurring problem.

When agents share context across iterations, they mostly verify that problems from the first pass are fixed. They rarely find new issues. The shared memory works against me.

Why shared context makes agents miss new problems

When an agent carries forward everything it found in the previous iteration, its attention is anchored to those findings. It checks whether the fix was applied correctly and confirms the problem is gone, but it never looks at the code from scratch.

I see the same thing when I re-review code I already reviewed: I verify my previous comments rather than look for entirely new problems. An AI agent in a continuous context window does the same — it re-reads its own notes instead of the code.

Building an agent system with clean context

About a year ago, I built an AI agent system from scratch for a personal project. Agentic workflows weren’t standardized yet, and I was figuring things out based on my own intuition about how review cycles should work.

The key design choice I made: each review iteration starts with only the input and the agent instructions. No memory of what previous passes found. No history of prior issues or fixes.

This forces the agent to approach the work fresh every time. After refining the agent persona to avoid repetitive patterns, it started catching new problems on each pass.

Applying this manually in daily work

I still apply this principle in my daily coding tasks, especially during code reviews. I run the review orchestration from scratch each time with clear context — only the code and the review instructions.

Across multiple iterations, it keeps finding issues that previous passes missed entirely. Those only show up when the reviewer doesn’t know what was already flagged.

So I now run every multi-pass review with isolated context.

What I’m looking for

The limitation of my current approach is that it’s manual. I launch each review iteration myself, ensuring the context is clean before each pass.

Ideally a tool would automate this pattern — repeated independent evaluation with clean context at each pass. I want systems that natively support context isolation between review cycles, not just human-in-the-loop orchestration.

I haven’t found one yet.