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
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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
- Key Performance Indicators (KPIs)
- Agentic systems
- Statistical Analysis
- Business Intelligence
- Data Analysis
- Event Monitoring
- Data-Driven Decision-Making
- Correlation Analysis
- Statistical Methods
- Statistical Inference
- Data Transformation
- Statistical Hypothesis Testing
- Generative AI Agents
- Performance Metric
- Descriptive Analytics
Tools you'll learn
Details to know
December 2025
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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.
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 videos•Total 11 minutes
- Defining Agent Success: From Vanity Metrics to Actionable KPIs•6 minutes
- The Modern Data Stack for AI•6 minutes
1 reading•Total 7 minutes
- Advanced Time-Series Aggregation: Windows, Bucketing, and Operational Definitions•7 minutes
2 assignments•Total 30 minutes
- Build an Agent Performance Data Model•20 minutes
- Knowledge Check: Data Transformation for Business Intelligence•10 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.
What's included
3 videos1 reading2 assignments1 ungraded lab
3 videos•Total 16 minutes
- Correlation is Not Causation•5 minutes
- Running a Chi-square Test•5 minutes
- Non-Parametric Tests•6 minutes
1 reading•Total 8 minutes
- Principles of A/B Testing•8 minutes
2 assignments•Total 40 minutes
- Agent Performance Analysis Report•30 minutes
- Knowledge Check: Statistical Significance in Agent Experiments•10 minutes
1 ungraded lab•Total 25 minutes
- Analyze a Controlled Experiment•25 minutes
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