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Course Outline
Introduction to Mastra
- Survey of TypeScript-based AI frameworks
- Core features and benefits of Mastra
- Installation procedures and project initialization
Understanding Mastra’s Architecture
- System design and core components
- Architecture of memory, workflows, and agents
- Integration points for APIs and LLMs
Developing AI Agents
- Constructing basic agents with TypeScript
- Leveraging tools and context for agent reasoning
- Orchestrating complex, multi-step AI tasks
Workflows and Automation
- Designing workflows driven by agents
- Managing and triggering asynchronous tasks
- Implementing process control and error handling
Integrating RAG (Retrieval-Augmented Generation)
- Setting up document indexing and retrieval
- Linking to external knowledge bases
- Refining responses through contextual data utilization
Observability and Debugging
- Tracking logs and monitoring agent activity
- Conducting performance profiling and optimization
- Diagnosing workflow issues and monitoring outcomes
Deployment and Scaling
- Rolling out Mastra applications to production environments
- Integrating with cloud infrastructure
- Adhering to best practices for security and scalability
Best Practices and Enterprise Scenarios
- Addressing reliability, auditability, and governance
- Analyzing case studies from enterprise deployments
- Exploring future directions and community roadmap
Summary and Next Steps
Requirements
- Foundational knowledge of JavaScript and TypeScript
- Prior experience with REST APIs or backend development
- Basic understanding of AI or Large Language Model (LLM) concepts
Target Audience
- Software engineers developing AI or automation solutions
- Engineering leads responsible for building agent-driven systems
- Developers investigating enterprise-grade TypeScript AI frameworks
14 Hours
