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

Foundations of Mastra Debugging and Evaluation

• Analyzing agent behavior models and identifying failure modes.
• Core principles of debugging within the Mastra ecosystem.
• Assessing 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 in-depth analysis.
• Curating datasets and prompts for structured testing scenarios.

Debugging AI Agent Behavior

• Tracing decision paths and internal reasoning signals.
• Detecting 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.
• Utilizing benchmark datasets for repeatable assessments.

Reliability Engineering for AI Agents

• Designing reliability tests for long-running agent processes.
• Identifying drift and performance degradation in agents.
• Implementing safeguards for critical workflow operations.

Quality Assurance Processes and Automation

• Constructing QA pipelines for continuous evaluation.
• Automating regression tests for agent updates.
• Integrating QA practices with CI/CD and enterprise workflows.

Advanced Techniques for Hallucination Reduction

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

Reporting, Monitoring, and Continuous Improvement

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

Summary and Next Steps