Course Outline

Understanding Mastra Architecture and Operational Concepts

  • Core components and their production roles
  • Supported integration patterns for enterprise environments
  • Security and governance considerations

Preparing Environments for Agent Deployment

  • Configuring container runtime environments
  • Preparing Kubernetes clusters for AI agent workloads
  • Managing secrets, credentials, and config stores

Deploying Mastra AI Agents

  • Packaging agents for deployment
  • Using GitOps and CI/CD for automated delivery
  • Validating deployments through structured testing

Scaling Strategies for Production AI Agents

  • Horizontal scaling patterns
  • Autoscaling with HPA, KEDA, and event-driven triggers
  • Load distribution and request-handling strategies

Observability, Monitoring, and Logging for AI Agents

  • Telemetry instrumentation best practices
  • Integrating Prometheus, Grafana, and logging stacks
  • Tracking agent performance, drift, and operational anomalies

Optimizing Performance and Resource Efficiency

  • Profiling agent workloads
  • Improving inference performance and reducing latency
  • Cost-optimization approaches for large-scale agent deployments

Reliability, Resilience, and Failure Handling

  • Designing for resiliency under load
  • Implementing circuit-breaking, retries, and rate limiting
  • Disaster recovery planning for agent-based systems

Integrating Mastra into Enterprise Ecosystems

  • Interfacing with APIs, data pipelines, and event buses
  • Aligning agent deployments with enterprise DevSecOps
  • Adapting architectures to existing platform environments

Summary and Next Steps

Requirements

  • An understanding of containerization and orchestration
  • Experience with CI/CD workflows
  • Familiarity with AI model deployment concepts

Audience

  • DevOps engineers
  • Backend developers
  • Platform engineers responsible for AI workloads
 21 Hours

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