Why Code Review Becomes the Hardest Part After Codex Parallelly Handles Three Spring Boot Tasks

Running Codex on three independent Spring Boot tasks—fixing an enum compatibility issue, adding unit tests for a payment callback, and analyzing user‑module parameter‑validation problems—demonstrates how quickly AI can produce dozens of file changes when each task runs in its own worktree.

While the worktree isolation prevents local pollution, the real effort shifts to reviewing the generated diffs. Each diff may touch multiple layers (Controller, Service, Mapper XML, tests), and without a structured review process developers can be overwhelmed.

The author proposes a concrete review checklist that Codex should output alongside its diff, covering core reasons for changes, affected files, impact on interfaces, database schema, cache, MQ, transactions, test coverage, and remaining manual risk points.

Beyond the checklist, the article lists critical questions developers must ask when evaluating AI‑generated changes, such as whether the modification masks historical data issues, introduces excessive mocking, or alters contracts with front‑end callers.

To manage risk, the author classifies Codex tasks into four categories:

Read‑only analysis : no code changes, only impact analysis.

Low‑risk modifications : adding tests, fixing imports, updating documentation—diffs are acceptable but still need review.

Medium‑risk local changes : bug fixes or boundary adjustments in a limited module—require worktree isolation, review summary, and test verification.

High‑risk core logic : payment, order flow, inventory, permissions—AI may suggest solutions but should not merge without human judgment.

The recommended workflow is: (1) isolate each task in a separate worktree, (2) let Codex generate code and a review summary, (3) perform a thorough human review using the checklist, and (4) decide whether to merge, rollback, or defer the change.

This approach shifts the core competency of Java developers from writing code quickly to evaluating AI‑generated changes, controlling merge risk, and orchestrating multiple parallel AI tasks safely.