ReviewSquad: AI code review with IBM Bob

The problem Code review is one of the slowest and least reliable steps in software delivery. Pull requests wait hours for a busy reviewer, and when the review happens, it is often rushed: the tests pass, the diff looks reasonable, approved. But passing tests do not mean safe code. Hard-coded secrets, SQL injection, inverted business logic and untested code slip into production, where fixing them costs far more. Reviews are also inconsistent: each reviewer checks different things, and the team's written guidelines are rarely applied line by line. The solution ReviewSquad turns IBM Bob into a disciplined code review team. A developer opens the pull request in IBM Bob, selects the Review Lead mode and asks it to review. ReviewSquad then runs the full workflow: 1. It converts the git diff into a review brief with exact line numbers and runs the tests and fast automated checks for hard evidence. 2. The Review Lead reads the team's own code review guidelines from a PDF (document understanding). 3. It runs four specialist reviews - Security, Bugs & Logic, Tests and Team Rules - each recording structured findings with the file, line, severity, the guideline rule broken, and a fix. 4. The findings are merged into one review with a clear verdict. 5. After a human approves, a Fixer mode repairs every finding and adds tests; tests and checks run again. 6. ReviewSquad publishes a shareable report of what was found, by whom, and the before/after state. Target users are development teams where senior reviewers are a bottleneck. Developers interact entirely through IBM Bob's chat; reviewers read one report. Why it is different Most AI review tools make a single pass and leave a list of comments. ReviewSquad runs the whole workflow - understand the rules, review, decide, fix, verify, report - with clear roles and permissions: reviewer modes are read-only, and only the Fixer may edit code, only after approval. Deterministic tools do the counting; Bob does the judgment. Because the rules come from a document, every team gets reviews against its own standards, not generic advice. We also measure instead of claiming. Our demo pull request contains 11 planted problems and passes all 8 of its tests; GitHub marks it "Ready to merge". A hidden answer key, which Bob cannot read, scores the review. Impact (our recorded run) - Automated checks alone caught 7 of 11 planted problems; ReviewSquad with IBM Bob caught 10 of 11, including an inverted coupon-expiry check that no test or linter flagged. - After the Fixer: 37 of 37 tests pass (up from 8), and automated warnings dropped from 22 to 0. - The full review, fix and verification took minutes of agent time instead of a manual review cycle, with every finding traceable to a line and a team rule.