Vercel's CEO Just Unveiled The Greatest AI Coding Workflow Ever
Key Takeaway
Building applications with AI agents requires structured planning files, automated direction checks, security scans, and dedicated testing agents to maintain code quality and prevent drift.
Highlights
Vercel projects typically start with a specs folder containing a PRD file to outline core features before building an MVP.
The direction command runs multiple agents across requirement documents to check if an application is drifting off course.
DeepSec scans code for security gaps and generates reports after features are finished.
A breaker agent tests applications by simulating high concurrent pressure and user mistakes on copied data.
First-time user agents are restricted from reading app code via hooks to ensure unbiased browser testing of the UI.
Connecting MCP servers like Gmail and Sentry allows Claude to read support emails and error logs automatically.
Timeline
Initial Testing and Specifications
Developers often rely on unverified prompting tips, which can lead to flawed foundations. Establishing a product requirement document early helps catch fundamental flaws before code accumulates. Generating a quick minimum viable product allows teams to validate ideas early without wasting effort.
- Ideas are tested from scratch using independent agents rather than relying on unverified prompting guides.
- Projects start with a specs folder containing a PRD file detailing main features.
- Building an MVP quickly reveals whether an app idea is worth pursuing.
Tracking Application Direction
Agents make building cheap and fast, which can cause apps to drift away from original requirements into unneeded features. Creating a custom direction command evaluates the codebase against requirement documents. Developers can then choose to remove unwanted changes or update their specifications.
- Token usage and shipped changes measure activity rather than strategic progress.
- The direction command checks whether built features match requirement documents using separate agents.
- Reports provide scorecards and actionable lists to address code drift.
Security Debt and Quality Gates
The high volume of code produced by agents introduces significant security challenges and messy code known as slop debt. Running automated security tools and setting up strict quality gates ensures that critical parts of the application remain protected and reliable.
- Security debt accumulates rapidly when agents generate large volumes of code.
- DeepSec runs automated security scans to locate vulnerabilities in agent-built applications.
- Quality gates use review agents to hold code changes to strict standards before deployment.
Defining Non-Negotiable Trade-Offs
Building software always involves trade-offs between speed, cost, and polish. Because models tend to forget instructions over long contexts, non-negotiable rules must be explicitly written down in project configuration files and enforced via automated tests.
- Agent speed requires strict trade-off agreements regarding safety and functionality.
- A dedicated must not trade-off section inside the claw.md file defines core requirements.
- Rules are paired with automated tests and git commits to prevent agents from skipping them.
Stress Testing with Breaker Agents
Code generated by AI typically works under ideal conditions but often fails when users interact with it in unexpected ways. Deploying a dedicated breaker agent exposes weak spots and performance bottlenecks under pressure before real users encounter them.
- Breaker agents simulate heavy concurrent usage and edge cases to break applications on purpose.
- Testing runs on data copies to ensure real user data remains safe.
- Review loops allow agents to continuously find and fix weaknesses.
Simulating First-Time User Experience
Creators struggle to view their own software objectively because they already know how it works. Restricting test agents from accessing source code allows them to uncover confusing user interfaces and friction points that cause customers to abandon the app.
- Error-free sessions measure how many users complete app flows without encountering issues.
- Hooks block test agents from reading code so they simulate genuine first-time users.
- Browser test agents run through key paths and write code fixes when failures occur.
Automated Customer Feedback Integration
Handling customer issues efficiently requires connecting AI models directly to support channels and error reporting tools. Claude can sort incoming reports, fix straightforward bugs instantly, and flag complex feedback for human review.
- Customer feedback speed indicates how effectively a team utilizes AI agents.
- MCP servers connect Claude to tools like Gmail and Sentry for automated support reading and error tracking.
- Clear issues are resolved immediately while ambiguous feedback is routed to human judgment.
Description
The AI coding workflow of Vercel's CEO, broken into 7 tips. We turn his approach to AI agents into an AI coding setup you can copy in Claude Code: direction checks, breaker agents, first-time-user testing and more, with lessons for software engineering at any level. Thanks to Viktor for sponsoring. Hire your own AI employee at https://ref.viktor.com/ailabs-yt and get $100 in free credits with code AILABS100. Community with All Resources: https://ailabspro.io/?v=y2ifUC9_zuU The Roundup, our daily newsletter covering the AI stories that matter. Join now: https://www.theroundup.so/ Got a software idea but don't have the team to build it? Autometa turns your ideas into production-ready software. Check out https://autometa.dev/yt to see what we can build for you. The CEO of Vercel gave an interview about how he works with AI agents. It's one of the most useful AI coding interviews we've seen, so we went through all of it and pulled out the 7 tips that actually help when you're coding with AI: 1. Test before you choose. He tests every idea in a new project and has an agent check from scratch whether it works. That goes for picking models too. You don't find the best AI for coding by going off vibes or a prompting guide. You test it yourself. We do the same thing by building an MVP before we commit to the whole app. 2. Direction check. Tokens spent and changes shipped show how much you're building, not where it's going. We turn this into a direction command that checks your app against your PRD and requirement docs, then gives you a scorecard and a to-do list. 3. Careful zones. Security debt and slop debt grow fast when agents write the code. Run a security scan after every feature (Vercel built DeepSec for this) and put quality gates on the parts of your app that matter most. 4. Say what you won't trade off. Add a must-not-trade-off section to your CLAUDE.md, and back each rule with a test so the agent can't skip it. 5. Breaker agent. An AI coding agent whose only job is to break your app on purpose. It works on copies of your data, so your real data stays safe. 6. First-time-user agent. An agent that uses your app like a brand-new customer would. A hook blocks it from reading your code, so its feedback stays honest. A second agent tracks error-free sessions across every flow your customers have to get through. 7. Feedback triage. Connect Claude to Gmail and Sentry through MCP servers. Clear bugs get fixed straight away, and anything that needs your judgment comes to you. AI vibe coding is easy when you're the only user. These tips are for when real customers show up. Most of them are easy AI coding wins: each one starts with a single prompt to your agent. We use Claude Code, but the ideas aren't tied to any one coding AI. They work with whatever AI coding tools you already use, from ChatGPT and other OpenAI tools to a free AI coding plan. If you want to build real products with artificial intelligence, this is how someone running a major software company does it. 00:00 Intro 00:51 Test Ideas First 02:18 Stay on Course 04:01 Sponsor 04:57 Security Debt 07:14 Non-Negotiables 08:33 Breaker Agent 10:26 First-Time User Agent 12:42 Customer Feedback Hashtags #ai #claude #chatgpt #openai #vibecoding #aiagent #artificialintelligence #aicoding