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At a glance
WHAT IT’S REALLY ABOUT
Auto-approve low-risk PRs using an Eve AI review agent
- AI-generated code is creating PR review backlogs, and Claire argues teams can safely stop human-reviewing every PR by using an AI-in-the-loop risk scoring and approval process.
- Inspired by Intercom’s results, the approach aims to improve both speed and safety—auto-approved PRs can move faster while maintaining auditability for compliance environments like SOC 2.
- The tutorial walks through implementing the agent with Vercel Eve’s directory-based framework (instructions, skills, tools, channels) and Vercel connectors for easy Slack/GitHub integration.
- The agent reviews diffs after CI is green, assigns a low/medium/high risk rating via a six-factor model, posts evidence in the PR, and either approves, requests changes, or escalates to humans.
- Claire highlights operational realities (repo rules, required human approvals) and recommends running ongoing evals to measure and continuously improve agent decisions.
IDEAS WORTH REMEMBERING
5 ideasYou can reduce PR review load without sacrificing safety by gating approvals on risk.
Instead of reviewing everything, the agent classifies changes as low/medium/high risk, allowing low-risk PRs to be fast-tracked while preserving human attention for risky areas like auth, billing, or API behavior changes.
Compliance concerns are addressable if the process is auditable and policy-backed.
Claire emphasizes that frameworks like SOC 2 can accommodate AI-assisted approvals when risk policies are documented and decisions are traceable (who/what approved, evidence, and logs).
Eve lowers the “agent plumbing” cost by standardizing channels and integrations.
Eve agents are largely a directory of instructions and TypeScript skills, while Vercel connectors handle painful OAuth/refresh-token and channel setup for GitHub and Slack.
Triggering after checks are green is a practical way to cut noise and risk.
The bot waits until CI/actions complete successfully, then reviews the diff and metadata; this avoids premature reviews and makes “verification gap” part of the risk assessment.
A simple, explicit scoring rubric beats vague ‘LGTM’ automation.
The bot scores risk across six factors (blast radius, reversibility, data security, operational impact, verification gap, and category/area risk) and uses thresholds (≤24 low, 25–64 medium, ≥65 high) to drive actions.
WORDS WORTH SAVING
5 quotesEverybody is drowning in AI-created PRs.
— Claire Vo
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.
— Claire Vo
AI-approved PRs and AI-written code in general can be even safer and even higher quality than what you're doing with just a human in the loop.
— Claire Vo
I often get this objection from folks. They say, "Well, I'm in a SOC 2 environment. I couldn't possibly auto-approve PRs."
— Claire Vo
I like to say this thing where we can put AI to work for us or we can have AI put us to work, and this is an example where you do both.
— Claire Vo
SPEAKERS
Claire Vo
hostHost of the "How I AI" show, covering practical AI tooling and workflows for engineers.
High quality AI-generated summary created from speaker-labeled transcript.
