AI code review memory is the difference between detecting the same issue repeatedly and preventing it from recurring. Most teams already have the knowledge. It is buried in old pull requests, Slack threads, chat transcripts, and one developer's memory.
Agents do not reliably rehydrate from that mess. They need structured findings connected to the repository. Nool gives that workflow a minimal command cycle.
flowchart TD
A[Review comment] --> B{Reusable lesson?}
B -- No --> C[Resolve locally]
B -- Yes --> D[nool learn --kind finding]
D --> E[nool findings subject]
E --> F[Future agent context]
F --> G[Less repeated review]
When a reviewer spots a recurring issue, record it with `nool learn --about <file-or-topic> --kind finding --content "..."`. If the finding explains a dependency relationship, use `--kind dependency_insight`. If it explains why a previous approach failed, use `--kind root_cause`. Later, retrieve it with `nool findings <subject> --compact`.
For session-level context, use threads. `nool thread chat <name> --message "..."` stores DAG-backed notes. `nool thread show <name> --full` gives the next agent decisions, constraints, patterns, issues, and test gaps in one view. That is a better input than asking Codex, Claude, or Cursor to infer review history from scattered comments.
The review loop still includes tests and human judgment. Nool does not replace code review. It makes review cumulative. The first time a reviewer says "do not put payment secrets in frontend config," it can become a durable finding. The tenth time an agent tries it, the workflow has failed.
Good AI code review is not louder review. It is memory that compounds.
The best review findings are short and operational: "Use server-side Paddle tokens only," "Do not add generated assets to semantic analysis," or "Run `nool doctor --semantic-only --compact` before release markers." They are specific enough for future agents to act on. They also let human reviewers raise the level of review from repeated hygiene comments to product and architecture judgment.
Treat each good finding like a tiny internal lint rule with context. It does not need to block every change. It needs to be available before the same mistake reaches review again.