Agentic Engineering

Build a complete AI agent from scratch — from a simple API call to a production multi-agent system with memory, planning, RAG, security, and deployment. 15 chapters, one real project, complete Python code.

Construa um AI agent completo do zero — de uma chamada simples à API até um sistema multi-agente em produção com memória, planejamento, RAG, segurança e deploy. 15 capítulos, um projeto real, código Python completo.

What if agentic AI was less about hype and more about work you can actually inspect?OpenClaw and Hermes for Agentic AI is a practical field guide to building with local agents, memory, tools, sessions, cron jobs, and ACP bridges. It shows how to turn fuzzy ideas into reliable workflows with clear boundaries, real artifacts, and human review where it matters most.If you want a grounded look at how agent systems really operate, this book starts there.

Master AI integration on Apple platforms by bridging Swift 6 with OpenAI, LangChain, and autonomous agents. Build high-performance RAG pipelines using hardware-accelerated vector math and persistent local semantic memory. Architect thread-safe, real-time apps with strict concurrency, intelligent function calling, and efficient token streaming. Move from basic API calls to production-grade intelligence with the definitive guide for modern Apple developers.

Most writing about AI and infrastructure stops at the demo. This book starts on the day the model is confidently wrong at 3 a.m. and an auditor asks who authorised the action it took. Seven working labs — MCP servers with real identity and audit, closed-loop remediation behind a reversibility gate, and autonomy that can be revoked — for platform engineers in safety-critical and regulated industries.

Stop building chatbots and start building agents that interact directly with the Android OS. Master Gemini Nano and AICore to architect autonomous systems that see, think, and take action. Implement production-ready Tool Calling, Screen Awareness, and the ReAct loop using Kotlin 2.x. Move beyond the API call—embrace the Agentic Era and build the future of mobile intelligence.

Claude Code users routinely spend more on tokens than they expect, and cannot see where the money goes. Chapter 8 catalogues 48 diagnostic symptoms — what each one looks like, what it costs you, and what to change — with a fix where a fix exists, and an honest "unresolved" where none does yet. Chapter 7 puts before-and-after numbers against the changes, measured on the author's own unattended runs, and Chapter 9 collects the settings templates.

This book presents an architecture-first approach to designing trustworthy GenAI applications. Using Digital Forensics and Incident Response (DFIR) as a continuous case study, you will progressively build an AI-assisted investigation system. If you want to move beyond building AI applications that simply work, and start architecting AI systems that professionals can trust, this book is for you.

A governance framework for taming complexity in distributed systems. From solo platforms to AI agents.

An AI system becomes an architecture problem when it can influence decisions, invoke tools, carry state or change the outside world. This practitioner playbook shows how to design Gen AI and Agentic AI systems that remain reliable, governable and defensible once they reach production.

I strongly recommend this book to developers, platform engineers, and technical architects who work with Microsoft 365. These professionals will learn to integrate AI-assisted workflows into existing business processes without unnecessary complexity.

Evolve from building static applications to architecting sophisticated cognitive systems. Master the Microsoft Semantic Kernel to bridge deterministic C# code with LLM reasoning. Build autonomous agents that plan, execute, and adapt using advanced enterprise patterns. Stop writing scripts and start engineering the future of intelligent .NET software.

Stop scripting and start engineering the next generation of cloud-native AI. Master C# and Kubernetes to build resilient, distributed agent swarms at scale. Bridge the gap between experimental logic and high-performance, production-grade systems. Architect the future of intelligence—from event-driven scaling to secure service meshes.