Overview

This book examines strategies for distributing AI agents across diverse computing environments, with emphasis on achieving portability through containerization, orchestration frameworks, and model-agnostic architectures. It addresses reproducible configuration practices using infrastructure-as-code principles, version-controlled prompt and tool definitions, and dependency isolation to support consistent behavior in production settings. The content explores integration patterns for leveraging capabilities from Anthropic's Claude models and OpenAI's GPT series within unified agent workflows, including API routing, multi-model orchestration, context management, and tool interoperability via protocols such as MCP. Discussions cover deployment considerations across cloud providers, edge devices, and hybrid infrastructures, alongside observability, versioning, and scaling techniques suitable for enterprise-grade implementations. Intended for software engineers, DevOps professionals, and AI architects with prior experience in LLM-based systems and cloud deployment, this volume provides detailed technical insights rather than introductory concepts. Enhance your approach to building resilient, platform-independent agent systems-add this reference to your professional library today.

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