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Course Outline
Understanding Mastra Architecture and Operational Concepts for Government
- Core components and their production roles
- Supported integration patterns for enterprise environments
- Security and governance considerations
Preparing Environments for Agent Deployment for Government
- Configuring container runtime environments
- Preparing Kubernetes clusters for AI agent workloads
- Managing secrets, credentials, and configuration stores
Deploying Mastra AI Agents for Government
- Packaging agents for deployment
- Using GitOps and CI/CD for automated delivery
- Validating deployments through structured testing
Scaling Strategies for Production AI Agents for Government
- Horizontal scaling patterns
- Autoscaling with HPA, KEDA, and event-driven triggers
- Load distribution and request-handling strategies
Observability, Monitoring, and Logging for AI Agents for Government
- Telemetry instrumentation best practices
- Integrating Prometheus, Grafana, and logging stacks
- Tracking agent performance, drift, and operational anomalies
Optimizing Performance and Resource Efficiency for Government
- Profiling agent workloads
- Improving inference performance and reducing latency
- Cost-optimization approaches for large-scale agent deployments
Reliability, Resilience, and Failure Handling for Government
- Designing for resiliency under load
- Implementing circuit-breaking, retries, and rate limiting
- Disaster recovery planning for agent-based systems
Integrating Mastra into Enterprise Ecosystems for Government
- Interfacing with APIs, data pipelines, and event buses
- Aligning agent deployments with enterprise DevSecOps
- Adapting architectures to existing platform environments
Summary and Next Steps for Government
Requirements
- An understanding of containerization and orchestration technologies.
- Experience with continuous integration and continuous deployment (CI/CD) workflows.
- Familiarity with the concepts and practices of AI model deployment.
Audience
- DevOps engineers for government agencies and departments.
- Backend developers supporting public sector applications.
- Platform engineers responsible for managing AI workloads in governmental settings.
21 Hours
