Agentic AI
This course delves into the realm of Agentic AI, focusing on the development and deployment of intelligent, autonomous agents capable of performing complex tasks. Participants will explore various frameworks and tools, including CrewAI, LangChain, LangGraph, and Knowledge Graphs, to build and manage AI agents effectively.
Skills You'll Acquire
Agent Development
Design Patterns
Knowledge Graphs
System Integration
AI Ethics
Instructor-led sessions (live or virtual)
Hands-on labs and real-world projects
Interactive discussions and assessments
Collaborative group work
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Proficiency in Python programming
Understanding of AI/ML concepts
Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch)
Basic knowledge of natural language processing
Understand the fundamentals and significance of Agentic AI
Develop and deploy AI agents using frameworks like CrewAI and LangChain
Implement agentic design patterns for enhanced AI behavior
Utilize knowledge graphs to enrich agent intelligence
Integrate AI agents into existing systems and workflows
Address ethical considerations and ensure robust AI agent performance
Curriculum
This Course contains 7 Modules.
Overview of Agentic AI and its applications
Historical context and evolution
Key concepts: autonomy, adaptability, and collaboration
Introduction to CrewAI: Structure and functionalities
Exploring LangChain for agent development
Understanding LangGraph and its applications
Hands-on: Setting up development environments
Common design patterns in Agentic AI
Implementing reflection and planning in agents
Multi-agent collaboration strategies
Case studies: Successful agentic design implementations
Introduction to knowledge graphs
Integrating knowledge graphs with AI agents
Enhancing agent decision-making with structured data
Practical session: Building a simple knowledge graph
Utilizing LangGraph's functional API
Exploring the LangChain ecosystem
Developing custom GPTs for specialized tasks
Hands-on: Creating an agent with LangChain and LangGraph
Strategies for integrating AI agents into existing systems
Deployment considerations and best practices
Monitoring and maintaining agent performance
Hands-on: Deploying an AI agent in a simulated environment
Ethical considerations in Agentic AI
Ensuring fairness and transparency
Addressing security and privacy concerns
Discussion: Case studies on ethical dilemmas in AI
Designing and developing a comprehensive AI agent
Incorporating learned frameworks and design patterns
