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Course Outline
Fundamentals of Mastra Debugging and Evaluation
- Analyzing agent behavior models and potential failure modes
- Core debugging principles specific to the Mastra framework
- Assessing both deterministic and non-deterministic agent actions
Establishing Agent Testing Environments
- Configuring test sandboxes and isolated evaluation spaces for government systems
- Capturing logs, traces, and telemetry data to support detailed analysis
- Preparing structured datasets and prompts for comprehensive testing
Debugging AI Agent Behavior
- Tracing decision paths and internal reasoning signals
- Identifying hallucinations, errors, and unintended behaviors in operational contexts
- Utilizing observability dashboards to facilitate root-cause investigation for government applications
Evaluation Metrics and Benchmarking Frameworks
- Defining quantitative and qualitative evaluation metrics aligned with public sector standards
- Measuring accuracy, consistency, and contextual compliance
- Applying benchmark datasets to enable repeatable assessment for government workloads
Reliability Engineering for AI Agents
- Designing reliability tests for long-running agents in critical infrastructure
- Detecting drift and degradation in agent performance
- Implementing safeguards for high-stakes government workflows
Quality Assurance Processes and Automation
- Building QA pipelines to support continuous evaluation of federal systems
- Automating regression tests for agent updates within secure environments
- Integrating QA processes with CI/CD and enterprise workflows for government agencies
Advanced Techniques for Hallucination Reduction
- Implementing prompting strategies to minimize undesired outputs in sensitive contexts
- Utilizing validation loops and self-check mechanisms for enhanced control
- Experimenting with model combinations to improve reliability for government use cases
Reporting, Monitoring, and Continuous Improvement
- Developing QA reports and agent scorecards for accountability
- Monitoring long-term behavior and error patterns to ensure operational integrity
- Iterating on evaluation frameworks to support evolving government systems
Summary and Next Steps
Requirements
- Proficiency in analyzing artificial intelligence agent behaviors and model interactions
- Demonstrated experience debugging or testing complex software systems
- Competence with observability and logging technologies
Audience
- Quality assurance engineers
- AI reliability specialists
- Developers responsible for ensuring agent quality and performance, for government operations
21 Hours
