Building an AI agent prototype is relatively easy. With an LLM, a retrieval pipeline, and several API connections, developers can create an impressive demonstration within days.
The real challenge begins when the system reaches production.
Real users submit unclear requests, external tools fail, business data changes, and model costs increase unexpectedly. An agent that performs well in a controlled test may become unreliable when thousands of people start using it.
A Real-World Example: Vanta’s Support Agent
Vanta provides a useful example of how an AI agent should be tested before full deployment.
