Agentic AI: Beginners Bootcamp

• Lectures: 75
• Duration: 10 weeks

🧭 Purpose of the Course

The Agentic AI: Beginners Bootcamp is designed to equip architects, tech managers, and engineering leaders with a hands-on, fast-track understanding of how to build intelligent agents and orchestrated Agentic AI systems using modern LLM frameworks.

Participants will explore the fundamentals and practical implementation of agents, multi-agent orchestration, LangGraph workflows, and tool integration. The course is highly application-focused, offering guided exercises and real-world mini-projects to help learners translate theory into working systems. By the end of the course, participants will be able to design, build, and deploy agent-based AI solutions confidently, with a solid grasp of architecture patterns, design considerations, and integration techniques.

🏗 Course Outline

• Foundations of LLMs and Environment Setup
• Prompt Engineering Essentials
• Function Calling and Tool Execution
• LangChain & Langgraph Basics and Tooling
• Agent Design Patterns
• Building Real-World Agents
• Capstone Projects: Building Your Own Multi-Agent System

🧰 Tools & Technologies Used

• Python
• Vertex AI
• LangChain
• LangGraph
• LangSmith
• Streamlit
• FastAPI
• Custom utility libraries
• GitHub APIs

👨💻 Who is This Course For?

• Architects and Tech Leads exploring AI Agent system design
• AI Enthusiasts and Learners looking to break into Agentic AI
• Product and Engineering Managers evaluating practical AI applications
• Python Developers aiming to transition into Generative AI roles
• LLM Practitioners seeking hands-on experience with Agents and Orchestrators
• Solution Designers who want to build and demo multi-agent pipelines
• Teams building prototypes with LangChain, LangGraph, and function tools
• Professionals interested in integrating LLMs with APIs, UI, and real-world workflows

✅ Outcomes

• Understand the core concepts of LLMs, prompts, and function tools
• Gain hands-on experience with LangChain and LangGraph workflows
• Design and build single-agent and multi-agent architectures
• Implement orchestrators to manage agent workflows effectively
• Integrate agents with real-world APIs like Amazon, Flipkart, and Sapna
• Build frontends using Streamlit or RESTful services to interact with agents
• Deploy MCP-style agent systems with modular, scalable design
• Develop a working knowledge of Agentic AI design patterns
• Complete a capstone project demonstrating end-to-end agent orchestration