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

Core Principles of Mastra Debugging and Evaluation

• Analyzing agent behavior models and common failure modes
• Foundational debugging principles within the Mastra framework
• Assessing both deterministic and non-deterministic agent actions

Configuring Environments for Agent Testing

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

Debugging AI Agent Behavior

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

Evaluation Metrics and Benchmarking Frameworks

• Establishing quantitative and qualitative evaluation metrics
• Measuring accuracy, consistency, and contextual adherence
• Utilizing benchmark datasets for repeatable and reliable assessments

Reliability Engineering for AI Agents

• Designing reliability tests for long-running agent processes
• Identifying performance drift and degradation in agent outputs
• Implementing safeguards for critical business workflows

Quality Assurance Processes and Automation

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

Advanced Strategies for Hallucination Reduction

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

Reporting, Monitoring, and Continuous Improvement

• Generating QA reports and comprehensive agent scorecards
• Monitoring long-term behavior and recurring error patterns
• Refining evaluation frameworks to accommodate evolving systems

Summary and Recommended Next Steps