Introduction
Your team deploys an AI agent that handles customer inquiries, and initially it performs well. But as costs climb and customer feedback highlights response quality issues, you face a critical challenge: how do you improve the agent systematically without guessing which changes will help?
Random optimization attempts waste time and resources. You might switch models hoping for better performance, but without measuring the impact, you can't determine whether quality improved, costs decreased, or response times changed meaningfully. Different team members evaluate the same agent responses differently, making it impossible to compare experiments objectively.
Effective agent optimization requires structured evaluation: clear metrics that reveal quality, cost, and performance characteristics; controlled experiments that test one change at a time; and consistent scoring methods that eliminate human bias. Without this systematic approach, optimization becomes guesswork rather than evidence-based engineering.
Adventure Works, an outdoor adventure company, operates a Trail Guide Agent that helps customers plan hiking trips with trail recommendations, accommodation bookings, and gear suggestions. The team wants to reduce operational costs by switching from GPT-4 to GPT-4 mini, but they need to verify that quality doesn't degrade below their 4.2/5.0 customer satisfaction target and response times remain under 30 seconds. They need a structured approach to test this change objectively.
Learning objectives
In this module, you learn to:
- Design evaluation experiments with clear metrics for quality, cost, and performance
- Apply git-based workflows to organize and compare agent variants systematically
- Create evaluation rubrics that ensure consistent scoring across human evaluators
Let's start by discovering how to design evaluation experiments that measure agent performance objectively.