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

Foundations of Mastra Debugging and Evaluation

• Analyzing agent behavior models and common failure modes
• Core debugging principles within the Mastra framework
• Evaluating both deterministic and non-deterministic agent actions

Configuring Environments for Agent Testing

• Setting up test sandboxes and isolated evaluation spaces
• Capturing logs, traces, and telemetry for granular analysis
• Preparsing datasets and prompts for structured testing

Debugging AI Agent Behavior

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

Evaluation Metrics and Benchmarking Frameworks

• Defining quantitative and qualitative evaluation criteria
• 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 minimize undesired outputs
• Implementing validation loops and self-check mechanisms
• Experimenting with model combinations to enhance 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