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Course Outline
Introduction to CrewAI and Multi-Agent Architecture
- Overview of CrewAI concepts and architecture
- Understanding agent roles and flows
- Use cases and design patterns
Designing Custom Agents and Tools
- Defining agent goals, memory, and behavior
- Creating and integrating custom tools
- Tool abstraction and modular design
Advanced Agent Collaboration
- Sequencing and synchronization of tasks
- Nested and parallel flows
- Multi-agent decision making
API and System Integration
- Calling external APIs from agents
- Incorporating real-time data sources
- Building pipelines and dynamic inputs
Event-Driven Orchestration
- Trigger-based workflows and custom events
- Error handling and fallback logic
- Using webhooks and schedulers
Monitoring, Testing, and Optimization
- Observing agent behavior and performance
- Debugging workflows and logging
- Scaling strategies and optimization tips
Practical Implementation and Case Studies
- Implementing a domain-specific use case
- Case study: enterprise automation with CrewAI
- Lessons learned and best practices
Summary and Next Steps
Requirements
- Experience with Python programming
- Understanding of AI and machine learning fundamentals
- Familiarity with API integration and software architecture concepts
Audience
- AI engineers
- Researchers
- Software architects
14 Hours
