Building Autonomous Agentic AI Systems for Beginners HandsOn

Hands-On Foundations for Creating Autonomous AI Agents

What you’ll learn
Understand the fundamentals of Agentic AI, how it works, and how it differs from traditional AI systems
Build functional AI Agents using modern frameworks like Smolagents, CrewAI, MCP, and n8n
Integrate RAG (Retrieval-Augmented Generation) into agent workflows to improve accuracy and reliability
Design multi-agent systems capable of collaboration, autonomy, and complex reasoning
Implement memory systems, embeddings, vector search, and orchestration logic inside agents
Build real-world multi-agent solutions, including cloud monitoring and cross-cloud intelligence
Apply reflection, self-improvement, safety, and guardrail techniques to make agents more robust and trustworthy

Requirements
Familiarity with Python
Basic understanding of how LLMs work
Curiosity to explore Agentic AI, autonomous agents, and AI-driven workflows
Prior fundamental experience with AI is required

Description
Agentic AI is rapidly transforming how work gets done, marking a pivotal shift in the evolution of intelligent systems. Mastering this new paradigm has never been more essential. This course empowers you with the practical skills and strategic understanding needed to design, build, and deploy real-world agentic AI systems, unlocking powerful opportunities for innovation, automation, and career growth.What’s in this course?We begin with the core foundations of Agentic AI – understanding what agents are, how they differ from traditional AI systems, and how reasoning, planning, and tool usage enable autonomous workflows. From there, we explore the building blocks of agentic systems, including LLMs, memory, embeddings, vector databases, and RAG pipelines. You’ll learn how agents reason, how they use tools, and how to structure prompts and task orchestration for optimal performance.Next, we move into advanced, real-world demonstrations, where you will build practical agentic solutions such as:Standalone AI AgentsAn MCP Server and Client AgentMulti-Agent Systems using CrewAIAn AWS Monitoring Multi-Agent SystemA Cross-Cloud Multi-Agent System using AWS and Google CloudAgentic RAG systems for intelligent knowledge retrievalA Course Intelligence Agent using Agentic RAGA Project Feasibility Analysis Agent powered by Agentic RAGThese demonstrations simulate real enterprise and startup use cases, helping you understand how agentic AI systems are designed, scaled, and deployed in production environments.By the end of this course, you’ll be able to:Build autonomous AI agents from scratchUse Smolagents, MCP, CrewAI, and n8n to create production-grade agentic systemsIntegrate RAG, memory systems, embeddings, and tool-based reasoningDesign multi-agent workflows where agents collaborate intelligentlyBuild cloud-aware and cross-cloud agentic systemsDeploy agents using Docker or cloud environmentsImplement guardrails, safety, debugging, and reflection-based optimizationBuild scalable, real-world automation powered by Agentic AISpecial NoteThis course is highly practical, packed with real demonstrations, hands-on projects, troubleshooting scenarios, and end-to-end agent-building exercises. You won’t just learn how agents work – you’ll build them, optimize them, and deploy them in real-world applications.Course Structure:Concept LecturesStep-by-step DemonstrationsReal-world Agentic AI ProjectsPractical Scenarios & TroubleshootingCourse Contents:Fundamentals of Agentic AILLMs and Core FoundationsBuilding AI Agents from ScratchSmolagents FrameworkMemory, Embeddings & Vector DatabasesRetrieval-Augmented Generation (RAG)Model Context Protocol (MCP)Multi-Agent SystemsCloud & Cross-Cloud Multi-Agent ArchitecturesTool Use & OrchestrationReflection, Safety & GuardrailsDeployment TechniquesReal-world Projects & DemonstrationsAll sections of this course are demonstrated live, with the goal of encouraging enrolled users to set up their own environments, complete the exercises, and learn through hands-on experience!

Who this course is for
Developers who want to build intelligent automation & AI agents
AI enthusiasts who want to move beyond basic LLM prompts
Data scientists & ML engineers expanding into Agentic AI
Backend engineers integrating AI-driven workflows
System architects adopting multi-agent design patterns
Product builders working on AI-first products
Students and professionals preparing for the future of AI engineering

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