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🤖 Agentic AI Track Intermediate–Advanced 11 Modules + Bonus

Agentic AI Training Master AI Agents

Build state-of-the-art autonomous AI agents from scratch. Master LangChain, LangGraph, CrewAI, Autogen, and Phidata frameworks. Implement Agentic RAG, multi-agent systems, AI observability with Langsmith and AgentOPs, and deploy AI agents on AWS Bedrock, Azure OpenAI, and GCP Vertex AI.

schedule80 Hours
science20+ Labs
workspace_premium5+ Projects
languageEnglish
terminalHands-on Labs
starstarstarstarstar
4.9 (38 reviews) · 950+ enrolled
person Created by Rahul Sharma · Senior AI Engineer & Agentic Systems Architect, 10+ years experience
boltEnroll Now — ₹26,999

What you'll learn

check_circle Build autonomous AI agents using LangGraph, CrewAI, Autogen, and Phidata with real-world use cases
check_circle Implement Agentic RAG with LlamaIndex and Cohere — intelligent retrieval, adaptive RAG, and document analysis
check_circle Design and orchestrate multi-agent systems with LangGraph and CrewAI — collaborative, parallel, and hierarchical workflows
check_circle Apply Agentic AI design patterns: Reflection, Tool Use, Planning, ReAct, ReWOO, and Multi-Agent patterns
check_circle Monitor and observe AI agents with Langfuse, Langsmith, and AgentOPs — tracing, evaluation, and performance management
check_circle Build AI agents using No/Low-Code platforms: Wordware, Relevance AI, and Langflow — no heavy coding required
check_circle Deploy AI agents on cloud platforms — AWS Bedrock, Azure OpenAI, and GCP Vertex AI with serverless architecture
check_circle Implement long-term memory, Human-in-the-Loop (HITL), and state management for reliable production AI agents
smart_toy

20+ Hands-on Agent Labs

Build real AI agents in every module — Finance Bot, Sales Analyzer, Customer Support Chatbot, Stock Analysis Agent, and more. Every lab deploys a working agent end-to-end.

hub

Multi-Framework Mastery

LangGraph, CrewAI, Autogen, and Phidata — you learn all four major agent frameworks so you can pick the right tool for any production use case, not just one ecosystem.

cloud

Cloud Deployment Included

Bonus module covers AWS Bedrock, Azure OpenAI, and GCP Vertex AI deployment of generative AI agents. From notebook to cloud production in the same course.

Course Curriculum

11 Modules + Bonus · 80 Hours
articleAgentic AI Introduction — agents vs. Generative AI vs. Traditional AI
40:00
articleAgentic AI Building Blocks — autonomous agents, human-in-the-loop, single vs. multi-agent
50:00
articleAgentic AI Frameworks Overview — LangGraph, CrewAI, Autogen, Phidata compared
45:00
articleEthical and Responsible AI — best practices and implementation success stories
35:00
scienceLab: Analyzing AI Agent Use Cases · Exploring Agentic AI Frameworks
50:00
articleAgentic Architecture Types — Perception, Cognitive, Action, Learning, Collaboration, Security modules
60:00
articleDesign Patterns — Reflection, Tool Use, Planning, ReAct, ReWOO patterns
60:00
articleMulti-Agent Pattern and Design Considerations for secure, scalable architectures
50:00
scienceLab: Designing an AI agent architecture · Implementing different design patterns
50:00
articleLangChain Components — data ingestion, document loaders, text splitting, embeddings
55:00
articleVector Database Integration — FAISS, Pinecone, Chroma for semantic search and retrieval
50:00
articleLCEL — Runnables, Chains, building and deploying pipelines, deployment with Langserve
55:00
scienceLab: Build a Self-correcting Coding Assistant with LangChain
60:00
articleLangGraph Introduction — State Schema, State Reducer, Multiple Schemas, Trim and Filter Messages
60:00
articleMemory and External Memory — Short vs. Long Term Memory, Memory Schema
55:00
articleUX and Human-in-the-Loop (HITL), Building Agents with LangGraph, Deployment
55:00
scienceLab: Building a Finance Bot with LangGraph — state management and long-term memory
70:00
articleAgentic RAG vs. Traditional RAG — Architecture, Components, and Adaptive RAG
55:00
articleVariants of Agentic RAG, Applications, Agentic RAG with LlamaIndex and Cohere
60:00
scienceLab 1: AI-Powered Sales Report Analyzer with LlamaIndex
60:00
scienceLab 2: Market Research Agent with RAG & Cohere
60:00
articlePhidata Agents, Models, Tools, Knowledge, Chunking — Vector DB, Storage, Embeddings
60:00
articleWorkflows in Phidata — building and orchestrating multi-step AI agent pipelines
55:00
scienceLab: Design a Data Analysis Agent with Phidata
65:00
articleMulti-Agent Systems — workflows, collaborative agents, multi-agent designs with LangGraph
60:00
articleCrewAI — Introduction, Components, Setting up environment, Building Agents
60:00
scienceLab 1: Build a Customer Support Chatbot with LangGraph
65:00
scienceLab 2: Design a Stock Analysis Agent with CrewAI
55:00
articleAutogen — Salient Features, Roles and Conversations, Chat Terminations, Human-in-the-Loop
60:00
articleCode Executor, Tool Use, Conversation Patterns, Developing Autogen-powered Agents
55:00
articleDeployment and Monitoring of Autogen agents in production
45:00
scienceLab: Develop an AI Research Agent with Autogen
60:00
articleLangfuse — Overview, Dashboard, Tracing, Evaluation, Managing Prompts, Experimentation
60:00
articleLangsmith — AI Observability, Setting Up, Managing Workflows, AgentOps Practical Implementation
55:00
scienceLab: AI Observability with Langsmith · AgentOps Practical Implementation
65:00
articleNo-Code/Low-Code AI — Benefits, Challenges, Key Components, Building Workflows Without Coding
55:00
articleDesigning with Drag-and-Drop Interfaces, Integrating with Existing Systems, Security, Scaling
55:00
scienceLab 1: Content Writer Agent in Wordware
45:00
scienceLab 2: Design Your own SEO Agent with Relevance AI
45:00
scienceLab 3: Creating an AI Agent with Langflow
45:00
articleDeploying Generative AI Models with Amazon Bedrock — RAG, AI Agents, Serverless deployment, Observability
Self-paced
articleDeveloping Generative AI with Azure OpenAI, Agentic Workflows with Azure Machine Learning, Fine-Tuning LLMs on Azure
Self-paced
articleWorking with Vertex AI Agent Builder — Building No-Code Conversational AI Agents on GCP
Self-paced
scienceLab: Build and Deploy AI Models on AWS Bedrock, Azure OpenAI, and GCP Vertex AI
Self-paced

Tools & Technologies

Agent Frameworks
LangChainLangGraphCrewAIAutogenPhidataLlamaIndex
Observability & Ops
LangfuseLangsmithAgentOPsLCELLangserve
RAG & Knowledge
CohereFAISSPineconeChromaVector Databases
No/Low-Code
WordwareRelevance AILangflow
Cloud Platforms
AWS BedrockAzure OpenAIGCP Vertex AIAzure ML

Frequently Asked Questions

Basic Python programming knowledge is required. Prior ML experience is helpful but not mandatory — the course begins with Agentic AI essentials and progressively builds to advanced multi-agent systems. You should be comfortable reading and writing Python code.
The course follows a structured path — starting with LangChain/LCEL foundations, then LangGraph for stateful agents, Phidata for workflow-based agents, CrewAI and Autogen for multi-agent systems. By the end you'll be proficient in all four and know when to use each.
Yes. Labs use real LLM APIs (OpenAI, Cohere, Anthropic), real vector databases (Pinecone, FAISS), and the bonus module deploys on AWS Bedrock, Azure OpenAI, and GCP Vertex AI. You get access to cloud sandboxes so you never pay for cloud resources from your own account.
AI Engineer, Agentic AI Developer, GenAI Solutions Architect, ML Engineer (LLM-focused), AI Automation Engineer, and NLP/AI Platform Engineer. Companies across fintech, SaaS, healthcare, and e-commerce are hiring urgently for these roles in 2025.
Yes. You receive a Thick Brain Technology certificate upon completing all 10 modules and the assessments. The certificate is shareable on LinkedIn and verifiable by employers.
🔥 Most Popular Course
Agentic AI & LLM Engineering
₹54,999₹26,999
41% OFF · 7 seats left
  • check_circle80 hours live instructor-led training
  • check_circle20+ AI agent hands-on labs
  • check_circleLangChain · CrewAI · LangGraph
  • check_circlePlacement support until hired
  • check_circleCertificate of completion
Enroll Now — ₹26,999
★ 4.9/5 from 280+ students
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