How I built a 2-agent code review loop for local Ollama workflows

A developer built a two-agent code review loop for local Ollama workflows, splitting the job into Reviewer and Verifier roles to reduce hallucinations and generic advice. The workflow is packaged into HAICHI, a desktop workspace for Windows and Linux that connects to local Ollama models and keeps workflow state inspectable.

When running code reviews with local LLMs, a single model can either hallucinate non-existent bugs or generate generic advice you end up ignoring. To make local AI code review more useful, I built a closed Reviewer vs. Verifier loop for local Ollama workflows. Instead of trusting one model's output, the workflow splits the job into two roles: The goal is not to make the model "always right". The goal is to make weak claims easier to catch before you act on them. Many agent workflows eventually ask you to move private workspace context into somebody else's control plane. I packaged this workflow into HAICHI , a desktop workspace for Windows and Linux that connects to local Ollama models and keeps the workflow state inspectable. Key features: HAICHI Personal is free to try. If you're already using Ollama for real development work, test the Reviewer vs. Verifier loop on one change and let me know where it helps, where it is too noisy, and what your local setup looks like.