What we learned building agents at Vercel
Vercel just shared its playbook for finding AI agents that actually work—and it's not about capability, it's about cost-benefit fit.
Why it matters
As enterprises experiment with agents, Vercel is publishing strategic lessons from its own internal AI transformation—offering a rare glimpse into how a leading platform company identifies which agent use cases are ready for production today, not someday.
The key facts
9 to knowVercel has spent months experimenting with internal agents across customer support, code reviews, and sales operations
Core insight: the constraint isn't AI capability—it's finding problems where agents deliver ROI at sustainable cost
Vercel developed a repeatable methodology for prioritizing AI projects by business impact likelihood
Company is using its own products to build agents, positioning itself as a reference implementation
Focus on internal dogfooding and pragmatic adoption rather than speculative use cases
Vercel using internal agents for customer support, code reviews, sales operations
Focus on ROI: identifying problems AI can solve today at economically viable cost
Months of experimentation led to repeatable methodology for agent investment decisions
Emphasis on productivity gains and quality outcomes in enterprise workflows
Go to the source
Vercel Blogvercel.com
Publisher excerpt: Agents present incredible promise for increased productivity and higher quality outcomes in enterprises. Companies are already using them to streamline customer support, code reviews, and sales operations. When building custom internal agents, the challenge isn't whether AI can create value, it's…
