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{ "item_title" : "AutoGPT Engineering Mastery", "item_author" : [" Danny Munrow", "Damon Reeves Ashford "], "item_description" : "Reactive PublishingAutonomous agents are no longer experimental-they are redefining how modern enterprises build, operate, and scale intelligent systems.AutoGPT Engineering Mastery is the definitive guide to designing, orchestrating, and deploying next-generation autonomous AI agents from the ground up. Whether you are building your first AutoGPT prototype or architecting a fully autonomous enterprise platform, this book gives you the technical depth, patterns, and real-world frameworks required to execute at scale.Written by Damon Reeves Ashford, this handbook distills the emerging discipline of agent engineering into a practical, end-to-end blueprint. You will learn not only how to build autonomous agents, but why they fail, how to stabilize them, and how to integrate them into mission-critical infrastructure.Inside, You Will Learn: Design Principles for Autonomous Agents- Core components of AutoGPT systems- Planning, tool-use, memory, and self-correction loops- How to structure goals, constraints, and agent personas for reliabilityEngineering Production-Grade Architectures- Action-selection pipelines- State management and deterministic routing- Re-entrant task loops, critic models, and recovery patternsLangGraph, CrewAI, and Multi-Agent Systems- Building graph-based autonomous workflows- Coordinating multi-agent teams with specialized roles- Designing competitive, collaborative, and hierarchical agent structuresScaling From Prototype to Enterprise Deployment- API orchestration, observability, and telemetry- Vector memory engineering for long-term context- Performance tuning, cost optimization, and reliability engineeringFailure Modes and Stabilization Techniques- Detecting hallucination cascades- Preventing infinite reasoning loops- Hardening tool invocation pathways- Building robust fallback and checkpointing mechanismsSecurity, Governance, and Compliance- Permission models and guardrails- Enterprise-grade risk management- Auditing autonomous decisions and actionsWho This Book Is ForThis book is designed for: - AI engineers and LLM developers- CTOs, architects, and technical leaders- Automation and RPA professionals upgrading to agentic systems- Researchers designing the next generation of intelligent infrastructure- Builders who want to move beyond prompting into real autonomous systemsIf you want to understand how to architect, deploy, and scale autonomous agents that operate reliably in production, this is your handbook.Autonomous agents are the next major platform shift in software.This book shows you exactly how to build them.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/827/750/9798277506233_b.jpg", "price_data" : { "retail_price" : "33.99", "online_price" : "33.99", "our_price" : "33.99", "club_price" : "33.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
AutoGPT Engineering Mastery|Danny Munrow

AutoGPT Engineering Mastery : A Comprehensive Guide: Designing, Deploying, and Scaling Autonomous AI Agents from Prototype to Production

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Overview

Reactive Publishing

Autonomous agents are no longer experimental-they are redefining how modern enterprises build, operate, and scale intelligent systems.
AutoGPT Engineering Mastery is the definitive guide to designing, orchestrating, and deploying next-generation autonomous AI agents from the ground up. Whether you are building your first AutoGPT prototype or architecting a fully autonomous enterprise platform, this book gives you the technical depth, patterns, and real-world frameworks required to execute at scale.

Written by Damon Reeves Ashford, this handbook distills the emerging discipline of agent engineering into a practical, end-to-end blueprint. You will learn not only how to build autonomous agents, but why they fail, how to stabilize them, and how to integrate them into mission-critical infrastructure.


Inside, You Will Learn:
Design Principles for Autonomous Agents

- Core components of AutoGPT systems
- Planning, tool-use, memory, and self-correction loops
- How to structure goals, constraints, and agent personas for reliability

Engineering Production-Grade Architectures

- Action-selection pipelines
- State management and deterministic routing
- Re-entrant task loops, critic models, and recovery patterns

LangGraph, CrewAI, and Multi-Agent Systems

- Building graph-based autonomous workflows
- Coordinating multi-agent teams with specialized roles
- Designing competitive, collaborative, and hierarchical agent structures

Scaling From Prototype to Enterprise Deployment

- API orchestration, observability, and telemetry
- Vector memory engineering for long-term context
- Performance tuning, cost optimization, and reliability engineering

Failure Modes and Stabilization Techniques

- Detecting hallucination cascades
- Preventing infinite reasoning loops
- Hardening tool invocation pathways
- Building robust fallback and checkpointing mechanisms

Security, Governance, and Compliance

- Permission models and guardrails
- Enterprise-grade risk management
- Auditing autonomous decisions and actions


Who This Book Is For

This book is designed for:

- AI engineers and LLM developers
- CTOs, architects, and technical leaders
- Automation and RPA professionals upgrading to agentic systems
- Researchers designing the next generation of intelligent infrastructure
- Builders who want to move beyond prompting into real autonomous systems

If you want to understand how to architect, deploy, and scale autonomous agents that operate reliably in production, this is your handbook.


Autonomous agents are the next major platform shift in software.

This book shows you exactly how to build them.

This item is Non-Returnable

Details

  • ISBN-13: 9798277506233
  • ISBN-10: 9798277506233
  • Publisher: Independently Published
  • Publish Date: December 2025
  • Dimensions: 9 x 6 x 1.04 inches
  • Shipping Weight: 1.11 pounds
  • Page Count: 418

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