Build an AI code review bot in 30 minutes with Vercel Eve
Episode
24 min
Read time
2 min
Topics
Remote Work, Investing, Fundraising & VC
AI-Generated Summary
Key Takeaways
- ✓PR Auto-Approval at Scale: Intercom's AI PR review system delivers 5x faster approvals than human reviewers while simultaneously reducing production revert rates. Their approach proves AI-reviewed code can exceed human-reviewed code in quality and safety, making auto-approval a net positive rather than a risk tradeoff for engineering teams managing high PR volume.
- ✓Risk Scoring Framework: Structure PR risk scoring across six dimensions: change size and blast radius, reversibility, data and security impact, operational changes, and verification gap (test coverage plus CI completion). Assign numeric weights to each dimension; scores below 24 are low risk, 25–64 medium, and 65-plus high risk requiring mandatory human review.
- ✓Vercel Eve Agent Architecture: An Eve agent requires only four components to function as a PR reviewer: a GitHub channel connector, a markdown instruction file (roughly one page), a skill to read PR diffs, and two tools for risk assessment and decision output. Vercel handles OAuth, refresh tokens, and Slack/GitHub configuration through built-in wizard-based connectors.
- ✓SOC 2 Compliance Compatibility: Auto-approving PRs does not violate SOC 2 or HIPAA frameworks provided the policy is documented in risk and code review policies, and every decision is logged, auditable, and queryable. Teams should confirm specifics with their security team, but compliance is achievable without requiring human sign-off on every individual PR.
- ✓Eval Loop for Internal Bots: Treat internal AI agents like customer-facing products by logging every PR review decision into an evaluation platform. Engineers periodically audit whether the bot scored correctly, enabling continuous improvement of risk thresholds and scoring logic — the same eval methodology used to improve external AI products applies directly to internal automation tools.
What It Covers
Host Claire demonstrates how to build an AI-powered pull request review bot using Vercel's Eve agent framework in under 30 minutes. The bot automatically scores PRs as low, medium, or high risk using a 100-point system, auto-approves low-risk changes, and escalates others to humans via Slack.
Key Questions Answered
- •PR Auto-Approval at Scale: Intercom's AI PR review system delivers 5x faster approvals than human reviewers while simultaneously reducing production revert rates. Their approach proves AI-reviewed code can exceed human-reviewed code in quality and safety, making auto-approval a net positive rather than a risk tradeoff for engineering teams managing high PR volume.
- •Risk Scoring Framework: Structure PR risk scoring across six dimensions: change size and blast radius, reversibility, data and security impact, operational changes, and verification gap (test coverage plus CI completion). Assign numeric weights to each dimension; scores below 24 are low risk, 25–64 medium, and 65-plus high risk requiring mandatory human review.
- •Vercel Eve Agent Architecture: An Eve agent requires only four components to function as a PR reviewer: a GitHub channel connector, a markdown instruction file (roughly one page), a skill to read PR diffs, and two tools for risk assessment and decision output. Vercel handles OAuth, refresh tokens, and Slack/GitHub configuration through built-in wizard-based connectors.
- •SOC 2 Compliance Compatibility: Auto-approving PRs does not violate SOC 2 or HIPAA frameworks provided the policy is documented in risk and code review policies, and every decision is logged, auditable, and queryable. Teams should confirm specifics with their security team, but compliance is achievable without requiring human sign-off on every individual PR.
- •Eval Loop for Internal Bots: Treat internal AI agents like customer-facing products by logging every PR review decision into an evaluation platform. Engineers periodically audit whether the bot scored correctly, enabling continuous improvement of risk thresholds and scoring logic — the same eval methodology used to improve external AI products applies directly to internal automation tools.
Notable Moment
Counterintuitively, the most time-consuming part of building the bot was not writing code — it was configuring third-party dashboards for Slack and GitHub app permissions. The host resolved this by using an AI browser agent to navigate and complete those configuration screens autonomously, cutting setup time dramatically.
Episode Transcript
Everybody is drowning in AI created PRs. When anyone can code, anyone can cook, and anyone can vibe anything to GitHub, that means that a lot of us are sitting on a big queue of pull requests that then we have to review. If you are an engineering leader or a software engineer in a company that has embraced AI in any way, shape, or form, you feel this pain. And I get this question all the time. We figured out how to code with AI. Now what do we do with all these PRs? Well, people smarter than us have figured this out. And you know what the answer is? You don't have to review all your PRs. Yep. You heard it here first. You do not actually have to review one on one by a human all those PRs. So in today's episode, I'm gonna show you how to build your own PR review, risk scoring, and automatic approval bot so you can take all the low risk PRs off your plate, get those to production, and focus on the ones that really require your human expertise. This episode is inspired by a couple folks, especially our past HowIAI guest, Brian from Intercom, who has led the way in auto scoring and auto approval PRs. But I'm gonna show you exactly how it gets done, and we're gonna get it done very quickly in this thirty minute How I AI mini episode. Let's get to it. This episode is brought to you by WorkOS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems. Your copilot needs to see your entire code base. Your chatbot needs to search across internal docs. And for enterprise buyers, that raises serious security concerns. That's why these apps face intense IT scrutiny from day one. To pass, they need secure authentication, access controls, audit logs, the whole suite of enterprise features. Building all that from scratch, it's a massive lift. That's where Work OS comes in. Work OS gives you drop in APIs for enterprise features so your app can become enterprise ready and scale up market faster. Think of it like Stripe for enterprise features. OpenAI, Perplexity, and Cursor are already using Work OS to move faster and meet enterprise demands. Join them and hundreds of other industry leaders at workos.com. Start building today. Okay. I know what you all are thinking. You're thinking, Claire, you are crazy. Of course, I have to review every PR that comes out of AI. That's just how it's done. We review all of our human PRs, so I have to review all of our AI PRs. And I am telling you in 2026, the answer is you can safely not review PRs by putting AI in the loop. And I wanna call attention to …
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