7 AI Agent Evaluation Patterns That Catch Failures Before Production

Why Most AI Agents Fail in Production You built an AI agent. It works in your notebook. You deploy it. Then users start reporting hallucinations, infinite loops, and $400 API bills from runaway tool calls. Sound familiar? The gap between "works in demo" and "works in production" is evaluation . Yet most teams skip it entirely — or worse, they "vibe check" outputs manually. In this guide, I'll share 7 concrete evaluation patterns with real code you can copy into your projects today. Pattern 1: Deterministic Output Assertions The simplest pattern. Before you get fancy, test the things you know should be true. import json from dataclasses import dataclass from typing import Any , Callable @dataclass class EvalCase : """ A single evaluation test case. """ name : str input_prompt : str assertions : list [ Callable [[ str ], bool ]] max_tokens : int = 4096 temperature : float = 0.0 @dataclass class EvalResult : """ Result of running an eval case. """ case_name : str passed : bool failures :