Full Stack Agentic AI Development Using OpenAI SDK & LangChain Framework
Total Classes: 15
Duration: 2 Hours per Class
Prerequisite: Python & Basic Programming Knowledge
NOTE : As part of this course, you will prepare 50 Most Frequently Asked Agentic AI Development Interview Questions with Detailed Answers (Theory + Coding-Based).
Module 1: Foundations of Agentic AI (Classes 1–3)
Class 1: Introduction to LLMs & Agentic AI
- Evolution from traditional ML to Generative AI
- Understanding Large Language Models (LLMs)
- Tokens, temperature, context window, embeddings
- What is Agentic AI?
- Components of an AI Agent (LLM + Tools + Memory + Planning)
- Architecture overview of agent-based systems
Class 2: OpenAI SDK Deep Dive (Python)
- Using OpenAI SDK: API structure and authentication
- Chat Completions API
- System, user, and assistant roles
- Function calling & tool invocation
- Structured outputs (JSON schema responses)
- Streaming responses
- Error handling & retry logic
- Hands-on: Build a structured AI assistant with function calling
Class 3: Prompt Engineering & Cost Optimization
- Prompt patterns (zero-shot, few-shot, chain-of-thought)
- System prompt design for agents
- Guardrails and response control
- Token usage monitoring
- Reducing cost & latency in production
Module 2: LangChain Framework for Agent Development (Classes 4–8)
Class 4: LangChain Architecture & Core Components
- LLM wrappers
- Prompt templates
- Output parsers
- Chains vs Agents
- Runnable interface
- Hands-on: Build first LLM chain
Class 5: Tools & Agents
- Defining custom tools
- Integrating external APIs
- Agent types (ReAct, tool-calling agents)
- Building reasoning + acting loop
- Hands-on: Build a research agent using tools
Class 6: Memory Systems
- ConversationBufferMemory
- ConversationSummaryMemory
- VectorStore-backed memory
- Short-term vs long-term memory
- Hands-on: Build chatbot with memory
Class 7: Advanced Agent Patterns
- Multi-step reasoning
- Planning & task decomposition
- Self-reflection agents
- Error recovery strategies
Class 8: Async & Scalable Agent Design
- Async execution in Python
- Parallel tool calls
- Streaming agent responses
- Performance optimization
Module 3: Retrieval-Augmented Generation (RAG) (Classes 9–11)
Class 9: Embeddings & Vector Databases
- Embedding models
- Chunking strategies
- Similarity search
- Intro to vector stores (FAISS / Chroma)
- Hands-on: Index custom documents
Class 10: Building Production RAG Pipeline
- Document loaders
- Text splitters
- Retriever + LLM integration
- RAG evaluation metrics
- Hands-on: Build Document Q&A system
Class 11: Advanced RAG Techniques
- Hybrid search (keyword + vector)
- Re-ranking
- Context compression
- Metadata filtering
Module 4: Multi-Agent & Workflow Systems (Classes 12–13)
Class 12: Multi-Agent Systems
- Agent-to-agent communication
- Supervisor + worker agent model
- Role-based collaboration
- Delegation strategies
- Hands-on: Build multi-agent research assistant
Class 13: Workflow Automation Agents
- Task pipelines
- Event-driven AI systems
- Integration with REST APIs
- Scheduling & orchestration
Module 5: Full Stack Integration & Deployment (Classes 14–15)
Class 14: Backend Integration
- FastAPI integration
- Secure API key management
- Middleware & logging
- Caching strategies
- Docker basics
Class 15: Production Deployment & Best Practices
- Monitoring & observability
- Rate limits & scaling
- Model selection strategy
- Security & data privacy considerations
- Final Capstone Project Presentation
Certified
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