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

Basics of Mastra Debugging and Evaluation

• Exploring agent behavior models and potential failure modes.
• Key debugging principles within the Mastra framework.
• Assessing both deterministic and non-deterministic agent actions.

Establishing Agent Testing Environments

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

Debugging AI Agent Conduct

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

Evaluation Metrics and Benchmarking Systems

• Creating quantitative and qualitative evaluation metrics.
• Assessing accuracy, consistency, and contextual compliance.
• Utilizing benchmark datasets for reproducible assessment.

AI Agent Reliability Engineering

• Creating reliability tests for long-duration agents.
• Identifying drift and performance degradation in agents.
• Deploying safeguards for critical workflows.

QA Processes and Automation

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

Advanced Methods for Mitigating Hallucinations

• Prompting techniques to minimize unwanted outputs.
• Implementing validation loops and self-check mechanisms.
• Testing model combinations to enhance reliability.

Reporting, Monitoring, and Continuous Optimization

• Generating QA reports and agent scorecards.
• Monitoring long-term behavior and error trends.
• Refining evaluation frameworks as systems evolve.

Wrap-up and Future Directions