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

Foundations of Mastra Debugging and Evaluation

• Comprehending agent behavior models and common failure modes
• Essential debugging principles within the Mastra ecosystem
• Assessing both deterministic and non-deterministic agent actions

Preparing Environments for Agent Testing

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

Debugging AI Agent Behavior

• Tracking decision paths and internal reasoning signals
• Detecting hallucinations, errors, and unintended actions
• Leveraging observability dashboards for root-cause analysis

Evaluation Metrics and Benchmarking Frameworks

• Defining quantitative and qualitative performance metrics
• Measuring accuracy, consistency, and contextual adherence
• Utilizing benchmark datasets for consistent assessment

Reliability Engineering for AI Agents

• Creating reliability tests for long-running agent sessions
• Identifying drift and performance degradation in agents
• Establishing safeguards for critical operational workflows

Quality Assurance Processes and Automation

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

Advanced Techniques for Reducing Hallucinations

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

Reporting, Monitoring, and Continuous Improvement

• Generating QA reports and agent performance scorecards
• Monitoring long-term behavior and recurring error patterns
• Refining evaluation frameworks as systems evolve

Summary and Next Steps