6 Months Program
AI Agent Engineer
Build autonomous AI agents and multi-agent systems for complex problem-solving with LangChain, RAG, and production deployment
6 Months
Duration
5-6 hrs/week
Weekly Coaching
30-40
Batch Size
$799
Course Fee
Learning Path
LangChain for LLM Apps
→
Building AI Agents with RAG
→
Advanced Agents Workshop
Course Structure
Months 1-2
Foundations & Chains
Goal
Understand LLM fundamentals, LangChain architecture, and chain design.
Topics
- •LLM basics: prompts, tokens, embeddings, and context windows
- •LangChain core concepts: chains, prompts, memory, tools
- •Setting up a LangChain project (Python environment, dependencies)
- •Integrating OpenAI, Anthropic, and local models
- •Basic agents vs. chains — when to use what
Practical Work
- • Build a Q&A chatbot using LangChain's LLMChain
- • Create a multi-step chain for text summarization and sentiment analysis
- • Add conversation memory to a chatbot
Assessment
- • Weekly quizzes on LangChain components
- • Submit a GitHub repo with a working multi-step chain
Months 3-4
Retrieval-Augmented Pipelines & Tool-Calling
Goal
Build AI agents that can use knowledge beyond the LLM's base model.
Topics
- •Retrieval-Augmented Generation (RAG) architecture
- •Document loaders and text splitting strategies
- •Embedding models (OpenAI, Hugging Face)
- •Vector stores: FAISS, Pinecone, Chroma
- •LangChain retrievers and similarity search
- •OpenAI Functions API & LangChain tool calling
Practical Work
- • Build a document chatbot with FAISS + LangChain retriever
- • Implement hybrid search (semantic + keyword)
- • Add tools: web search, calculator, API fetcher
- • Integrate OpenAI Functions API for structured outputs
Assessment
- • Repo review of FAISS-based doc chatbot
- • Mid-term oral defense: explain your RAG pipeline
Months 5-6
Autonomous Planning Loops, CI/CD Deploy
Goal
Create autonomous AI agents and deploy them in production-like settings.
Topics
- •Agent planning loops (ReAct, AutoGPT-style architectures)
- •Long-term memory with vector stores
- •Evaluation harness for agents (LangSmith, custom metrics)
- •Containerizing AI agents with Docker
- •CI/CD for AI: GitHub Actions, testing LLM workflows
- •Deployment options: Streamlit, FastAPI, Vercel, Azure Functions
Practical Work
- • Build a ReAct agent with tool integration & memory
- • Implement an evaluation harness for reliability testing
- • Deploy your AI agent to a cloud environment with automated testing
Assessment
- • Final Project: Autonomous AI Agent with documentation and deployment link
- • GitHub repo + recorded demo presentation
Final Deliverables
Doc-chat Bot
FAISS vector store integration
ReAct Agent
Tool calling and evaluation harness
Deployment
Production-ready AI Agent in live environment
Certificate
Certificate of Completion
Technologies You'll Master
LangChain
Agent Framework
RAG
Retrieval Systems
FAISS
Vector Storage
ReAct
Agent Architecture
Docker
Containerization
Ready to Build Autonomous AI Agents?
Join our comprehensive 6-month program and become an AI Agent Engineer with hands-on expertise in LangChain, RAG, and production deployment.