Building Autonomous Agentic AI Systems for Beginners HandsOn

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

10 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course guides you through the foundations of Agentic AI, equipping you with the skills to build autonomous AI systems that can reason, act, and adapt. You’ll explore large language models, agent workflows, and the core technologies behind intelligent, self-directed agents. Through step-by-step demonstrations, you’ll develop hands-on experience in constructing both single-agent and multi-agent systems, using modern frameworks and low-code/no-code tools. Advanced concepts such as Model Context Protocol, RAG-enhanced agents, and cross-cloud multi-agent setups are carefully explained, showing you how to optimize, deploy, and ensure the safety of your agents. By progressing through the course, you will gain a clear understanding of agentic design patterns, enabling you to select and implement architectures for real-world autonomous solutions. This course is perfect for beginners in AI development, data enthusiasts, and software developers seeking to enter the field of autonomous intelligent systems, with no prior experience required. By the end of the course, you will be able to build, optimize, and deploy autonomous agentic AI systems using modern frameworks, design multi-agent architectures, integrate RAG pipelines, and implement safety and reflection mechanisms.

Syllabus

Course Introduction

In this module, we will introduce the course objectives, learning approach, and the hands-on projects ahead. You will gain a clear understanding of how the course is structured and what skills you will develop throughout the journey. This foundation ensures you are ready to start exploring Agentic AI effectively.

Introduction to Agentic AI

In this module, we will explore the fundamentals of Agentic AI, including its purpose, components, and real-world applications. You will learn how Agentic AI differs from traditional AI systems and why organizations are adopting it. By the end, you will understand the scope and potential impact of agentic intelligence.

Foundation of Agentic AI

In this module, we will cover the foundational technologies behind Agentic AI, from LLMs and AI agents to retrieval-augmented generation and memory systems. You will see step-by-step demonstrations building functional agents and learn to orchestrate tasks and prompts. By the end, you will understand how to structure and execute end-to-end agent workflows.

Developing Agentic AI Systems

In this module, we will introduce development tools and frameworks for Agentic AI. You will build agents for weather data, SQL operations, and other tasks using both code-based and low-code/no-code approaches. By the end, you will be equipped to create functional agentic systems and understand development best practices.

Getting Started with MCP

In this module, we will cover Model Context Protocol (MCP) and its role in enabling agentic AI systems. You will learn to build MCP servers, connect agents, and deploy containerized systems. By the end, you will understand how MCP facilitates agent-context interactions in scalable, real-world setups.

Introduction to Multi-Agent Systems

In this module, we will explore multi-agent systems and their collaborative potential. You will build simple to advanced multi-agent setups and learn to orchestrate agents across cloud environments. By the end, you will understand multi-agent architectures and workflows for autonomous AI systems.

RAG-Enhanced Agents

In this module, we will focus on integrating RAG into agentic AI systems. You will build agents that can retrieve and reason over external knowledge sources to perform intelligent tasks. By the end, you will understand how RAG enhances agent accuracy and practical capabilities.

Reflection, Optimization and Deployment

In this module, we will cover deployment and optimization strategies for agentic AI. You will explore methods for self-reflection, safety, and reliability in autonomous agents. By the end, you will be ready to deploy optimized, secure, and efficient agentic AI systems.

Agentic Design Patterns

In this module, we will introduce design patterns specific to agentic AI systems. You will learn to select, implement, and coordinate agents using best-practice patterns. By the end, you will understand how to design scalable, maintainable, and collaborative agent architectures.

Course Conclusion

In this module, we will conclude the course with a recap of the key concepts and demonstrations covered. You will review your learning journey and explore next steps for applying agentic AI in practical scenarios. This final section ensures you are ready to build, optimize, and deploy autonomous AI systems confidently.