Claude Code Crash Course: Master Claude Code From Scratch

Learn to build an autonomous AI coding agent with tools, memory, workflows, execution sub-agents and context control

What you’ll learn
✓ Design and build a fully autonomous AI coding agent from scratch.
✓ Implement agentic loops that allow AI systems to plan, execute, and adapt.
✓ Create safe and extensible tool systems for file operations, shell commands, and web access.
✓ Build long-term memory and session persistence for AI agents.
✓ Manage large contexts using compaction and pruning techniques.
✓ Engineer sub-agents to handle complex tasks through multi-agent workflows.
✓ Implement feedback loops for self-correction and error recovery.
✓ Detect and prevent infinite loops in autonomous AI systems.
✓ Add approval systems, autonomy controls, and checkpoint management.
✓ Extend AI agents with custom tools and third-party services using MCP.

Requirements
● Solid understanding of Python programming.
● Ability to follow advanced, long-form technical implementations.
● Access to an LLM provider (OpenAI, Claude, or OpenRouter).
● Claude Pro Plan or Anthropic API Key

Description
This is a comprehensive, hands-on program where you will build a fully autonomous AI coding agent from scratch. Over the span of 19 hours, you will engineer a real system capable of reading and understanding an entire codebase, writing and editing files, running shell commands, searching and fetching data from the web, and managing long-running tasks autonomously.

Please note: This is an advanced course and is not suitable for complete beginners.
This curriculum is designed for experienced developers and AI practitioners who already have a solid foundation in software engineering, Python, and large language models. We will work extensively in the terminal and build a production-grade CLI and TUI-based system. Familiarity with APIs, tooling, and modern AI workflows is strongly recommended.

This course is for developers, AI engineers, and system builders who want to move beyond simple chat-based assistants and learn how to design, implement, and scale real agentic AI systems.

What This Course Is About

Modern AI tools can generate code, but real AI agents must do far more.
They must plan, execute, adapt, remember, recover from errors, and continue working even when context becomes large or tasks become complex.

In this course, you will build an AI coding agent that

• Reads and understands your codebase

• Writes, edits, and manages files safely

• Executes shell commands and tools

• Searches and fetches information from the web

• Maintains long-term memory across sessions

• Plans tasks and tracks progress

• Spawns sub-agents when complexity increases

• Detects and breaks out of infinite loops

• Learns from mistakes through feedback loops

• Compacts and prunes context to run indefinitely

• Saves sessions and restores checkpoints

• Allows controlled autonomy and approvals

• Is fully extensible with custom tools and third-party integrations using MCP

This is not a demo project. You are building a real AI system architecture that mirrors how modern autonomous coding agents are designed.

Architecture and Core Concepts You Will Master

This course takes a deep dive into agentic AI system design, covering both theory and implementation.

Agentic Loop and Execution Engine

You will design the core agentic loop that allows the AI to plan, execute actions, observe results, and iterate until tasks are complete.

Tool System and Tool Registry

Learn how to define, register, and expose tools to the AI using schemas, enabling safe and structured tool execution for file operations, shell commands, search, and more.

Context Management

Implement advanced context management strategies, including compaction and pruning, so the agent remains effective even during long-running tasks.

Memory Systems

Build memory tools that allow the agent to remember important information across turns and sessions, enabling personalization and continuity.

Sub-Agents and Multi-Agent Workflows

Design specialized sub-agents that handle specific responsibilities and are spawned dynamically when tasks grow complex.

Feedback Loops and Self-Correction

Implement mechanisms that allow the agent to recognize mistakes, adjust behavior, and avoid repeated failures.

Sessions and Checkpoints

Learn how to save, restore, and manage agent sessions, enabling long-term workflows and resumable execution.

MCP (Model Context Protocol)

Extend your agent by integrating third-party services and external tools through MCP, making the system modular and future-proof.

Practical, Real-World Implementation

Throughout the course, you will build

• A full CLI application using Click

• A rich terminal UI for agent output and interaction

• A robust configuration and error-handling system

• Approval and permission controls for safe autonomy

• Loop detection and execution guards

• A scalable architecture that users can extend with their own tools

All concepts are implemented step by step with real code, not abstractions.

What You Will Be Able to Do After This Course

By the end of this course, you will be able to

• Design and build autonomous AI coding agents from scratch

• Engineer agentic loops that plan, execute, and adapt

• Implement tool-driven AI systems with real-world safety controls

• Manage long context windows using compaction and pruning

• Build multi-agent architectures for complex workflows

• Extend AI agents with custom tools and external services

• Create production-ready AI systems, not prototypes

Who This Course Is For

• Software developers who want to build AI systems, not just use them

• AI engineers looking to master agentic architectures

• Advanced GenAI users ready to move beyond chat interfaces

• System builders interested in autonomous tooling and workflows

Who This Course Is Not For

• Complete beginners to programming

• Learners looking for basic prompt engineering

• Users seeking no-code or low-code AI solutions

This course gives you the knowledge and practical experience needed to engineer real autonomous AI coding agents, using the same principles behind modern AI developer tools.

If you are ready to move from AI usage to AI system design, this course is for you.

Who this course is for
■ Software developers who want to build real AI systems, not just use them.
■ AI engineers interested in agentic architectures and autonomous workflows.
■ Advanced GenAI users looking to move beyond chat-based assistants.
■ Developers building internal tools and AI-powered automation.
■ Engineers curious about how modern AI coding agents are designed.
■ Professionals working with large codebases and complex workflows.
■ Developers who want to understand memory, tools, and context at scale.
■ System builders interested in extensible and production-ready AI agents.
■ Learners who prefer deep, hands-on engineering over high-level theory.
■ Anyone aiming to design and deploy autonomous AI coding agents professionally.