Since my team already uses Cursor Pro+, I spent some time weighing whether to buy a dedicated SaaS bot, build a custom agent from scratch, or leverage cloud agents via GitHub Actions. I ended up going with the Action + cloud-agent route because it kept the pipeline in my repo and utilized a subscription I was already paying for.
The AI Review Workflow #
Most AI review paths—whether it's a DIY agent or a paid product—follow the same logic: they are great at reasoning over a diff and surrounding code, but they aren't a replacement for deterministic CI. In my current AI workflow, I've split the process into four distinct layers:
Deterministic CI: Lints, tests, secret scans, and dependency audits.AI First-Pass: Narrative briefs and judgment calls (the "sharp intern" layer).Human: The final merge decision.Preview/Staging: Clickable environments for manual verification.
Comparing the Three Main Paths #
Every automated review system follows a "Listen → Think → Speak" pattern. Depending on the tool, you either own the logic or rent it.
Option 1: Dedicated PR Review Products (SaaS)
These are "plug-and-play" GitHub Apps that provide inline comments and summaries.
Pros: Fastest setup; polished UX with severity levels and ignore rules.Cons: Another vendor to clear with security; pricing often scales by seat/repo rather than your own AI credits.
Option 2: The DIY Agent (Build from Scratch)
This involves setting up a GitHub webhook or Action that calls a model API, fetches PR data, and posts a comment.
Pros: Total control over the prompt and severity bar; can integrate internal APIs.Cons: You are responsible for maintaining the "plumbing" and tool-calling logic.
Option 3: Cloud Coding Agents (The Hybrid Path)
Using a cloud runtime (like Cursor's) triggered by a GitHub Action.
Pros: Leverages existing IDE subscriptions; keeps the trigger logic in the repo.Cons: You're tied to that specific agent's ecosystem.
For a real-world deployment, the "Think" part of the process usually looks like this:
Listen → PR opened or /review command triggered
Think → Model gathers context and reasons over the diff
Speak → Structured comment posted to the PR
If you're looking for a beginner-friendly way to start, treating the AI as a preliminary analyst rather than a final gate is the most sustainable approach. It catches the "obvious" logic bugs and N+1 queries, leaving the humans to focus on high-level architecture.
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