Building Autonomous Agentic AI Systems for Beginners HandsOn, Published by Packt Publishing
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.
- 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
- Familiarity with Python
- Basic understanding of how LLMs work
- Curiosity to explore Agentic AI, autonomous agents, and AI-driven workflows
- Prior experience with AI is required
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 Agents
- An MCP Server and Client Agent
- Multi-Agent Systems using CrewAI
- An AWS Monitoring Multi-Agent System
- A Cross-Cloud Multi-Agent System using AWS and Google Cloud
- Agentic RAG systems for intelligent knowledge retrieval
- A Course Intelligence Agent using Agentic RAG
- 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.
- Build autonomous AI agents from scratch
- Use Smolagents, MCP, CrewAI, and n8n to create production-grade agentic systems
- Integrate RAG, memory systems, embeddings, and tool-based reasoning
- Design multi-agent workflows where agents collaborate intelligently
- Build cloud-aware and cross-cloud agentic systems
- Deploy agents using Docker or cloud environments
- Implement guardrails, safety, debugging, and reflection-based optimization
- Build scalable, real-world automation powered by Agentic AI
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.
- Concept Lectures
- Step-by-step Demonstrations
- Real-world Agentic AI Projects
- Practical Scenarios & Troubleshooting
- Fundamentals of Agentic AI
- LLMs and Core Foundations
- Building AI Agents from Scratch
- Smolagents Framework
- Memory, Embeddings & Vector Databases
- Retrieval-Augmented Generation (RAG)
- Model Context Protocol (MCP)
- Multi-Agent Systems
- Cloud & Cross-Cloud Multi-Agent Architectures
- Tool Use & Orchestration
- Reflection, Safety & Guardrails
- Deployment Techniques
- Real-world Projects & Demonstrations
- 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