Agentic AI with LangChain and LangGraph in Hyderabad

About this course

This intermediate programme teaches you to design, build, and deploy production-grade agentic AI systems using LangChain and LangGraph. Delivered live by an expert instructor, every session combines conceptual grounding with hands-on Python labs in Jupyter Notebooks. You progress from foundational agentic concepts through multi-agent orchestration, retrieval-augmented generation, and human-in-the-loop workflows, culminating in a capstone project where you package and containerise a fully functional agentic application using FastAPI and Docker.

What is Agentic AI with LangChain and LangGraph?

Agentic AI refers to systems in which a large language model does not simply respond to a single prompt but instead reasons, plans, and acts across multiple steps — calling external tools, managing memory, and adapting its behaviour based on intermediate results. LangChain provides the foundational primitives: chains, runnables, prompt templates, output parsers, and a rich tool ecosystem including the Tavily Search API and Wikipedia API. LangGraph extends this by modelling agent behaviour as explicit stateful graphs of nodes and edges, enabling loops, conditional branching, parallel execution, and interrupts. Together they form a production-ready stack for building agents that integrate with vector stores such as FAISS, Chroma, and Pinecone, and communicate with frontier models through the OpenAI API and Anthropic Claude API.

Why learn Agentic AI with LangChain and LangGraph now

Enterprises are moving rapidly from experimental LLM demos to autonomous agents embedded in real workflows — research assistants, coding agents, data analysis pipelines, and approval-gated decision systems. The engineers who can construct these systems with LangChain and LangGraph, instrument them with LangSmith observability, and ship them inside Docker containers behind FastAPI endpoints are commanding serious attention from hiring teams. Proficiency in multi-agent collaboration patterns, adaptive RAG, human-in-the-loop interrupts, and robust error handling and fallback logic distinguishes practitioners who build reliable agentic products from those who only prototype. This course gives you exactly that production-oriented skill set.

Who this course is for

• Python developers moving into AI engineering — Those comfortable with Python and REST APIs who want to build and ship real LLM-powered agent systems professionally.
• Machine learning engineers expanding into generative AI — Practitioners with ML foundations who need hands-on LangChain and LangGraph skills to work on agentic product pipelines.
• Backend engineers building AI-powered services — Server-side engineers who want to architect and deploy agentic APIs using FastAPI, Docker, and LangGraph in production.
• Data scientists exploring LLM applications — Analysts who understand models conceptually and now want to build retrieval-augmented, tool-using agents with real memory.
• Conversational AI developers — Chatbot and dialogue system builders ready to move beyond single-turn prompting into stateful, multi-step agentic workflows.
• Technical leads evaluating agentic architectures — Engineers who need architectural fluency in supervisor agents, subgraph composition, and multi-agent delegation patterns.

What you’ll be able to build

By the end you will have built — not just studied — the core systems of the field:

• ReAct Agent with Custom Tools — A fully functional reasoning-and-acting agent built with create_react_agent that calls the Tavily Search API and a custom decorated tool, traced end-to-end in LangSmith.
• Stateful LangGraph Workflow — A compiled LangGraph state graph using TypedDict-defined state, conditional edges, and loop cycles that implements an iterative plan-and-execute reasoning architecture.
• Multi-Agent Supervisor System — A hierarchical multi-agent system in LangGraph where a supervisor agent routes tasks to specialised sub-agents, passing shared state and handling delegation and handoffs.
• RAG-Enabled Agent Pipeline — An adaptive retrieval-augmented generation pipeline integrating a Chroma or Pinecone vector store as an agent tool, with query rewriting and self-correction loops evaluated via LangSmith.
• Human-in-the-Loop Approval Workflow — A LangGraph workflow implementing interrupt_before approval gates, human feedback injection, and full audit logging for high-stakes agent actions requiring oversight.
• Persistent Memory Agent — An agent equipped with conversation buffer memory, LangGraph checkpointer-backed state persistence, and FAISS-powered long-term episodic memory across multiple turns.
• Containerised Agentic REST API — A production-packaged agentic service exposing streaming endpoints via FastAPI, containerised with Docker, with environment secrets management and rate-limiting considerations applied.

Career paths & salary

Completing this course positions you for roles including AI Engineer, LLM Application Developer, Generative AI Developer, Conversational AI Developer, and AI Solutions Architect, as well as backend engineering positions where AI-powered services are the core product. Demand for engineers who can move beyond prompt experimentation and build observable, debuggable, production-deployed agent systems is growing sharply across technology companies, AI-native startups, and enterprise digital transformation teams. Proficiency in the LangChain and LangGraph stack, combined with practical experience in LangSmith tracing, multi-agent orchestration, RAG integration, and Docker-based deployment, makes a candidate genuinely distinctive. Research Engineer roles at applied AI labs also value the evaluation, observability, and systematic debugging skills developed throughout this programme.