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Analyze Agent Performance: Build and Test

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Analyze Agent Performance: Build and Test

This course is part of Agentic AI Performance & Reliability Specialization

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Aggregate agent performance data and apply statistical A/B tests to objectively measure and validate improvements in AI systems.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Agentic AI Performance & Reliability Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

Analyze Agent Performance: Build and Test is an intermediate course for data analysts, ML engineers, and developers tasked with optimizing AI systems. In a world where agentic AI is increasingly common, it is not enough to build an agent—you must prove its effectiveness. This course equips you with the data-driven skills to measure, monitor, and improve AI agents built with frameworks like LangChain, Autogen, and CrewAI.

You will learn to transform raw, noisy logs into actionable KPIs by applying data aggregation techniques with SQL and dbt. Through hands-on labs, you will design and execute controlled A/B experiments, comparing agent versions to identify meaningful improvements. You will master core statistical methods, including the Chi-square test, to determine whether your results are statistically significant or just random chance. You will be able to move beyond correlation to causation, making objective, evidence-based recommendations on deploying agent enhancements.

Module details

This module establishes the foundation for effective AI agent performance analysis. Learners will move beyond raw system logs to create structured, high-level metrics suitable for business intelligence and monitoring. The module focuses on applying data aggregation techniques with SQL and dbt to transform operational data into meaningful key performance indicators (KPIs) like conversation counts and latency.

What's included

2 videos1 reading2 assignments

2 videosTotal 11 minutes

Defining Agent Success: From Vanity Metrics to Actionable KPIs6 minutes
The Modern Data Stack for AI6 minutes

1 readingTotal 7 minutes

Advanced Time-Series Aggregation: Windows, Bucketing, and Operational Definitions7 minutes

2 assignmentsTotal 30 minutes

Build an Agent Performance Data Model20 minutes
Knowledge Check: Data Transformation for Business Intelligence10 minutes

Module Description: This module equips learners with the skills to scientifically prove the effectiveness of changes to their AI agents. Learners will move from correlation to causation by designing and analyzing controlled A/B experiments. The module provides hands-on experience with statistical hypothesis testing, focusing on the Chi-square test to determine if observed performance improvements are statistically significant.