[100% Off] 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:
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Standalone AI Agents
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An MCP Server and Client Agent
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Multi-Agent Systems using CrewAI
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An AWS Monitoring Multi-Agent System
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A Cross-Cloud Multi-Agent System using AWS and Google Cloud
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Agentic RAG systems for intelligent knowledge retrieval
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A Course Intelligence Agent using Agentic RAG
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A Project Feasibility Analysis Agent powered by Agentic RAG
These 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:
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Build autonomous AI agents from scratch
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Use Smolagents, MCP, CrewAI, and n8n to create production-grade agentic systems
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Integrate RAG, memory systems, embeddings, and tool-based reasoning
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Design multi-agent workflows where agents collaborate intelligently
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Build cloud-aware and cross-cloud agentic systems
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Deploy agents using Docker or cloud environments
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Implement guardrails, safety, debugging, and reflection-based optimization
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Build scalable, real-world automation powered by Agentic AI
Special Note
This 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:
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Concept Lectures
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Step-by-step Demonstrations
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Real-world Agentic AI Projects
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Practical Scenarios & Troubleshooting
Course Contents:
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Fundamentals of Agentic AI
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LLMs and Core Foundations
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Building AI Agents from Scratch
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Smolagents Framework
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Memory, Embeddings & Vector Databases
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Retrieval-Augmented Generation (RAG)
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Model Context Protocol (MCP)
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Multi-Agent Systems
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Cloud & Cross-Cloud Multi-Agent Architectures
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Tool Use & Orchestration
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Reflection, Safety & Guardrails
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Deployment Techniques
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Real-world Projects & Demonstrations
All 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!
