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Haichi Ops
Haichi Ops

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

#ai #productivity #opensource

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.

The Architecture: Two Local Agents, One Loop

Instead of trusting one model's output, the workflow splits the job into two roles:

  1. Agent 1 (Reviewer): Reads the git diff or file changes. It searches specifically for logical flaws, security vulnerabilities, edge cases, or missing unit tests.
  2. Agent 2 (Verifier): Takes the Reviewer's list of findings and actively challenges them. If a finding is weak or unsupported, the Verifier pushes it out of the action list. If it holds up, the next step stays visible.

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.

Why Local-First?

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:

  • Local-first workflow: Run Reviewer and Verifier style loops around local models.
  • Visible evidence trail: Keep task, review, challenge, and result in one workspace instead of scattered chat tabs.
  • Scoped execution: Keep actions bounded to the workflow you explicitly run.
  • Practical limits: Control how much concurrent agent work runs on your machine.

Try it on your own code

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.

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