Originally published on AIdeazz — cross-posted here with canonical link.
This experience led to a hard decision: no new AI agent feature ships without first integrating into my evaluation harness. I now have 131 tests across four distinct layers, costing me $0.03 per full run. This harness isn't a luxury; it's the bedrock of shipping production AI agents with zero VC funding and a single developer.
The Fundamental Flaw of Unit Tests for AI Agents
Unit tests verify my code. They assert that my_function(input) returns expected_output. For traditional software, this is sufficient. For AI agents, it's dangerously incomplete. An AI agent's core logic isn't deterministic code I wrote; it's emergent behavior from an LLM interacting with tools and external systems.
Consider a multi-agent system designed to process customer inquiries.
