Mastering AI Reasoning Systems : A Hands-On Guide to Building Hybrid Reasoning Models, Intelligent Agents, and Enterprise AI Applications

Cedric Wynthorne

ISBN 13: 9798193004561

Published by Independently Published Aug 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798193004561

Bibliographic Details

• Title: Mastering AI Reasoning Systems : A Hands-On Guide to Building Hybrid Reasoning Models, Intelligent Agents, and Enterprise AI Applications
• Publisher: Independently Published Aug 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798193004561
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 11D millimeters 254 millimeters width by 178 millimeters height by 11 millimeters depth
• Weight: 351 grams

About this title

Synopsis

Build production-ready AI systems that can reason, retrieve information, use tools, and operate reliably.
Large language models can generate impressive responses, but building dependable AI applications requires much more than calling an API. Production reasoning systems need structured workflows, retrieval, tool integration, evaluation, security, observability, and operational controls.
Mastering AI Reasoning Systems takes a practical, systems-engineering approach to building these capabilities with Python. Using the Compass platform as a continuous example, you will progressively build a reasoning architecture from its foundations through production deployment.
Inside, you will learn how to:

• Design hybrid reasoning architectures and intelligent routing
• Build retrieval and planning pipelines
• Connect AI systems to databases, APIs, and external tools
• Develop reliable tool-using agents
• Evaluate reasoning quality with repeatable tests
• Implement explainability, security, and failure controls
• Optimize latency, token usage, and operating costs
• Build APIs and operational interfaces
• Add monitoring, observability, and incident practices
• Containerize and deploy reasoning systems to cloud infrastructure

Rather than treating reasoning as a feature of the model alone, this book treats it as an engineering problem. Models, data, retrieval, tools, evaluation, and infrastructure are brought together into a coherent system designed for real-world workloads.
Whether you are an AI engineer, Python developer, ML engineer, software architect, or technical professional building LLM-powered applications, this book provides a practical path from simple model calls to engineered reasoning systems.
A companion repository provides the runnable implementations used throughout the book.
Build the architecture. Connect the tools. Measure the reasoning. Engineer the system.