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

Foundations of Mastra Debugging and Evaluation

• Analyzing agent behaviour models and identifying failure modes
• Core debugging principles embedded within Mastra
• Assessing both deterministic and non-deterministic agent actions

Establishing Environments for Agent Testing

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

Debugging AI Agent Behaviour

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

Evaluation Metrics and Benchmarking Frameworks

• Defining quantitative and qualitative assessment metrics
• Measuring accuracy, consistency, and adherence to context
• Utilizing benchmark datasets for repeatable and reliable assessment

Reliability Engineering for AI Agents

• Architecting reliability tests for long-running agent sessions
• Identifying performance drift and degradation patterns
• Implementing safeguards for mission-critical workflows

Quality Assurance Processes and Automation

• Constructing QA pipelines for continuous evaluation
• Automating regression testing for agent updates
• Integrating QA processes with CI/CD and enterprise operations

Advanced Techniques for Hallucination Reduction

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

Reporting, Monitoring, and Continuous Improvement

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

Summary and Next Steps