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Duration 21 hours
Course Outline
Fundamental Principles of Debugging and Evaluation in Mastra
- Analyzing agent behavioral models and identifying failure mechanisms
- Core diagnostic standards within the Mastra architecture
- Assessing deterministic and non-deterministic agent operations
Configuring Environments for Agent Validation
- Establishing secure test sandboxes and isolated evaluation zones
- Acquiring logs, execution traces, and telemetry data for comprehensive analysis
- Curating datasets and prompts to facilitate structured testing
Diagnosing AI Agent Performance
- Tracking decision pathways and internal reasoning indicators
- Detecting hallucinations, procedural errors, and unintended outcomes
- Utilizing observability interfaces for root-cause analysis
Metrics for Evaluation and Benchmarking Standards
- Defining quantitative and qualitative assessment criteria
- Measuring precision, consistency, and adherence to contextual requirements
- Utilizing benchmark datasets for reproducible assessments
Reliability Engineering for AI Agents
- Constructing reliability protocols for long-duration agent tasks
- Identifying performance drift and degradation in agent operations
- Deploying safeguards for mission-critical workflows
Quality Assurance Protocols and Automation
- Developing QA pipelines for sustained evaluation
- Automating regression testing for agent modifications
- Integrating QA processes with CI/CD and enterprise operational workflows
Advanced Strategies for Mitigating Hallucinations
- Employing prompting techniques to minimize erroneous outputs
- Implementing validation loops and self-verification mechanisms
- Testing model combinations to enhance operational reliability
Reporting, Surveillance, and Continuous Optimization
- Producing QA reports and agent performance scorecards
- Monitoring longitudinal behavior and error trends
- Refining evaluation frameworks for evolving systems
Conclusion and Subsequent Actions
Requirements
- A working knowledge of AI agent behavior and model interactions
- Experience with debugging or testing complex software systems
- Familiarity with observability or logging tools
Audience
- QA engineers
- AI reliability engineers
- Developers responsible for agent quality and performance
