Don't Trust Your AI Agents. Test Them Like a Scientist — Stephanie Jarmak, Omni Analytics
Surface metrics like cost savings can mask fundamental AI agent tool-use failures during evaluation
“you need to understand what you are actually trying to configure”
Stephanie Jarmak, drawing on backgrounds in astronomy and agent engineering, argues that AI agent evaluation requires scientific skepticism rather than trust in surface-level metrics. Her example: a 28% cost reduction looked promising, but the agent still wasn't using the MCP tool it was configured to use — the headline metric obscured the actual failure. The core lesson is that practitioners must rigorously define what they are measuring before drawing conclusions from evaluation results.