Moving from chains to graphs is what lets agents handle state, approvals, and collaboration properly. This course covers that progression in Python, building agents with the ReAct architecture, adding persistent memory and human in the loop workflows, then deploying through FastAPI, Docker, and Streamlit.

This Course Offers

• Agentic AI fundamentals covering how LLMs, prompts, memory, and tools combine into autonomous systems
• LLM powered applications with conversational memory, external tool integration, and custom Python tools for automation
• LangGraph workflows including graph based architecture, state management, persistent memory, and human in the loop approval steps
• Autonomous agents built with the ReAct architecture, plus collaborative multi agent systems using supervisor and worker patterns
• Deployment using FastAPI, Docker containerization, and a Streamlit interface for interacting with your agents

Why We Love This Course

1. Basic Python is expected but no LangChain or agent experience is required, so it works if these frameworks are new.
2. LangGraph gets proper coverage, and graph based workflows are what enable state and approval patterns that chains cannot handle.
3. Human in the loop approvals are included, which matters for anything touching real business processes.
4. At one and a half hours across forty lessons, it is a focused, code along introduction rather than a broad survey.

Agentic AI skills are in demand, and the developers who understand state management and multi agent coordination will build the systems others maintain. Do you want to keep chaining prompts, or build agents that reason and remember? Enrollment is open, and basic Python is recommended.

• This course is perfect for Python developers wanting to build intelligent AI agents.
• It suits AI and machine learning enthusiasts interested in Agentic AI and autonomous systems.
• Software developers integrating LLMs, tools, memory, and multi agent workflows, and those wanting to deploy production ready agents, will find the deployment sections directly useful.

• Basic knowledge of Python programming is recommended.
• Familiarity with fundamental programming concepts like variables, functions, loops, and classes is expected.
• No prior experience with LangChain, LangGraph, or AI agents is required, since everything is taught step by step.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.