GenAI & Agentic AI Course | Live AI Training India
Build Production-Ready GenAI and Agentic AI Systems
Move beyond basic prompting. Learn Claude AI, RAG, LangChain, LangGraph, AI agents, MCP/SDK-powered workflows, memory systems, evaluation, and deployment through hands-on projects and capstone-based learning.
About this programme
This comprehensive Generative AI course is designed for developers and professionals who want to master LLM applications, RAG pipelines, and autonomous AI agents. Through structured live sessions, learners build a production-ready multi-agent system using LangChain, LangGraph, and MCP tools, moving from prompt engineering foundations to enterprise-grade deployment on Azure.
- Duration
- 13-Week Intensive / 4-Month Weekend
- Delivery
- Live Online / Hybrid
- Live learning
- 65 hours
- Claude AI
- Prompt Engineering
- RAG Systems
- LangChain
- LangGraph
- AI Agents
- Multi-Agent Systems
- MCP/SDK Workflows
- Memory Systems
- Streamlit + FastAPI
- Azure Deployment
- Enterprise Capstone
Diagram: GenAI and Agentic AI core surrounded by connected capability nodes — Claude AI, RAG, LangGraph, AI Agents, MCP and SDK Tools, Memory, Evaluation, and Deployment.
- Claude AI
- RAG
- LangGraph
- AI Agents
- MCP / SDK Tools
- Memory
- Evaluation
- Deployment
GenAI & Agentic AI Learning Paths
Algo Labs offers multiple GenAI learning paths depending on your goal, schedule, and career depth. Choose the focused AI systems track, the advanced project-focused path, or the longer career track with internship and domain specialization.
GenAI & Agentic AI Programme
Best for: Learners who want deeper practical exposure, portfolio-grade AI systems, and advanced agentic workflows.
Outcome: Move from GenAI user to AI systems builder through project-based learning, advanced workflows, and capstone execution.
- Multi-LLM integration
- RAG and vector databases
- LangGraph workflows
- AI agents
- Multi-agent orchestration
- MCP/SDK tool integration
- Memory systems
- AI evaluation
- Deployment workflows
GenAI Claude Career Track
Best for: Learners who want a longer career-building path with domain specialization, internship, portfolio, and career readiness.
Outcome: Build a career-ready GenAI profile with a domain AI prototype, internship project, capstone, and portfolio launch.
- 6-month / 8-month structured roadmap
- Python, Data, ML, NLP, and Deep Learning foundations
- GenAI, Claude AI, RAG, and Agentic AI
- Multi-agent systems and MCP
- Azure deployment
- Domain specialization
- Live internship
- Capstone and portfolio review
Which GenAI Track Should You Choose?
GenAI & Agentic AI Course Curriculum
A professional journey from foundations through deployment — every module builds directly on the previous one to ensure you master LLMs and AI agent development.
GenAI Foundations
- • LLM fundamentals
- • Claude AI
- • OpenAI
- • Gemini
- • Groq
- • Prompt engineering
- • Structured outputs
- • Function calling
- • Prompt pipelines
RAG & Knowledge Systems
- • Embeddings
- • Semantic similarity
- • Vector databases
- • FAISS
- • ChromaDB
- • Chunking
- • Document loaders
- • PDF Q&A bot
- • Retrieval workflows
Agentic AI & Workflows
- • Agent loops
- • ReAct pattern
- • Tool calling
- • LangChain agents
- • LangGraph workflows
- • Planner / Executor / Reviewer patterns
- • Multi-agent systems
- • CrewAI / AutoGen
MCP, Memory & Evaluation
- • MCP concepts
- • SDK / tool workflow integration
- • Short-term memory
- • Long-term memory
- • Vector memory
- • RAG evaluation
- • Hallucination testing
- • Prompt reliability benchmarking
Deployment & Portfolio
- • Streamlit
- • FastAPI
- • Azure deployment
- • GitHub
- • CI/CD concepts
- • README documentation
- • Architecture diagrams
- • Capstone viva
Industrial GenAI System Architecture
Every layer plays a role — from the user query down to deployed AI applications.
13-Week GenAI Course Roadmap
A focused GenAI and Agentic AI systems path for learners who want practical skill development in 13 weeks or weekend mode.
Hands-On GenAI & Agentic AI Projects
Every module is reinforced with practical building tasks, culminating in an enterprise-grade AI agent capstone project.
GenAI & Agentic AI Course Projects
- Multi-LLM chatbot with smart routing
- PDF Q&A RAG bot
- LangGraph research workflow
- Multi-agent research crew
- Chatbot with persistent memory
- Streamlit + FastAPI GenAI app
- Enterprise AI Agent capstone
Multi-LLM Router
Route user tasks across Claude, OpenAI, Gemini, and Groq based on cost, speed, and capability.
Enterprise RAG Bot
Build document-based Q&A with chunking, embeddings, FAISS / ChromaDB, and citation-aware responses.
LangGraph Workflow
Build conditional, retry-enabled, multi-step AI workflows.
Multi-Agent Crew
Coordinate planner, researcher, writer, reviewer, and tool agents.
Memory-Enabled Chatbot
Combine short-term, long-term, and vector memory.
Deployed AI App
Package GenAI systems into Streamlit / FastAPI applications.
Need Internship and Domain Specialization?
The GenAI Claude Career Track is the longer career-building path for learners who want domain specialization, internship, capstone, portfolio, and career readiness.
Internship: Integrated in Month 5 for Regular or Months 6–7 for Weekend, based on program eligibility and project completion.
Build Projects That Prove Real GenAI Capability
CLI Chatbot + API Data Fetch
Build a Python-based chatbot and connect it to external data.
Real Business Dataset EDA Report
Clean, analyze, and summarize business data.
NLP Sentiment Demo
Understand tokenization, embeddings, and sentiment classification.
Multi-LLM Chatbot
Build Claude + GPT + Gemini integration with smart routing.
PDF Q&A Bot using RAG + FAISS
Ask questions from documents using retrieval-augmented generation.
LangGraph Research Workflow
Design multi-step AI workflows with conditional routing and retry logic.
Multi-Agent Research System
Coordinate agents for research, writing, review, and summarization.
Chatbot with Persistent Memory
Create conversational memory using buffer, summary, and vector memory.
Streamlit + FastAPI GenAI App
Deploy an AI-powered application with frontend and API backend.
Enterprise AI Agent Capstone
Combine RAG, LangGraph, agents, MCP/tool integration, memory, evaluation, and deployment.
Enterprise AI Agent System
Combine every layer of the program into one deployable, viva-ready AI system.
Enterprise GenAI Technology Stack
Core
- Python
- VS Code
- Jupyter
- Git
GenAI Platforms
- Claude models
- OpenAI
- Gemini
- Groq
- Llama
- Ollama
RAG
- FAISS
- ChromaDB
- LangChain Retrieval
Agents
- LangGraph
- CrewAI
- AutoGen
- LangChain Agents
MCP / SDK
- MCP SDK
- API tools
- Database tools
- File-system tools
Deployment
- Streamlit
- FastAPI
- Azure OpenAI
- Azure App Service
- GitHub Actions
Evaluation
- RAGAS
- Hallucination testing
- Prompt reliability testing
Assessment and Evaluation
Assessment focuses on practical implementation, capstone quality, presentation, documentation, and readiness for certification preparation.
Certification Preparation Support
The program includes preparation support for selected Microsoft or IBM certification pathways, along with Algo Labs project and course completion recognition based on program completion requirements.
Azure AI Fundamentals and Azure Fundamentals — specialized training and practice support.
AI foundations and Watson Studio ML concepts — specialized training and practice support.
Learn From Rohit Krishnan M
Lead trainer and AI architect for Algo Labs programmes. Focused on building industrial-grade GenAI and Agentic AI systems.
Frequently Asked Questions
Start Building GenAI and Agentic AI Systems
Move beyond basic prompting. Learn to build RAG systems, AI agents, LangGraph workflows, MCP/SDK-powered automation, memory systems, and deployed AI applications.
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Programme Practical Details
Algo Labs Programme Fact Sheet · Last Reviewed: 2026-08-16