Automated PR Reviews: My Experience with Coding Agents

I wanted every single Pull Request to get a structured "first-turn brief"—a summary of changes, potential risks, and security or performance smells—before I even looked at the code. The goal wasn't to replace human reviewers or build a full AppSec program, but to stop starting every review "cold." I needed a signal on whether a PR looked fine, needed changes, or required a deeper discussion.

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.