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.
Analyze Agent Performance: Build and Test
Analyze Agent Performance: Build and Test
This course is part of Agentic AI Performance & Reliability Specialization
Instructor: LearningMate
Access provided by Xavier School of Management, XLRI
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
- Data Transformation
- Statistical Hypothesis Testing
- Agentic systems
- Business Metrics
- Statistical Methods
- Statistical Inference
- Descriptive Analytics
- Data Analysis
- Data-Driven Decision-Making
- Business Intelligence
- Key Performance Indicators (KPIs)
- Performance Testing
- Performance Metric
- Event Monitoring
- Correlation Analysis
- Statistical Analysis
- Generative AI Agents
Tools you'll learn
Details to know
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Taught in English
Recently updated!
December 2025
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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.
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- Gain a foundational understanding of a subject or tool
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There are 2 modules in this course
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
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.
What's included
3 videos1 reading2 assignments1 ungraded lab
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