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

Foundations of Mastra Debugging and Evaluation

• Comprehending agent behavior models and failure modes.
• Core debugging principles within the Mastra framework.
• Assessing deterministic versus non-deterministic agent actions.

Establishing Environments for Agent Testing

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

Debugging AI Agent Behavior

• Tracking decision paths and internal reasoning signals.
• Recognizing hallucinations, errors, and unintended behaviors.
• 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 reproducible assessment.

Reliability Engineering for AI Agents

• Designing reliability tests for long-running agents.
• Detecting performance drift and degradation.
• Implementing safeguards for critical workflows.

Quality Assurance Processes and Automation

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

Advanced Techniques for Hallucination Reduction

• Employing 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

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

Summary and Next Steps