The no-nonsense approach to AI agent development
By Steven Van ·
The guide walks through prototyping an agent by hand, automating the loop in plain code, and adding evaluations, using v0.dev as its scoping example.
Vercel published a guide to building AI agents, arguing that the most reliable agents are narrow, tightly scoped, and built with ordinary programming rather than new abstractions.
- Prototype by hand. Simulate the task manually first: feed real inputs (screenshots, API responses, messy CSV data) into an LLM through prompts that mirror the actual workflow, and carry out any resulting actions yourself before writing code.
- Automate the loop. Model the agent as a loop or state machine that gathers input, runs deterministic code wherever possible, calls the model only when judgment is needed, evaluates the result, and decides whether to continue or exit.
- Optimize for reliability. Tighten prompts and tool calls, replace model calls with plain deterministic functions where possible, then add structured evaluations to catch regressions across a range of real-world inputs.
As an example of narrowing scope, the guide points to Vercel's own v0.dev: when it launched in 2023, LLMs struggled to generate reliable HTML, and the fix came not from better models but from narrowing the task and using structured Tailwind inputs. It also links out to Vercel's AI SDK docs for common agent patterns.
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