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

Basics of Mastra Debugging and Evaluation

• Analyzing agent behavior models and common failure patterns
• Essential debugging principles specific to Mastra
• Assessing both deterministic and non-deterministic agent actions

Configuring Environments for Agent Testing

• Setting up test sandboxes and isolated evaluation zones
• Recording logs, traces, and telemetry data for in-depth analysis
• Compiling datasets and prompts for systematic testing

Troubleshooting AI Agent Behavior

• Tracking decision pathways and internal reasoning signals
• Detecting hallucinations, mistakes, and unexpected behaviors
• Leveraging observability dashboards to investigate root causes

Assessment Metrics and Benchmarking Frameworks

• Establishing quantitative and qualitative evaluation criteria
• Measuring precision, consistency, and adherence to context
• Utilizing benchmark datasets for reproducible assessments

Reliability Engineering for AI Agents

• Creating reliability tests for extended agent operations
• Identifying drift and performance degradation in agents
• Introducing safeguards for mission-critical workflows

Quality Assurance Processes and Automation

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

Advanced Strategies for Reducing Hallucinations

• Employing prompting techniques to minimize undesired outputs
• Implementing validation loops and self-check mechanisms
• Experimenting with model ensembles to boost reliability

Reporting, Monitoring, and Continuous Improvement

• Creating QA reports and agent performance scorecards
• Monitoring long-term behavioral trends and error patterns
• Refining evaluation frameworks as systems evolve

Conclusion and Next Steps