Agentic Coding for beginners
Agentic Coding for beginners
The Future of Software Development with AI Agents, Copilot, and Context-Aware Coding
About the Book
About This Book
Agentic Coding for Beginners: A Practical Guide to Building AI-Assisted Workflows with ChatGPT, Copilot, and Multi-Agent Systems is designed for software engineers, technical learners, and curious builders exploring the next phase of intelligent software development. The book introduces agentic coding, a structured approach in which AI agents plan, act, evaluate, and improve through continuous feedback.
Who This Book Is For
This book is written for developers ready to move beyond passive AI assistance into active collaboration with intelligent systems. Whether you use VS Code, GitHub Copilot, Cursor, or ChatGPT, this guide will help you understand how to design autonomous workflows that enhance productivity and reliability.
You don’t need a deep AI background to start. Basic programming knowledge and curiosity about automation are enough. Each section is written to be practical, clear, and directly applicable to real development workflows.
What You’ll Learn
You’ll explore the foundations and practice of agentic coding through examples, walkthroughs, and conceptual discussions:
* How agents plan, execute, and self-evaluate tasks
* Setting up your development environment for agentic workflows
* Designing single- and multi-agent systems using frameworks like CrewAI and AutoGen
* Managing memory, context, and iterative feedback loops
* Ensuring safety, alignment, and human oversight in autonomous systems
* Applying constructionist learning to enhance agent performance
Each concept builds toward mastery, preparing you to build and refine your own intelligent coding agents.
Why It Belongs on Leanpub
Leanpub’s iterative publishing model aligns perfectly with the spirit of agentic development. Just as agents learn by doing, this book grows with the ecosystem, evolving as tools, frameworks, and best practices advance. Readers gain lifetime access to updates, new examples, and refined workflows as the field progresses.
How to Read This Book
Start at the beginning if you’re new to agentic systems, or jump into practical chapters if you prefer learning by experimentation. The book is structured so that theory, design, and implementation reinforce each other. Real projects and walkthroughs anchor the concepts, ensuring you can apply them directly in your workflow.
This is not just a technical manual. It’s a guide to building confidence, understanding, and creativity with intelligent tools, an essential step toward mastering the future of software development.
About the Author
Bundles that include this book
Table of Contents
- Introduction
- What Is Agentic Coding?
- How Agentic Coding Works
- 1. Intent
- 2. Interpretation
- 3. Generation
- 4. Validation
- Communicating with Agents
- 1. From Conversation to Collaboration
- 2. The Role of Context
- 3. Structure Over Style
- 4. Iteration, Not Expectation
- 5. Common Communication Mistakes
- Setting Up Your Agentic Environment
- 1. Choosing the Right Editor
- 2. Installing AI Coding Tools
- 3. Configuring a Secure Workspace
- 4. Preparing Your Language Environment
- 5. Testing Your Setup
- Planning and Decomposing Tasks
- 1. Understanding Task Decomposition
- 2. Hierarchical Planning
- 3. Using Agents for Task Decomposition
- 4. Iterative Task Execution
- 5. Benefits of Structured Planning
- Tool Use and External Interaction
- 1. Understanding Tools as Executors
- 2. Connecting Agents to Tools
- 3. Essential Coding Tools
- 4. Best Practices for Tool Interaction
- 5. Real-World Example
- Observation and Reflection
- 1. Closing the Feedback Loop
- 2. Structured Self-Correction
- 3. Observing Tool Outputs
- 4. Learning from Iteration
- Memory, Context, and RAG Implementation
- 1. The Context Window Challenge
- 2. Scaling Memory with Persistent Storage
- 3. Retrieval-Augmented Generation (RAG)
- Selecting the Right Agentic Framework
- 1. Low-Code Entry Points
- 2. Advanced and Customizable Frameworks
- Phase 1: Context Definition and the Requirements Contract
- 1. Defining the Requirements Contract
- 2. Advanced Prompt Engineering for Agentic Tasks
- Phase 2 & 3: Autonomous Execution, Evaluation, and Self-Correction
- 1. Executing Code Safely
- 2. Mandatory Self-Verification
- 3. Error Diagnosis and Resilience Architecture
- Governance and Human-in-the-Loop (HITL) Oversight
- 1. The Human-in-the-Loop Role
- 2. Security Best Practices Against Adversarial Threats
- Hands-On Project I: Simple Agent Development (Python/LangChain)
- 1. Document Summarizer Agent
- 2. FAQ Answering Agent with RAG
- 3. Integrating External Tools in Python
- Hands-On Project II: Designing Multi-Agent Systems (CrewAI/AutoGen)
- 1. Choosing the Right Architecture: Single vs. Multi-Agent
- 2. Collaborative Code Review Workflow
- 3. LLM Orchestrator vs. Deterministic Workflows
- Advanced Agentic Workflow Techniques
- 1. Improving Through Constructionist Learning
- 2. The System Prompt as an Orchestrator
- Scaling, Trust, and Technical Gaps
- 1. Toolchain and Language Incompatibility
- 2. Inadequate Evaluation and Benchmarking
- 3. Safety, Alignment, and Trust
- Conclusion: The Autonomous Future of Software Development
- 1. Summary of Agentic Mastery
- 2. Continuing the Learning Journey
- 3. Final Thoughts
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