Evaluate and regression-test AI agents

Overview

Most agent teams discover failures after users complain. This course teaches you how to build eval sets, score agent behavior, and run regression tests so your agent improves over time instead of drifting unpredictably.

Who it's for

• Teams shipping agentic workflows into production
• Developers tuning tools, prompts, and orchestration logic
• Product owners who need evidence that an agent is getting better

What you'll build

• A representative eval dataset drawn from real user tasks
• A scoring framework covering correctness, tool use, latency, and escalation behavior
• A regression-testing loop for checking whether changes improved or degraded performance

Prerequisites

• An existing agent or prototype workflow
• Access to sample tasks or historical transcripts
• Comfort comparing outputs against expected behavior

Tools and setup

1. Choose the behaviors you need to measure
2. Assemble a realistic test set from actual use cases
3. Define pass and fail criteria before tuning the system

Modules

Module 1: Build the eval set

You will capture representative tasks, edge cases, and failure modes so your tests reflect production reality.

Module 2: Score the agent

You will define objective and rubric-based checks for final answer quality, tool-call quality, and when the agent should ask for help.

Module 3: Run regressions continuously

You will compare versions, investigate failure clusters, and turn eval results into concrete improvement work.

Deliverable

A reusable evaluation harness that helps you measure, compare, and improve agent performance over time.

Common mistakes

• Evaluating only happy-path demos
• Changing prompts without re-running the full test set
• Measuring eloquence while ignoring tool accuracy or failure handling

Next steps

Wire the eval loop into CI, release reviews, or a weekly quality review for your agent team.