Agentic Workflows
Master the architecture of autonomous agents. Learn to build systems that reason, act, and learn.
1Phase 1: Agentic Foundations
The Autonomous Loop
Moving from Chatbots to Agents. We explore the fundamental 'Thought-Action-Observation' loop that allows AI to behave autonomously. Understand the difference between zero-shot answering and iterative problem solving.
RAG & Vector Databases
Retrieval Augmented Generation (RAG) is the bridge between frozen model weights and your private data. Learn how vector embeddings work, how to store them, and how to retrieve relevant context for your agents.
2Phase 2: Tools & Capabilities
Function Calling Fundamentals
Agents need hands. Function calling (or Tool Use) allows LLMs to interact with the outside world—sending emails, querying databases, or running calculations. We learn how to define tool schemas and handle model outputs.
Building Custom Tools
Theory is great, but practice is better. We build a custom tool from scratch—a 'Stock Price Fetcher' and a 'Sentiment Analyzer'—and wire them into a LangChain agent.