In the previous masterclass, we explored the foundations of LangChain and how it helps structure LLM-powered applications.

In MC-13, we move one level deeper.

This session focuses on building tool-using AI agents with LangChain — agents that do not just generate text, but can connect with external tools, simple data sources, custom Python functions, and retrieval systems.

Participants will learn how to create a LangChain agent, define tools for the agent to use, connect those tools to simple data or business logic, and integrate a LlamaIndex-powered knowledge layer so the agent can retrieve information before responding.

This is a practical, builder-focused session designed for learners who want to understand how modern AI applications move from simple prompts to real agentic workflows.

By the end of the session, participants will understand how LangChain agents think, choose tools, call functions, retrieve context, and generate more useful responses for real-world AI applications.

This masterclass is part of the AI Residency.

✅ Join the new AI Residency cohort starting Jul 11 to build this end-to-end with guided support, project feedback, and a production-ready workflow—from data ingestion → indexing → retrieval → evaluation → deployment.

https://academy.decodingdatascience.com/airesidencyfasttrack