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

Foundations of Debugging and Evaluation in Mastra

• Comprehending agent behavior models and common failure modes
• Core debugging principles specific to the Mastra framework
• Assessing both deterministic and non-deterministic agent actions

Establishing Environments for Agent Testing

• Setting up test sandboxes and isolated evaluation spaces
• Collecting logs, traces, and telemetry data for in-depth analysis
• Curating datasets and prompts for structured testing procedures

Debugging AI Agent Behavior

• Tracing decision paths and internal reasoning signals
• Detecting hallucinations, errors, and unintended behavioral outcomes
• Leveraging observability dashboards for root-cause investigation

Evaluation Metrics and Benchmarking Frameworks

• Defining both quantitative and qualitative evaluation metrics
• Measuring accuracy, consistency, and contextual compliance
• Utilizing benchmark datasets for repeatable and reliable assessment

Reliability Engineering for AI Agents

• Designing reliability tests for long-running agent processes
• Identifying performance drift and degradation in agent output
• Implementing safeguards for critical business workflows

Quality Assurance Processes and Automation

• Constructing QA pipelines for continuous evaluation
• Automating regression tests to support agent updates
• Integrating QA processes with CI/CD and enterprise-level workflows

Advanced Techniques for Hallucination Reduction

• Employing prompting strategies to minimize undesired outputs
• Incorporating validation loops and self-check mechanisms
• Testing model combinations to enhance overall reliability

Reporting, Monitoring, and Continuous Improvement

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

Summary and Next Steps