Mastra Debugging, Evaluation & Quality Assurance for AI Agents Training Course

Foundations of Mastra Debugging and Evaluation

• Analyzing agent behavior models and failure patterns
• Core debugging principles specific to Mastra
• Assessing deterministic versus non-deterministic agent actions

Configuring Environments for Agent Testing

• Setting up test sandboxes and isolated evaluation spaces
• Capturing logs, traces, and telemetry for in-depth analysis
• Preparing datasets and prompts for systematic testing

Debugging AI Agent Behavior

• Tracing decision pathways and internal reasoning signals
• Recognizing hallucinations, errors, and unintended behaviors
• Leveraging observability dashboards for root-cause analysis

Evaluation Metrics and Benchmarking Frameworks

• Establishing quantitative and qualitative evaluation metrics
• Measuring accuracy, consistency, and contextual compliance
• Utilizing benchmark datasets for repeatable assessments

Reliability Engineering for AI Agents

• Developing reliability tests for long-running agents
• Identifying drift and degradation in agent performance
• Integrating safeguards for critical workflows

Quality Assurance Processes and Automation

• Constructing QA pipelines for ongoing evaluation
• Automating regression tests for agent updates
• Integrating QA into CI/CD and enterprise workflows

Advanced Techniques for Hallucination Reduction

• Employing prompting strategies to mitigate undesired outputs
• Implementing validation loops and self-check mechanisms
• Exploring model combinations to enhance reliability

Reporting, Monitoring, and Continuous Improvement

• Creating QA reports and agent scorecards
• Monitoring long-term behavior and error trends
• Refining evaluation frameworks for evolving systems

Summary and Next Steps