Building a Practical Test Suite for Your LLM Agent (Without Enterprise Tooling)

What We Will Build By the end of this tutorial, you will have three working test patterns you can drop into any LLM agent project today: Behavioral assertions that catch real failures without breaking on benign rewording An eval harness (under 50 lines) that uses a cheap LLM to grade your agent's output Contract boundary tests for the deterministic code wrapping your LLM calls No ML ops pipeline. No six-figure tooling budget. Just patterns that work for solo developers and small teams shipping AI-powered features. Let me show you a pattern I use in every project that involves an LLM agent. Prerequisites Python 3.10+ pytest installed ( pip install pytest ) An OpenAI API key (we will use gpt-4o-mini for judging — it costs fractions of a cent) A basic LLM agent you want to test (even a single function that calls an API and returns text) If you do not have an agent yet, the examples below are self-contained. You can follow along and adapt them to your own code after. Step 1: Understand Why