Vercel AI SDK Agent Development

Evals, guardrails, and observability, none of which a demo needs. A demo has to work once in front of an audience. A production agent has to behave acceptably across the long tail of real inputs, fail safely when it cannot, and be debuggable when it does. Most of our engineering time goes into that gap rather than into the agent loop itself, which is usually the easy part.

By constraining what it can reach rather than by asking it nicely in a prompt. Tools are scoped to least privilege, anything consequential such as sending money, emailing a customer, or writing to a system of record goes through an explicit approval gate, and the agent runs under step and budget ceilings. Prompt instructions are a useful layer but they are not a security boundary, and treating them as one is a recurring cause of incidents.

Tracing plus a regression eval suite you keep running. Every run records its inputs, tool calls, and outcome, so you can inspect any individual case. The eval suite catches quality regressions when you change a prompt or a model, which is the failure mode that otherwise goes unnoticed for weeks because nothing errors. You own both.

Whichever one meets your quality bar at an acceptable cost, which we determine by measuring rather than by preference. A common outcome is a frontier model such as Claude Opus 5 for the reasoning steps and a cheaper model like Claude Sonnet 5 or Claude Haiku 4.5 for high-volume classification or routing. We build the provider abstraction so this stays a configuration decision you can revisit.