Module 1: Agentic AI Essentials
Agentic AI Introduction
AI Agents Agentic AI
Comparison: Agentic AI, Generative AI, and Traditional AI
Agentic AI Building Blocks
Autonomous Agents
Human in the Loops Systems
Single and Multi Agent AI Systems
Agentic AI Frameworks Overview
Ethical and Responsible AI
Agentic AI Best Practices
AI Implementation Success Stories: Case Studies
Module 2: Agentic AI: Architectures and Design Patterns
Agentic AI Architecture
Agentic Architecture Types
Key Components of the Agentic AI Framework
Perception Module
Cognitive Module
Action Module
Learning Module
Collaboration Module
Security Module
Agentic AI Design Patterns
Reflection Pattern
Tool Use Pattern
Planning Pattern
ReAct (Reasoning and Acting) and ReWOO (Reasoning with Open Ontology)
Multi Agent Pattern
Design Considerations
Module 3: Working with LangChain and LCEL
Components and Modules
Data Ingestion and Document Loaders
Text Splitting
Embeddings
Integration with Vector Databases
Introduction to Langchain Expression Language (LCEL)
Runnables
Chains
Building and Deploying with LCEL
Deployment with Langserve
Module 4: Building AI Agents with LangGraph
Introduction to LangGraph
State and Memory
State Schema
State Reducer
Multiple Schemas
Trim and Filter Messages
Memory and External Memory
UX and Human-in-the-Loop (HITL)
Building Agent with LangGraph
Long Term Memory
Short Long Term Memory
Memory Schema
Deployment
Module 5: Implementing Agentic RAG
What is Agentic RAG?
Agentic RAG Traditional RAG
Agentic RAG Architecture and Components
Understanding Adaptive RAG
Variants of Agentic RAG
Applications of Agentic RAG
Agentic RAG with Llamaindex
Agentic RAG with Cohere
Module 6: Developing AI Agents with Phidata
Module 7: Multi Agent Systems with LangGraph and CrewAI
Multi Agent Systems
Multi Agent Workflows
Collaborative Multi Agents
Multi Agent Designs
Multi Agent Workflow with LangGraph
CrewAI Introduction
CrewAI Components
Setting up CrewAI environment
Module 8: Advanced Agent Development with Autogen
Module 9: AI Agent Observability and AgentOPs
AI Agent Observability and AgentOPs
Langfuse Dashboard
Tracing
Evaluation
Managing Prompts
Experimentation
AI Observability with Langsmith
Setting up Langsmith
Managing Workflows with Langsmith
AgentOps Practical Implementation
Module 10: Building AI Agents with No/Low- Code Tools
Introduction to No-Code/Low-Code AI
Benefits and Challenges of No-Code AI Development
Key Components of No-Code AI Platforms
Building AI Workflows Without Coding
Designing AI Agents with Drag-and-Drop Interfaces
Integrating No-Code AI with Existing Systems
Customizing and Fine-Tuning AI Solutions
Optimizing Performance and Efficiency in No-Code AI
Security and Compliance Considerations in No-Code AI
Best Practices for Deploying No-Code AI Solutions
Real-World Use Cases and Applications of No-Code AI
Scaling and Future Trends in No-Code AI
