Build, Customize, and Scale AI-Powered Development Workflows
What happens when AI-assisted coding moves beyond autocomplete and simple chat?
OpenCode for Developers provides a practical guide to building modern AI-assisted software engineering workflows using coding agents, large language models, custom agents, and Model Context Protocol (MCP) architecture.
This book takes you from the foundations of AI-assisted coding to the architecture required for reliable, secure, and scalable AI-powered development systems. You will learn how modern coding assistants work, how to design agentic workflows, and how to connect AI systems with tools, data, repositories, and development infrastructure.
Inside the Book, You Will Learn:
- How AI-assisted coding has evolved from autocomplete to autonomous development workflows
- How to design effective coding workflows using LLMs, prompts, RAG, and developer tools
- How to create custom AI agents with specialized roles, tools, memory, and behavioral constraints
- How to orchestrate multiple agents for planning, coding, testing, analysis, and deployment
- How Model Context Protocol (MCP) connects AI models with tools, data, and external resources
- How to design and deploy MCP servers and compare MCP architectures with traditional APIs
- How to protect AI development systems using guardrails, access control, sandboxing, and human approval
- How to scale model inference, vector stores, and AI-powered development infrastructure
- How to integrate LLMs into CI/CD pipelines and automated software testing
- How to monitor, trace, debug, and evaluate agent-based development systems
- How to deploy AI systems across cloud, on-premises, edge, and hybrid environments
- How engineering teams can plan and manage the transition toward AI-assisted software development
From AI Coding to AI Engineering
Rather than focusing only on generating code, this book examines the systems behind AI-assisted development. It explores agent architecture, tool integration, memory, multi-agent orchestration, MCP servers, security boundaries, performance engineering, CI/CD automation, observability, and production deployment.
You will also learn why human oversight remains essential when AI systems can execute tools, modify code, access data, and interact with development infrastructure. Practical architecture patterns and implementation examples help connect the concepts to real software engineering workflows.
Whether you are a software developer experimenting with AI coding tools, an engineer building custom agents, an architect designing MCP-based systems, or a technical leader planning AI adoption across a development team, OpenCode for Developers provides a structured foundation for building more capable and reliable AI-assisted software systems.
Learn how to move from AI-generated code to intelligent development systems that are secure, observable, scalable, and production-ready.
