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
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Analyze Agent Performance: Build and Test
This course is part of Agentic AI Performance & Reliability Specialization
Instructor: LearningMate
Included with
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
- Performance Metric
- Agentic systems
- Key Performance Indicators (KPIs)
- AI Workflows
- Performance Testing
- Statistical Methods
- Statistical Inference
- Event Monitoring
- Data-Driven Decision-Making
- Statistical Hypothesis Testing
- Data Analysis
- Correlation Analysis
- LangChain
- Business Metrics
- CrewAI
- Data Transformation
- Descriptive Analytics
- Statistical Analysis
- Generative AI Agents
- Business Intelligence
Details to know
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Recently updated!
December 2025
Taught in English
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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
- Develop job-relevant skills with hands-on projects
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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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Frequently asked questions
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