Test Your AI Agent Like a Senior Engineer: 4 Patterns That Work

Your AI agent passes every unit test. Then it hallucinates a database schema in production, invents an API endpoint that doesn't exist, and confidently returns a JSON response missing three required fields. Unit tests prove your functions run. They don't prove your agent works. The difference costs you production incidents, user trust, and the 3 AM pages that make you question your career choices. Here are 4 testing patterns that senior engineers use to catch these failures before deployment — with working Python code for each. Pattern 1: Schema Contract Tests The first thing that breaks in an AI agent is the output format. You ask for structured data, the LLM returns something close but not quite right. A missing field. A string where you expected an integer. A nested object with an unexpected key. Schema contract tests enforce that every agent output matches an exact Pydantic model — and they do it without calling the real LLM. from pydantic import BaseModel , Field from pydantic_ai