Get human-quality pull request reviews on every change — context-aware, codebase-trained, and posted back to GitHub.
Review agents read the full PR diff, the linked feature request, and the related call sites — not just the changed lines.
Catches the patterns that linters miss: API contract drift, unhandled edge cases, missing tests for new branches.
Posts inline comments and review summaries directly on GitHub PRs so reviewers see them in the tool they already use.
Most teams can't review every pull request line-by-line, especially when half of them come from agents now. AI code review agents are the second pair of eyes: a reviewer that reads the diff with full repo context, comments on the exact lines that need attention, and turns the review pass from a bottleneck into a background job. The reviews land in GitHub the same way a senior engineer's would — inline comments, a summary, a clear approve-or-request-changes verdict.
Most 'AI code review' tools read the patch in isolation: they see the changed lines but not the call sites that depend on them, the tests that cover them, or the linked feature request that explains what the change is trying to achieve. That's why they catch style issues but miss the regressions that actually matter — a return type that changed silently, a function whose contract drifted from its callers, an edge case the test suite never exercised. AI Expedite's review agent uses the workspace's code analysis index to read the full graph around the patch, so it can reason about whether the change is consistent with the rest of the codebase, not just whether the patch itself parses.
When a PR opens, the review agent pulls the diff and the linked feature metadata (Linear, Jira, or AI Expedite's own feature object). It runs a planning step — 'what should I be looking for in this kind of change?' — that's tuned to the kind of work being shipped (new feature vs. bug fix vs. refactor). It then walks the diff in the context of the relevant call sites and tests, and emits inline GitHub comments on the specific lines that warrant a second look. The summary explains the agent's reasoning and the changes it would request before merge.
The review agent is deliberately quiet. It does not comment on every line, and it does not nitpick style — that's lint's job. It comments when it spots a regression risk, an unhandled edge case, a missing test for a new branch, or a divergence from a pattern used elsewhere in the codebase. The goal is that the human reviewer sees three to five high-signal comments per PR, not thirty cosmetic ones. Comments include the agent's confidence so reviewers know when to trust it and when to push back.
Half the PRs the review agent sees are written by Claude Code or Codex — the other half by humans. The reviewer treats them the same way: the same checks, the same comment style, the same threshold for approval. That symmetry matters because agent-authored code shouldn't get a faster lane than human-authored code, and human-authored code shouldn't have to opt out of a review just because the team adopted agents.
Directly on the GitHub pull request, as inline comments with a top-level review summary. Reviewers see them in the same place they review human reviewers' comments.
Both modes are supported. By default the agent comments and requests changes without blocking. You can configure it to block on high-confidence regression flags — useful for protected branches.
Claude is the default for review because it's strong at long-context reasoning over diffs. The orchestrator can fall back to other models if Claude is unavailable or you've configured per-repo overrides.
Both, with the same checks. Half the value of an AI reviewer is making the agent-authored code go through the same quality gate as the rest of the team's work, but the review agent has no preference about who wrote the diff.
The biggest difference is context. AI Expedite's review reads the full repo dependency graph and the linked feature request, not just the patch. That's the difference between catching style issues and catching regressions.
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