PART 1 – AGENTIC AI

🤖 Agentic AI Training – Course Overview

📘 Course Details – 10 Days

This course provides a comprehensive understanding of Agentic AI systems, covering:

Fundamentals of AI, ML, Deep Learning, and Generative AI
Design and architecture of AI agents and multi-agent systems
Frameworks such as LangChain, LangGraph, CrewAI, and AutoGen
Concepts like memory, planning, reasoning, and tool usage
Agentic RAG, workflows, observability, and deployment strategies
Hands-on development of autonomous AI agents
Real-world applications across industries (finance, healthcare, retail)
Advanced topics including Amazon Bedrock Agents, LangFlow, and protocols (MCP, Agent2Agent)

👉 The course focuses on practical implementation, scalability, and enterprise deployment of AI agents

👥 Who Should Attend

This course is ideal for:

AI Engineers & Machine Learning Engineers
Data Scientists & Generative AI Developers
Software Developers & Full-Stack Engineers
Solution Architects & Cloud Architects
Product Managers & AI Consultants
IT Professionals transitioning to AI
Startups and innovators building AI-driven products

🚀 Key Benefits of the Course

✅ Understand end-to-end Agentic AI architecture and workflows
✅ Gain hands-on experience in building autonomous and multi-agent systems
✅ Learn industry tools like LangChain, LangGraph, CrewAI, and Bedrock
✅ Design scalable, production-ready AI agents
✅ Apply AI agents to real-world business use cases
✅ Master Agentic RAG, observability, and optimization techniques
✅ Stay ahead with future trends and AI agent innovations
✅ Build skills for high-demand AI roles and enterprise AI projects

Basics of Artificial Intelligence

Machine Learning and Algorithms
Deep Learning and Algorithms
Generative AI
Evolution of AI Agents

Agentic AI and its Applications

Agentic AI overview
Agentic AI workflow
Agentic AI Frameworks – AutoGen, LangGraph, CrewAI
Design Principles for Agentic AI
Real World Applications across Industries
Case Studies

Building Blocks and Design Pattern of Agentic AI

Core Components of AI Agents
Large Language Models
Memory
Planning & Reasoning
Action and Tool Utilization
Environment Interaction
Self-Reflection
Agentic Design patterns for AI Agents
Tool Use Pattern
Planning Pattern
Multi-agent Pattern
UX Design strategies for Agentic AI
Scaling and deploying AI Agents

Multi-Agent systems

Overview and Architecture
Building efficient multi-agent systems
Practical Example of multi-agent coding assistant

Building Autonomous AI Agents

Characteristics
Building Blocks
Real-world Applications

Practical Implementation of AI Agents

Designing and deploying AI Agents
Optimizing AI Agents
Ethical and Responsible implementation
Real-world example of customer support agent

Real-world applications of AI Agents

AI Agents in Finance
AI agents in retail and e-commerce
AI Agents in healthcare
Case Studies
Benefits of AI Agents for Businesses
Emerging Applications of AI Agents

Future Trends in Ai Agents

Innovations in Agentic AI
Industry impact through Agentic AI
Building Trust in agentic AI
Implementation Challenges for AI Agents

PART 2 – AGENTIC AI

The AI-Agent Ecosystem

Types of Agents in Sense – Plan – Act Cycle
The lifecycle of an Agent
Types of Agentic System
Ethics in Agentic AI
Use cases, benefits and case studies
Popular Design Patterns

The LangChain Ecosystem

LangChain Integrations
Overview of LangGraph
Overview of LangServe
Overview of LangSmith
SPA Cycle in LangChain
Phases of LangChain
Examples

Understanding Agentic RAG

Architecture of Agentic RAG
Classification and Applications of Agentic RAG
Limitations
Enhancing Agentic RAG with cohere Integration

Multi-agent Workflows and Components

Agent Handoffs in Multi-Agent Systems
LangGraph : Control Flow and State Management
Multi-Agent Architectures
CrewAI – Components and Workflows
Competitive Analysis and Best Practices
Multi-agent Systems with LangGraph

AI Agent Observability

Hands-on with AgentOps
Advanced Tracing with LangSmith
Business Intelligence with LangFuse
Implementation Strategy and Tool selection

A Multi-Agent Healthcare System

Performance Measures and Metrics

PART 3 – Advanced AGENTIC AI

Review of Agentic AI

ReAct Framework
CodeAct Framework
Control vs. Autonomy
Memory – Tool Calling – Evaluation
Hands-on Exercises

Amazon Bedrock Agents – Hyperscaler Based Approach

Definition
Core Components of Bedrock Agents
Building a simple Agent
How Agents work
Enhancing Bedrock agents with Knowledge Bases
Testing and Troubleshooting
Customized Agent Orchestration

LANGFLOW – A Low-Code Solution for Building AI Agents

Components
Debugging and API usage
Real-world Applications and use cases

LANGGRAPH – A Full-Code Approach

Core Concepts
Create your Agent with LangGraph
Memory and Tools in LangGraph
Human-In-The-Loop
RAG Using LangGraph Agents
Building Agents to Production
LangGraph Workflows

Protocols enabling Agentic AI

Overview and Benefits of Model Context Protocol ( MCP )
MCP Server and Client
Agent2Agent Protocol
Agent Communication Protocol
Agent-User Interface Protocol
Comparative Analysis of Protocols

Comprehensive Agentic AI training program covering AI agents, multi-agent systems, LangChain, LangGraph, CrewAI, Agentic RAG, and real-world deployment. Learn to design, build, and scale autonomous AI solutions with hands-on projects and industry use cases.

Key Benefits

Master building production-ready autonomous and multi-agent AI systems using industry-standard tools (LangGraph, CrewAI, Bedrock, LangFlow, etc.)
Gain hands-on experience in agent design patterns, Agentic RAG, memory, tool use, observability, and human-in-the-loop workflows
Learn to apply Agentic AI to real business problems with practical examples and case studies
Understand ethics, scalability, performance metrics, and emerging protocols
Stay ahead of the curve with the latest innovations and future trends in Agentic AI
Walk away ready to design, deploy, and optimize AI agents in your own projects or organization

Training Courses

Useful Links

Contacts

Agentic AI Training Course