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

Foundations of Mastra Debugging and Evaluation

• Comprehending agent behavior models and common failure patterns
• Core debugging principles specific to the Mastra ecosystem
• Evaluating both deterministic and non-deterministic agent actions

Establishing Agent Testing Environments

• Configuring test sandboxes and isolated evaluation zones
• Capturing logs, traces, and telemetry for granular analysis
• Curating datasets and prompts for structured testing protocols

Debugging AI Agent Behavior

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

Evaluation Metrics and Benchmarking Frameworks

• Defining quantitative and qualitative assessment metrics
• Measuring accuracy, consistency, and contextual adherence
• Utilizing benchmark datasets for repeatable performance assessment

Reliability Engineering for AI Agents

• Designing reliability tests for long-running agent sessions
• Detecting drift and performance degradation over time
• Implementing safeguards for mission-critical workflows

Quality Assurance Processes and Automation

• Constructing QA pipelines for continuous evaluation cycles
• Automating regression tests for agent iterations
• Integrating QA processes with CI/CD and enterprise-level workflows

Advanced Techniques for Hallucination Reduction

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

Reporting, Monitoring, and Continuous Improvement

• Creating comprehensive QA reports and agent scorecards
• Monitoring long-term behavioral trends and error patterns
• Refining evaluation frameworks to adapt to evolving systems

Summary and Next Steps