Learning Objectives:**
- Understand the evolution from traditional AI to Generative AI
- Explore different types of LLMs (GPT, Claude, LLaMA, etc.)
- Analyze real-world applications and use cases
Learning Objectives:**
- Define autonomous agents and their characteristics
- Understand agent vs. tool distinction
- Explore agent architectures (reactive, deliberative, hybrid)
Learning Objectives:**
- Survey no-code/low-code agent platforms
- Understand when to use no-code vs. custom development
- Explore integration capabilities and limitations
Learning Objectives:
- Python ecosystem for AI development
- Essential libraries overview (requests, asyncio, json, logging)
- Development environment setup best practices
*Learning Objectives:**
- Understand LangChain architecture and components
- Explore chains, agents, and memory systems
- Review integration ecosystem
**Learning Objectives:**
- Advanced prompting techniques (Chain-of-Thought, Tree of Thoughts)
- Prompt optimization and testing methodologies
- Safety and bias considerations in prompting
**Learning Objectives:**
- Retrieval-Augmented Generation principles
- Vector databases and similarity search
- RAG evaluation methodologies
**Learning Objectives:**
- Reasoning and Acting (ReAct) methodology
- Combining reasoning traces with action execution
- Error handling and recovery in reasoning loops
**Learning Objectives:**
- Comparative analysis of major agentic frameworks
- Framework selection criteria for different use cases
- Migration strategies between frameworks
**Learning Objectives:**
- Industry-standard architectural patterns
- Scalability and performance considerations
- Security and governance in agentic systems
**Hands-On Projects:**
- Create a customer service chatbot using ChatGPT GPTs
- Build a data analysis agent with Microsoft Copilot Studio
- Deploy and test agent performance
**Hands-On Projects:**
- Set up complete Python development environment
- Build a simple CLI-based agent using OpenAI API
- Implement basic conversation memory
**Hands-On Projects:**
- Design and implement agent architecture from ground up
- Create modular component system
- Implement decision-making logic and tool integration
Hands-On Projects:**
- Build agents using LangChain's agent framework
- Implement custom tools and chains
- Create memory-enabled conversational agents
**Hands-On Projects:**
- Implement ReAct agents with complex reasoning tasks
- Build multi-step problem-solving workflows
- Create reasoning trace visualization
**Hands-On Projects:**
- Design state-based agent workflows using LangGraph
- Implement conditional branching and error recovery
- Build agent supervision and monitoring
Hands-On Projects:**
- Create conversational multi-agent systems
- Implement role-based agent interactions
- Build collaborative problem-solving scenarios
Hands-On Projects:**
- Design hierarchical agent teams using CrewAI
- Implement task delegation and coordination
- Build project management agent crews
Hands-On Projects:**
- Build self-improving RAG systems with agent oversight
- Implement dynamic knowledge base updates
- Create RAG quality assessment agents
Hands-On Projects:**
- Design complex multi-agent ecosystems
- Implement agent communication protocols
- Build distributed problem-solving systems
Hands-On Projects:**
- Implement self-reflective agent architectures
- Build planning and goal-setting capabilities
- Create meta-learning agent systems
Hands-On Projects:**
- Design and implement comprehensive agentic system
- Deploy to production with monitoring and scaling
- Create documentation and maintenance procedures
