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Course Outline
Introduction to Mastra
- Overview of TypeScript-based AI frameworks.
- Key features and advantages of Mastra.
- Installation procedures and project setup.
Understanding the Mastra Architecture
- Core components and system design principles.
- Architecture of agents, workflows, and memory.
- Integration points for APIs and LLMs.
Building AI Agents
- Creating basic agents using TypeScript.
- Utilizing tools and context for agent reasoning.
- Composing multi-step AI tasks.
Workflows and Automation
- Designing agent-driven workflows.
- Triggering and managing asynchronous tasks.
- Error handling and process control mechanisms.
RAG (Retrieval-Augmented Generation) Integration
- Implementing document retrieval and indexing.
- Connecting to external knowledge bases.
- Optimizing responses through contextual data.
Observability and Debugging
- Monitoring agent activity and logs.
- Performance profiling and optimization strategies.
- Debugging workflows and tracking outcomes.
Deployment and Scaling
- Deploying Mastra applications to production environments.
- Integration with cloud infrastructure.
- Best practices for security and scaling.
Best Practices and Enterprise Use Cases
- Considerations for governance, auditability, and reliability.
- Case studies from enterprise implementations.
- 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 and Large Language Model (LLM) concepts.
Target Audience
- Software engineers developing AI or automation solutions.
- Engineering leads responsible for building agent-driven systems.
- Developers interested in enterprise-grade TypeScript AI frameworks.
14 Hours
