Build Autonomous AI Systems: 3-Day Bootcamp

Dive into the world of Agentic AI with this intensive 3-day bootcamp designed to take you from the basics to building sophisticated, production-ready multi-agent systems. In just three days, you will gain the knowledge and skills necessary to design AI systems that can reason, act, and collaborate to tackle complex real-world tasks.

On Day 1, you will establish a solid foundation of understanding how AI agents function, differentiating between chatbots, workflows, and agents. You will explore essential concepts such as the ReAct loop (Think → Act → Observe) and delve into the anatomy of an AI agent. By the end of the day, you will have created your first working agent equipped with tool integration and retrieval-based memory.

As we progress to Day 2, the focus shifts to scaling your knowledge from individual agents to multi-agent systems. You will learn about powerful design patterns like Planner–Executor and Researcher–Writer, and utilize modern orchestration frameworks such as LangGraph and CrewAI to build collaborative systems. On Day 3, you will transition your prototypes into production-ready systems, implementing evaluation pipelines, designing safety systems, and exploring cost optimization strategies. The course culminates in a capstone project where you will showcase a fully functional multi-agent system, ready for real-world application.

What you will learn:

Understand the difference between chatbots, workflows, and intelligent agents
Design the anatomy of an agent: system prompts, memory, tools, and output structures
Build agents with tool integration and retrieval-based memory
Design and coordinate multi-agent systems using patterns like Planner–Executor and Researcher–Writer
Use orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen) for structured workflows
Implement evaluation pipelines (LLM-as-a-Judge, test cases, and regression testing)
Design guardrails and safety measures against prompt injection and tool misuse
Monitor and observe systems: inputs, outputs, latency, and failures
Optimize costs and performance through parallelization, caching, and scaling strategies
Integrate agents with RAG, automation (Zapier, n8n), enterprise APIs, and UIs (Streamlit)
Deploy enterprise-grade architectures with API layers, orchestration, and governance
Deliver a capstone project: functional multi-agent system with demo and business case

Course Content:

Sections: 3
Lectures: 12
Duration: 20 hours

Requirements:

Basic understanding of AI concepts (helpful but not required)
Familiarity with Python is useful, but not mandatory
Comfortable using web apps and APIs (beginner level is enough)
Laptop with internet connection (Mac/Windows/Linux)
Ability to install tools like: Python and VS Code (or any IDE)
Willingness to learn hands-on by building real systems
No prior experience with agents or LLM frameworks required
Curiosity about AI, automation, and real-world applications

Who is it for?

Professionals looking to build real-world AI systems, not just use prompts
Software engineers who want to learn agentic AI and multi-agent architectures
Product managers aiming to design AI-powered products and workflows
Data scientists transitioning into LLM-based and agent-based systems
Entrepreneurs and builders creating AI startups or automation tools
Automation and no-code users wanting to upgrade to intelligent AI systems
Technical leaders exploring enterprise AI architecture and strategy
Anyone interested in moving from AI experimentation to production-ready systems

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