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Course Outline
Foundations of Mastra Debugging and Evaluation
- Understanding agent behavior models and failure modes
- Core debugging principles within Mastra
- Evaluating deterministic and non-deterministic agent actions
Setting Up Environments for Agent Testing
- Configuring test sandboxes and isolated evaluation spaces
- Capturing logs, traces, and telemetry for detailed analysis
- Preparing datasets and prompts for structured testing
Debugging AI Agent Behavior
- Tracing decision paths and internal reasoning signals
- Identifying hallucinations, errors, and unintended behaviors
- Using observability dashboards for root-cause investigation
Evaluation Metrics and Benchmarking Frameworks
- Defining quantitative and qualitative evaluation metrics
- Measuring accuracy, consistency, and contextual compliance
- Applying benchmark datasets for repeatable assessment
Reliability Engineering for AI Agents
- Designing reliability tests for long-running agents
- Detecting drift and degradation in agent performance
- Implementing safeguards for critical workflows
Quality Assurance Processes and Automation
- Building QA pipelines for continuous evaluation
- Automating regression tests for agent updates
- Integrating QA with CI/CD and enterprise workflows
Advanced Techniques for Hallucination Reduction
- Prompting strategies to reduce undesired outputs
- Validation loops and self-check mechanisms
- Experimenting with model combinations to improve reliability
Reporting, Monitoring, and Continuous Improvement
- Developing QA reports and agent scorecards
- Monitoring long-term behavior and error patterns
- Iterating on evaluation frameworks for evolving systems
Summary and Next Steps
Requirements
- An understanding of AI agent behavior and model interactions
- Experience with debugging or testing complex software systems
- Familiarity with observability or logging tools
Audience
- QA engineers
- AI reliability engineers
- Developers responsible for agent quality and performance
21 Hours
