Role-Based Programme
RB0586

Microsoft Copilot Studio for Quality Management

Explore AI Agent Testing, Evaluation & Quality Assurance

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how Microsoft Copilot Studio supports structured testing and quality assurance for enterprise AI agents.
  • Explore test cases and evaluation methods for assessing accuracy, relevance, groundedness, completeness, tool usage, and expected behaviour.
  • Identify defects in agent responses, knowledge retrieval, workflow execution, and user interactions.
  • Experience repeatable agent testing before publishing or modifying production agents.
  • Recognise the importance of responsible AI review, human validation, governance, and continuous quality monitoring.

Tools covered

Microsoft Copilot StudioTest Your AgentAgent EvaluationTest SetsGeneral Quality EvaluationCompare MeaningTool Use TestingKeyword MatchKnowledge TestingActivity MapAgent FlowsAnalytics & Monitoring

Who should attend

  • Quality Managers
  • Quality Assurance Professionals
  • Quality Analysts
  • Software Quality Engineers
  • Test Engineers
  • Quality Engineering Professionals
  • UAT Professionals
  • Process Quality Professionals
  • Digital Quality Professionals
  • AI Quality Professionals
  • Release Quality Professionals
  • Quality Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of quality assurance or testing
  • Familiarity with requirements, test cases, expected results, and defect concepts
  • Basic awareness of AI agents or automation is helpful
  • General understanding of business processes is beneficial
  • No programming expertise required
  • No previous Microsoft Copilot Studio experience required

TOC Modules

Concepts
  • Understanding AI agents and their quality characteristics within Copilot Studio
  • Understanding the difference between functional testing, response-quality testing, workflow testing, and responsible AI review
  • Identifying common agent-quality risks such as incorrect responses, poor grounding, missing information, and inappropriate tool selection
  • Understanding why generative systems require continuous rather than one-time testing
Practical activities
  • Exploring a sample Copilot Studio agent from a quality perspective
  • Mapping Agent Requirement → Expected Behaviour → Test Condition → Quality Evidence

Scenarios

Customer-Service Agent to Release Quality Gate

Agent Requirement → Test Cases → Knowledge Validation → Agent Evaluation → Response Quality Scores → Tool / Workflow Validation → Defect Correction → Regression Test → QA Recommendation

Participants evaluate a representative agent before release, identify failed scenarios, validate corrective changes, and provide an evidence-based quality recommendation.

Agent Change to Regression Assurance

Knowledge / Instruction / Tool Change → Existing Test Set → Automated Evaluation → Previous vs Current Results → Failed Test Analysis → Agent Refinement → Re-Evaluation

Participants use reusable test sets to determine whether an agent update improves the intended behaviour without introducing regressions into previously validated scenarios.

## Current Capability Reference

Microsoft Copilot Studio currently provides structured **Agent Evaluation** using reusable test sets. Evaluations can cover single responses or conversations and allow teams to repeatedly test changes against a consistent quality benchmark.

Current evaluation methods include **General Quality**, which measures relevance, groundedness, completeness, and abstention; **Compare Meaning** for expected-answer similarity; **Tool Use** for validating expected capabilities; and **Keyword Match** for required terms or phrases.

Copilot Studio's monitoring environment can also surface **run outcomes, trigger usage, tool usage, knowledge-source usage, effectiveness, and custom metrics**, supporting continuous quality improvement after deployment.

Current **Agent Flows** support AI actions, branching and control structures, connectors, and **human-in-the-loop** steps, so quality testing should validate both generative responses and deterministic workflow behaviour.

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