Agentic AI Mastery Training

Learn to design, build and govern autonomous AI systems. This Agentic AI Mastery ICT training course covers AI agents, LangChain, RAG, LangGraph, frameworks, governance and hands-on implementation.

This online ICT training course is designed for professionals who want to build job-ready knowledge and practical skills in a structured way. The course combines clear theory, hands-on learning, assessments and a complete learning path that supports both individual learners and organizations investing in future-proof IT capabilities.

This LearningKit with more than 5 hours of learning is divided into three tracks:

Demo Agentic AI Mastery Training

Why follow this ICT training course?

Modern IT teams need people who understand automation, AI, cloud, DevOps, reliability and governance. This OEM training helps learners move beyond theory and apply the concepts in realistic professional environments. The result is a stronger foundation for improving IT operations, building reliable systems and supporting innovation.

What you will learn

• Think in agent architectures, behaviors and autonomous decision-making
• Select and apply agentic AI frameworks and design patterns
• Understand tool use, memory, reasoning, RAG and multi-agent collaboration
• Design reliable, explainable and scalable AI agent systems
• Manage risks, limitations and governance for autonomous AI

Training structure

Track 1: Agentic AI - Foundations, Architectures, and Frameworks

Build a strong conceptual and architectural foundation for agentic AI systems, including autonomy, perception, reasoning, memory, feedback, adaptation, frameworks and reusable design patterns.

• Inside Agentic AI: Foundations and Frontiers
• Inside Agentic AI: Core Architecture of Agentic Systems
• Inside Agentic AI: Popular Frameworks
• Agentic AI Design Patterns: Reusable Blueprints for Smarter Systems

Inside Agentic AI: Foundations and Frontiers

Course: 40 Minutes

• Course Overview
• What Makes AI Agentic?
• Core Properties of Agentic AI Systems
• Knowledge Check: Reviewing the Core Properties of Agentic AI Systems
• Inside the Agentic Loop
• Knowledge Check: Assessing Information Flow in Agentic AI Systems
• Step-by-Step Agentic Flow: Case Study
• Where Agents Come Alive: Real-World Applications
• Limitations and Risks of agentic AI
• Course Summary

Inside Agentic AI: Core Architecture of Agentic Systems

Course: 35 Minutes

• Course Overview
• How Agentic AI Systems Think and Act: A Visual Walkthrough
• Knowledge Check: Understanding the Agentic AI Loop: From Perception to Feedback
• Perception: Understanding Inputs Intelligently
• Knowledge Check: Identifying How Agents Prepare for Planning
• How Agentic AI Systems Use Memory
• Knowledge Check: Understanding How Memory Contributes to Learning
• Planning with Agentic AI Systems
• Using Tools with Agentic AI
• Knowledge Check: Identifying How Agentic AI Selects Task-Relevant Tools
• Agentic AI and Environmental Integration
• How Agentic AI Systems Improve Over Time
• Reactive, Deliberative, and Learning-Based Architectures
• Course Summary

Inside Agentic AI: Popular Frameworks

Course: 33 Minutes

• Course Overview
• LangChain: Backbone of Custom AI Agents
• LangGraph: Structured Multi-Agent Workflows
• Knowledge Check: Identifying LangGraph Agent Workflows
• AutoGen: Simplified Multi-Agent Collaboration
• Knowledge Check: Reviewing AutoGen for Multi-Agent Setups
• CrewAI: Structured Role-Based Agent Collaboration
• Knowledge Check: Identifying Role-Based Agent Workflows in CrewAI
• LlamaIndex: Connecting Agents to External Knowledge
• Knowledge Check: Reviewing How LlamaIndex Enhances Workflows
• Haystack: Modular RAG Pipelines
• Knowledge Check: Reviewing Haystack's Support for Building Pipelines
• n8n: No-Code Automation for Agent Actions
• Course Summary

Agentic AI Design Patterns: Reusable Blueprints for Smarter Systems

Course: 49 Minutes

• Course Overview
• Why Design Patterns Matter in Agentic System
• Agentic Pattern Anatomy: Trigger, Flow, Feedback, and Role
• The ReAct Loop: Reasoning + Action + Feedback
• Knowledge Check: Identifying When Not to Use ReAct: Pattern Limitations
• Reflex Agents vs. Deliberative Agents
• Chain-of-Thought and Tree-of-Thought Patterns
• Knowledge Check: Identifying Characteristics of Chain-of-Thought Reasoning
• Role-Based Agent Collaboration
• Multi-Agent Task Routing and Arbitration
• Feedback-Driven Agent Loops
• Agentic AI: Reflection and Self-Critique Patterns
• Course Summary

Assessment: Final Exam: Agentic AI - Foundations, Architectures, and Frameworks

Track 2: Agentic AI in Action

Apply agentic AI concepts in practice with LangChain, RAG and LangGraph to build tool-enabled agents, memory-aware chatbots, controlled workflows and scalable agentic systems.

• Agentic AI in Action: Hands-On with LangChain
• Agentic AI in Action: Tool Use with LangChain
• RAG with LangChain: Foundations to Practice
• Agentic AI in Action: Controlled Workflows with LangGraph

Agentic AI in Action: Hands-On with LangChain

Course: 20 Minutes

• Course Overview
• Applying a Simple Chain with Gemini
• Designing Prompt Templates and Constructing SequentialChains in LangChain
• Implementing a Basic Chatbot Without Memory in LangChain
• Integrating ConversationBufferMemory to Enhance Chatbots
• Differentiating Context with ConversationBufferWindowMemory
• Course Summary

Agentic AI in Action: Tool Use with LangChain

Course: 31 Minutes

• Course Overview
• Building a Calculator Tool in LangChain
• Using Built-In Tools in LangChain: Search and Wikipedia
• Creating a Custom Weather Tool in LangChain
• Combining Multiple Tools in LangChain Agents
• Course Summary

RAG with LangChain: Foundations to Practice

Course: 1 Hour, 4 Minutes

• Course Overview
• RAG Essentials: Why We Need It?
• Knowledge Check: Assessing the Fundamentals of RAG
• Anatomy of a RAG Pipeline
• Knowledge Check: Identifying the Anatomy of a Rag Pipeline
• RAG with LangChain
• Knowledge Check: Evaluating How RAG Integrates with LangChain
• Preparing Data and Retrieval for a RAG System in LangChain
• Connecting the Language Model and Building the Complete RAG Pipeline
• Knowledge Check: Assessing How to Build a RAG Pipeline in LangChain
• Building a Conversational RAG System with LangChain
• Knowledge Check: Evaluating How to Build a Conversational RAG System Using LangChain
• Implementing Multi-Source RAG
• Course Summary

Agentic AI in Action: Controlled Workflows with LangGraph

Course: 1 Hour, 2 Minutes

• Course Overview
• LangGraph Core Concepts
• Understanding State Flow
• Knowledge Check: Reviewing How State Flows in LangGraph
• Understanding LangGraph Workflow Patterns
• How Conditional Routing Works
• Knowledge Check: Reviewing How Conditional Routing Works
• Building Your First StateGraph1
• Building Conditional Edges and Branching Logic in LangGraph
• Testing and Running Your Conditional Routing Workflow
• Course Summary

Assessment: Final Exam: Agentic AI in Action

Who should attend?

This course is ideal for developers, software architects, AI practitioners, data professionals, technical consultants and IT leaders who want to move from traditional AI workflows to agent-driven systems.

Outcome

After completion, you can analyze, design and implement agentic AI systems with modern frameworks while addressing reliability, scalability, explainability and governance.

Why choose OEM Office Elearning Menu?

• Self-paced online ICT training
• Practical course content with clear learning objectives
• Suitable for professionals, teams and organizations
• Focused on modern IT, cloud, AI and automation skills
• Supports career growth, certification preparation and professional development

Start Agentic AI Mastery today and build the skills needed for the next generation of AI-driven IT solutions.