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Course Outline
Foundations of Mastra Debugging and Evaluation
- Understanding agent behavior models and failure modes for government applications
- Core debugging principles within Mastra to ensure reliable performance in public sector environments
- Evaluating deterministic and non-deterministic agent actions to enhance decision-making processes
Setting Up Environments for Agent Testing
- Configuring test sandboxes and isolated evaluation spaces to ensure secure and controlled testing environments for government use
- Capturing logs, traces, and telemetry for detailed analysis to support transparent and accountable operations
- Preparing datasets and prompts for structured testing to align with public sector requirements
Debugging AI Agent Behavior
- Tracing decision paths and internal reasoning signals to identify issues early in the development cycle
- Identifying hallucinations, errors, and unintended behaviors to maintain trust and reliability in government systems
- Using observability dashboards for root-cause investigation to ensure comprehensive issue resolution
Evaluation Metrics and Benchmarking Frameworks
- Defining quantitative and qualitative evaluation metrics to measure performance against public sector standards
- Measuring accuracy, consistency, and contextual compliance to ensure alignment with government objectives
- Applying benchmark datasets for repeatable assessment to support continuous improvement in government operations
Reliability Engineering for AI Agents
- Designing reliability tests for long-running agents to ensure sustained performance in critical public sector applications
- Detecting drift and degradation in agent performance to maintain high standards of service delivery
- Implementing safeguards for critical workflows to protect sensitive government operations
Quality Assurance Processes and Automation
- Building QA pipelines for continuous evaluation to support seamless integration with government systems
- Automating regression tests for agent updates to ensure ongoing compliance and reliability
- Integrating QA with CI/CD and enterprise workflows to enhance efficiency and accountability in public sector projects
Advanced Techniques for Hallucination Reduction
- Prompting strategies to reduce undesired outputs and improve the accuracy of government applications
- Validation loops and self-check mechanisms to enhance the reliability of AI agents in public sector environments
- Experimenting with model combinations to improve reliability and performance for government use
Reporting, Monitoring, and Continuous Improvement
- Developing QA reports and agent scorecards to provide transparent insights into system performance for government stakeholders
- Monitoring long-term behavior and error patterns to identify trends and areas for improvement in public sector operations
- Iterating on evaluation frameworks for evolving systems to ensure ongoing alignment with government needs
Summary and Next Steps
Requirements
- A comprehensive understanding of AI agent behavior and model interactions for government applications
- Practical experience in debugging or testing complex software systems
- Familiarity with observability and logging tools
Audience
- Quality Assurance (QA) engineers for government
- AI reliability engineers
- Developers responsible for agent quality and performance in public sector environments
21 Hours
