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Generative AI
generative ai agentic
Agentic AI Frameworks and Workflow Automation
★ 4.8 · 300 learners · Intermediate
Starts 8 Aug 2026 · ~1–2 months
Intermediate Level
Amazon Bedrock (Generative AI on AWS)
★ 4.8
(300 learners)
60 hrs total 1 modules
AWS Amazon Bedrock Docker Salesforce LangChain LangGraph
+8 more
Only 4 seats left
· Next batch starts 29 Aug 2026
- 60 hrs
- of content
- 1 modules
- in curriculum
- 16 projects
- hands-on builds
- Intermediate
- difficulty
Curriculum covers
Frameworks you'll master
AWS Amazon Bedrock Docker Salesforce LangChain LangGraph CrewAI AutoGen n8n OpenAI ChatGPT Pinecone FAISS MCP
Course Curriculum
1 modules · 60 hours · 24 live sessions
LangChain & Prompt Engineering 18 sessions
- Lang Chain Introduction
- Introduction
- Environment Installation
- Integration with Open AI & Key Generation
- Lang Chain Models Module Overview
- Models
- LLM’s (GPT3, BERT, BLOOM etc.)
- Chat Models
- Text Embedding Models
- Prompts
- Prompt Templates
- Example Selectors
- Output Parsers
- Chain
- Indexes
- Agents
- Memory
- LangChain – Memory Module Concept
- ConversationBufferMemory
- ConversationBufferWindowMemory
- EntityMemory
- VectorStateBackedMemory
- Lang Chain – Data Connection Module
- Document Loaders
- Document Transformers
- Text Embedding Models
- Vector Stores (Pinecone Vectorstores)
- Retrievers
- Lang Chain – Agents Module Concept
- Action Agents
- PlanandExecute Agents
- LLAMA 2 Introduction
- First Lang Chain Application – Chaining a simple prompt – GIST of LangChain Application
- Chains
- Agents
- Custom Agents
- Tools, Toolkit
- Output Parsers
- Prompt Template
- Chat Model
- Chains
- Working Summary Example
- ICE Breaker Real Work Generative AI Application
- Integrating LinkedIn Data Processing – Part1 Scrapping
- Integrating LinkedIn Data Processing – Part2 Agents Theory
- Integrating LinkedIn Data Processing – Part3 Tools, AgentType and initialize_agent
- Integrating LinkedIn Data Processing – Part4 Custom Agent Implementation & Testing
- Twitter Data Processing
- Twitter Data Processing – Part1: Scrapping
- Twitter Data Processing – Part2: Agents
- Full Stack App – Building our LLM powered by LangChain Full Stack Application
- The GIST of Embeddings, Vector Databases and, Vector DBQA chain and RetreivalQA
- Embeddings
- Vector Stores (Pinecone)
- RetreivalQA Chain
- LangChain Document Loaders
- LangChain Text Splitters
- Theoretical Introduction to embeddings, Vectorstores & RetreivalQA chain (RAG)
- Chat with PDF
- PyPDF Loader
- Local Vector Store (FAISS)
- RetreivalQA Chain
- Building a Documentation Assistant (Embeddings, Vector DB's, RetreivalQA, Memory
- Vector Databases
- RetreivalQA Chain
- Similarity Search
- LLM Memory
- Streamlit
- Building as AI LangChain Chat Assistant
- Building as AI LangChain Chat Assistant – Vectorstores Pinecone Ingestion
- Building as AI LangChain Chat Assistant – “Frontend” with Streamlit (UI)
- Building as AI LangChain Chat Assistant – Memory & ConverstationalRetreivalChain
- Chunking Strategies
- Building a slim ChatGPT CodeInterpreter (Advanced Agents, OpenAI Functions
- PythonREPL Agent
- CSV Agent
- Router Agent
- OpenAI Functions
- Prompt Engineering
- Zero Shot Prompt
- One Shot Prompt
- Few Shots Prompt
- Chain of Thought Prompting (COT)
- ReAct = COT+Actions
- Top Frameworks available to build AGENTIC AI Workflows
- AutoGEN
- LangGraph
- AutoGen Deep DIVE
- AI Agentic Design Patterns
- The Reflection Pattern
- Reflection Design Pattern Workflow Overview
- Adding the Reflection and Orchestration parts to the Workflow
- Tool Use Pattern
- Trip Planner Workflow
- Build AI Agents with Lang Graph
- Lang Graph – Overview & Key Concepts
- Lang Graph Core Concepts – Simple Flow Diagram
- Lang Graph Data and State – Overview
- Building a Simple Agent with Lang Graph
- Lang Graph Simple Bot – Streaming Values – ConsoleApp
- Adding Tools to our Basic Lang Graph Agent
- Adding Memory to our Agent State
- Adding HumanintheLoop to the AI Agent
- Crew AI (Low Code Framework)
- Crew AI Framework to build AI
- Building our First AI Agent
- Connect OpenSource Models like Llama3 with Crew AI
- Sales Agent
- Deep Research Agent with DeepSeekR1
- AI Social Media Agent
- AI Agent for Nutrition
- Auto GPT
- IBM BEE
- Auto GPT Framework to build AI
- Building our First AI Agent
- Connect OpenSource Models like Llama3 with Auto GPT
- IBM’s Bee Agent Framework + Groq
- Building our First AI Agent
- Connect OpenSource Models like Llama3 with Auto GPT
- N8N Workflow (No Code Framework)
- Getting Started with n8n, AI Agents, and AI Automations
- Build our first n8n workflow StepByStep
- Build our First Automations & AI Automations in n8n
- Helping Maria Build their First Telegram Chatbot with n8n
- Helping Maria Build their First Telegram Chatbot with n8n
- Exploring Node Types and Categories in n8n
- Understanding Triggers in n8n
- Introduction to Webhooks
- How n8n works with Data
- AI Agents, AI Assistants, RAG, MultiAgent Systems, and AI Automations
- AI Agents, AI Assistants, RAG, MultiAgent Systems, and RAG Explained in n8n
- Professional LinkedIn Post Generator
- Create an AI Story Generator with n8n
- Create a Fully Automated Onboarding System for New clients
- Create an AI Agent that track our Finances
- Build a powerful Real Estate AI Agent (StepbyStep Guide)
- n8n Automation turns any Product Image into Viral Ads!
- RAG & Research AI Agents
- How to build an AI Agent that remembers everything (Supabase VB + PostgreSQL)
- MCP (Model Context Protocol) & How to use it for AI Agents in n8n
- Create our MCP Server to turn AI Assistant into AI Agent (Claude & ChatGPT)
- MCP Toolkit – Learn how to use verified MCP Servers through Docker Desktop App
- Conversational Flow – Receptionist AI Voice Agent (Physical Therapy Clinic)
- SEO Agent for Keyword Research & Article Creation
- Projects:
- Automated document generation → Creates product manuals, release notes, and FAQs from technical specs or code changes.
- Content summarization → Converts long technical details into simplified, user
- friendly explanations.
- Version management → Tracks changes and updates documentation automatically with each release.
- Multiformat publishing → Generates content for web, PDF, knowledge bases, and chatbots.
- Search & retrieval → Makes documentation easily searchable with semantic understanding.
- Localization support → Translates documentation into multiple languages while preserving context.
Batch Schedule
⚡ Seats filling fast — only 4 remaining across all batches
| Batch Type | Schedule | Duration | Next Start Date | Availability |
|---|---|---|---|---|
| Weekend | Sat & Sun, 10am–1pm | 10 weeks | 29 Aug 2026 | Available |
| Weekday | Mon–Fri, 7–9pm | 8 weeks | 31 Aug 2026 | Available |
| Fast-track | Mon–Sun, 6 hours/day | 2 weeks | 7 Sept 2026 | Only 4 seats left |
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