AI Engineering Complete

Software development changed. Not incrementally — fundamentally.

Between 2025 and 2026, AI tools crossed a threshold: from autocomplete assistants to autonomous agents that plan, execute, test, and iterate on their own. The developers who understood this shift early didn't just get faster — they started

working at a completely different level.

This book is your guide to that level.

"Agentic Software Development" takes you on a progressive journey through the Claude ecosystem — from understanding the paradigm shift, to mastering Claude Code as your daily development OS, to building custom agents and multi-agent systems,

to deploying them in production CI/CD pipelines.

You'll learn:

- Why copilots and agents are fundamentally different — and what experienced devs need to unlearn

- How to use Claude Code at an advanced level: CLAUDE.md as architecture, hooks, sub-agents, worktrees

- How to design prompt systems (not just prompts) that produce reliable agent behavior

- How to build MCP servers that connect agents to your databases, APIs, and monitoring

- How to use the Claude API and Agent SDK to build autonomous agents programmatically

- Multi-agent orchestration patterns: supervisor, pipeline, and swarm — and when each one fits

- Three complete production architectures: code review, deploy/rollback, and data pipeline investigation

- How to integrate agents into CI/CD, secure them for production, and get your team on board

Written for experienced developers who already code daily. No hand-holding, no hype — just pragmatic, technical content with working code examples throughout.

The gap between "AI-curious" and "agentic-native" teams is growing. This book puts you on the right side.

▎ AI agents are the next frontier of software development. We're no longer talking about chatbots that answer questions — we're talking about autonomous systems that reason, plan, use tools, and execute complex tasks end to end.

▎ Building AI Agents from Scratch with Python is a hands-on guide that takes you from simple LLM calls to production multi-agent systems. Across 15 chapters, you build Aria — an AI agent that starts as a basic loop calling the Anthropic API

and evolves into a complete system with persistent memory, planning, RAG, multi-agent orchestration, security guardrails, and deployment with Docker and FastAPI.

▎ Each chapter adds a real layer to the project: tool calling with a decorator-based registry, short and long-term memory, planning with ReAct and replanning, state machines with checkpoints, Retrieval Augmented Generation with a custom vector

store, testing with LLM-as-Judge, prompt injection defense, and observability with metrics and alerts.

▎ By the end, you extract everything into a reusable SDK and deploy Aria as a REST API with streaming, session management, and monitoring — ready for production.

▎ This is not a theoretical book. Every concept comes with complete, runnable Python code, practical exercises at three difficulty levels, and a common mistakes section that will save you weeks of debugging.

▎ Who this book is for:

▎ - Intermediate Python developers who want to build real AI agents

▎ - Engineers already using LLMs who want to go beyond simple wrappers

▎ - Teams that need to ship agents to production with security and observability

▎ The Model Context Protocol (MCP) is the open standard that connects AI models to the outside world. While REST APIs were designed for applications, MCP was designed for LLMs to use tools, access data, and execute actions in a structured,

secure way.

▎ MCP in Practice is the hands-on guide that takes you from protocol to production. Across 14 chapters, you build DevKit MCP — a suite of MCP servers that expose Git operations, API testing, and external service integrations to any AI

assistant that supports the protocol.

▎ You learn the three MCP primitives (Tools, Resources, and Prompts), implement both stdio and Streamable HTTP transports, integrate with external APIs like GitHub, add authentication and security with JWT and sandboxing, write automated tests

with InMemoryTransport, compose multiple servers with proxy routing, build custom MCP clients, and deploy with Docker and npm.

▎ Each chapter adds a real layer to the project, with complete TypeScript code, practical exercises at three difficulty levels, and a common mistakes section that saves you hours of debugging.

▎ Who this book is for:

▎ - Developers who want to build MCP servers for their tools and APIs

▎ - Teams integrating AI with internal systems (databases, APIs, repositories)

▎ - Anyone working with Claude Code, Claude Desktop, Cursor, or VS Code who wants to extend their AI assistant's capabilities

▎ Monitoring tells you something is broken. Observability tells you why. In a world of microservices, distributed systems, and complex deployments, the difference between the two is the difference between hours of debugging and minutes.

▎ Practical Observability with OpenTelemetry is a hands-on guide that takes you from console.log to full production observability. Across 14 chapters, you build the PetShop API — a Node.js multi-service system that starts as a monolith and

evolves into a fully observable microservices architecture with distributed traces, custom metrics, structured logs, and real-time alerting.

▎ You learn OpenTelemetry from the ground up: auto-instrumentation that gives you visibility in minutes, manual instrumentation for business-critical spans, distributed traces across multiple services, custom metrics with Prometheus,

structured logs correlated with traces, and a complete observability stack with Jaeger, Grafana, and Alertmanager.

▎ The book covers production-ready patterns: sampling strategies to control costs, SLOs and error budgets for reliability, security and PII sanitization, multi-environment configuration, and incident response workflows powered by real

telemetry data.

▎ Who this book is for:

▎ - Backend developers who want to understand what their systems are doing in production

▎ - SREs and DevOps engineers implementing observability pipelines

▎ - Engineering managers who need to set SLOs and build on-call practices

You have an idea for an app. You don't know how to code. Until recently, that meant hiring a developer, learning to program, or giving up.

Not anymore.

Vibe coding is the revolution that lets you build real, functional applications by describing what you want in plain English. AI tools like Cursor, Bolt.new, Replit, and v0 translate your descriptions into working software — no programming

knowledge required.

This book takes you from zero to launching production apps:

- Build your first app in 30 minutes (Chapter 2 — you'll be surprised)

- Choose the right tool for your project: Cursor vs Bolt.new vs Replit vs v0

- Build a landing page, dashboard, customer portal, and internal business tool

- Connect to real services: payments (Stripe), email, databases, APIs

- Make it look professional without design experience

- Debug when things break — prompt iteration strategies that work

- Deploy to production so real users can access your app

Written for entrepreneurs, designers, product managers, and anyone with ideas who doesn't write code. Each chapter builds on the previous, and by the end you'll have shipped multiple real applications.

63% of vibe coding users today are non-developers. The barrier to building software has never been lower. This book shows you exactly how to cross it.