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Free AI Coding Agents Course: Build Autonomous Dev Tools

Explore the landscape of AI coding agents with 10 free hands-on tutorials. From local workflows with LM Studio to full-stack agents with Claude API, LangChain crash courses, and open-source alternatives like Freebuff, this collection...

10 lessons 17h 25min total Free

Course overview

This course brings together a curated set of video tutorials exploring the theory and practice behind AI coding agents. You will examine different architectural approaches, starting with the fundamentals of agentic AI and local model inference using tools like LM Studio and Pi, then progressing to building complete full-stack coding agents with the Claude API and React. The content covers key patterns such as the ReAct loop, subagent architecture, and skill orchestration through platforms like LangChain, Freebuff, and Google Antigravity 2.0, giving you a broad view of how autonomous reasoning is applied to software development.

Who this course is for

This resource is designed for software developers who are comfortable with languages like Python or JavaScript and want to integrate large language models into their coding workflows. Whether you are a backend engineer exploring autonomous task completion, a full-stack developer interested in building your own Claude Code clone, or a technical lead evaluating local versus cloud-based AI solutions, the tutorials address various skill levels, from setting up local inference engines to deploying production-grade agent systems with monitoring and authentication.

How to study this sequence

The tutorials are sequenced to build knowledge progressively. Begin with the foundational concepts of agentic AI and local model setup, which establish the core reasoning behind autonomous systems. Next, move into full-stack implementation and framework-specific lessons using Claude, LangChain, and Trae Solo. Finally, evaluate alternative tools like Freebuff and Google Antigravity 2.0 to compare capabilities. As you watch, apply the configurations and patterns directly in your development environment to solidify your understanding of agentic coding workflows.

What you should be able to do

By engaging with these tutorials, you will gain a practical understanding of how to design, implement, and customize AI coding agents. You will be able to configure local models for private code assistance, integrate cloud-based APIs like Claude into command-line tools, and structure multi-agent systems using popular orchestration frameworks, enabling you to select and adapt the right agentic patterns for specific development tasks.

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What you learn in this AI Coding Agents course

This course organizes 10 AI Coding Agents lessons in a practical order, starting with the base and moving toward examples you can repeat in your own projects.

The main practice points are Understand the differences between RAG, tool-augmented, and agentic AI systems, Recognize the autonomy and reasoning capabilities that define agentic AI, Implement agentic AI systems using both code-first and low-code approaches and Distinguish between AI Agents and Agentic AI terminology. Use this page to review the course proposal, check the key topics, and open the lessons in the recommended order.

What you will practice

  • Understand the differences between RAG, tool-augmented, and agentic AI systems
  • Recognize the autonomy and reasoning capabilities that define agentic AI
  • Implement agentic AI systems using both code-first and low-code approaches
  • Distinguish between AI Agents and Agentic AI terminology
  • Design multi-step workflows where agents iterate and adapt dynamically
  • Understand how local AI models work architecturally and why they differ from cloud-based solutions

Concepts covered

Agentic AI RAG (Retrieval-Augmented Generation) Tool-Augmented Systems Agent Loop Autonomous Reasoning LLM Orchestration Generative AI Large Language Models (LLMs)

Course lessons

10 tutoriais organizados em sequência.

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Course summary

This AI Coding Agents course brings together 10 lessons in a guided sequence. It starts with "What is Agentic AI and How Does it Work?" and moves step by step, so each lesson supports the next one and helps turn the topic into practice.

During the course, the practical focus is on Understand the differences between RAG, tool-augmented, and agentic AI systems, Recognize the autonomy and reasoning capabilities that define agentic AI, Implement agentic AI systems using both code-first and low-code approaches and Distinguish between AI Agents and Agentic AI terminology. These points help you watch with a goal instead of treating the lesson as a loose introduction.

The course also introduces important ideas such as Agentic AI, RAG (Retrieval-Augmented Generation), Tool-Augmented Systems, Agent Loop and Autonomous Reasoning, which makes it easier to understand later tutorials and decide when each resource is useful.

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How to get more from this course

Who this AI Coding Agents course is for

This advanced course is for learners who already understand the fundamentals of AI Coding Agents and want to develop deeper technical or professional skills.

What you can do after finishing

By the end, you should be able to review the main idea of the course and practice Understand the differences between RAG, tool-augmented, and agentic AI systems, Recognize the autonomy and reasoning capabilities that define agentic AI, Implement agentic AI systems using both code-first and low-code approaches and Distinguish between AI Agents and Agentic AI terminology with more confidence.

Recommended study order

Watch the 10 lessons in the order shown on this page. Pause after each lesson to repeat the examples and only move on when the previous step makes sense.

What to study next

After finishing the course, explore the AI Coding Agents topic to find related tutorials and keep studying with more context.

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Course questions

Is this AI Coding Agents course for beginners?

No. This course covers advanced AI Coding Agents topics and is best suited to learners who already understand the fundamentals.

How many lessons are in this course?

This course currently has 10 lessons and about 17h 25min in total.

What should I do after finishing the course?

The best next step is to explore the AI Coding Agents topic and choose a related tutorial that matches what you want to practice.

Is this course free?

Yes. This is a free course organized by CursoB so you can study AI Coding Agents in a structured sequence.

Do I need previous experience to follow the course?

Yes. You should already understand the fundamentals of AI Coding Agents before starting this advanced course.

Should I watch the lessons in order?

Yes. The recommended path is to watch the 10 lessons in the order shown, because each lesson helps prepare the next one.

Can I use this course for practical projects?

Yes. Use the course as a practical reference to train Understand the differences between RAG, tool-augmented, and agentic AI systems, Recognize the autonomy and reasoning capabilities that define agentic AI, Implement agentic AI systems using both code-first and low-code approaches and Distinguish between AI Agents and Agentic AI terminology and adapt the examples to your own projects.

Does this course include a certificate?

CursoB stopped issuing certificates for its own courses in May 2026. CursoB no longer offers completion certificates. Courses and tutorials remain free to study. If content is hosted on another platform, any certificate depends exclusively on the original author or platform rules.

Are the lessons updated?

CursoB periodically reviews the AI Coding Agents catalog and may add, remove, or reorder lessons when better content is found.

Where can I find more content on this subject?

Explore the AI Coding Agents topic to find related tutorials and complementary courses.

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What each course lesson covers

Use these notes to understand the sequence before opening each lesson.

  1. 01 What is Agentic AI and How Does it Work? In this lesson, you practice Understand the differences between RAG, tool-augmented, and agentic AI systems, Recognize the autonomy and reasoning capabilities that define agentic AI and Implement agentic AI systems using both code-first and low-code approaches.
  2. 02 The Best Local Agentic Coding Workflow (Complete Guide) In this lesson, you practice Understand how local AI models work architecturally and why they differ from cloud-based solutions, Select appropriate models from Hugging Face based on hardware constraints and coding tasks and Configure and optimize models for specific hardware using quantization and performance tuning.
  3. 03 Build Your Own Claude Code | Full AI Coding Agent Tutorial In this lesson, you practice Build a complete full-stack AI coding agent from scratch, Implement real-time streaming for AI responses and tool outputs and Design and execute tool calling mechanisms for AI agents.
  4. 04 Agentic AI Crash Course using LangChain | LangChain Crash Course In this lesson, you practice Understand transformer architecture and how modern large language models function internally, Build retrieval-augmented generation (RAG) systems using vector databases and semantic search and Design and implement AI agents using the ReAct loop and multi-agent architectures.
  5. 05 Agentic Coding with Claude Code | Agentic AI Coding Tutorial | Coding With Claude Code | Simplilearn In this lesson, you practice Understand the core concepts and principles of agentic coding with AI models, Set up and configure a Node.js project integrated with Claude's API and Build a functional CLI tool that converts CSV files to JSON format.
  6. 06 Google’s Free AI vs Claude Code & Codex – Which One Should You Use? (Antigravity 2.0 Masterclass) In this lesson, you practice Set up and configure Google Antigravity 2.0 for complete beginners, Build production-ready websites without writing any code and Orchestrate multiple AI agents running in parallel for faster development.
  7. 07 NEW Open Claude Code Is A FULLY FREE AI Coding Agent! (Tutorial) In this lesson, you practice Install and configure Freebuff for immediate use with npm, Build real-world projects using autonomous AI coding agents and Compare Freebuff's performance and capabilities against Claude Code.
  8. 08 Stop Using Claude Code Without an Agentic OS In this lesson, you practice Build an effective agentic OS architecture for Claude Code systems, Implement memory layers for contextual intelligence and task continuity and Set up observability and monitoring for autonomous agent operations.
  9. 09 How to Setup AI Agent Skills for Better Code | TRAE SOLO In this lesson, you practice Understand how AI agent skills function as reusable instruction sets, Create custom skills tailored to specific coding standards and patterns and Configure prebuilt skills and adapt them to project requirements.
  10. 10 How I Turned Pi Into the Ultimate Coding Agent In this lesson, you practice Understand Pi's core architecture and why it excels as a coding agent, Customize Pi extensively to match your specific development workflows and Implement practical configurations that enhance coding productivity.