Loop Engineering for AI Agents: Design Reliable Agentic AI Systems Beyond the Prompt

di Enrico Papalini (Autore)

Packt Publishing Limited, 2026

Design reliable agentic AI loops that act autonomously while remaining bounded, observable, independently verifiable, and accountable. Key Features Apply a minimum-sufficient-loop framework, adding complexity only when measurable failures justify it Connect agentic AI architecture with organizational design and verified-outcome economics Build reliable loops with independent verification, protected evidence, budgets, and escalation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDesigning reliable agentic AI systems requires more than writing the perfect prompt. The central challenge is designing the control system around a probabilistic worker: what it may do, what evidence proves completion, when it may retry, how much it may spend, what state must persist, and when a human must take over. This book provides a practical framework for addressing these challenges. Loop Engineering for AI Agents starts by defining loop engineering and separating it from prompt, context, and harness engineering. You'll examine how persistent AI loops change team structures, governance, management, and the economics of software production. The practical sections turn these principles into implementation through the Ralph fresh-context pattern, local loop architecture, protected verification, durable plans, retry and recovery policies, observability, prompt-injection boundaries, and human escalation. Written by Enrico Papalini, who has over 25 years of software engineering experience, the book provides a signal-first framework for adding only the mechanisms that justify their cost. By the end, you'll understand how to delegate autonomy through explicit completion contracts, independent evidence, bounded resources, authorization policies, reversible state transitions, and accountable human ownership.What you will learn Define bounded completion contracts for agentic work Separate workers from verifiers using external evidence Persist intent, state, lessons, and checkpoints outside model context Design retry, resume, rollback, and budget policies Protect specifications, verifiers, and authorization policies Choose between fresh-context loops and full orchestration Measure cost per accepted outcome instead of token usage Design human-escalation policies for reliable autonomy Who this book is forEngineering leaders evaluating or scaling agentic development workflows, senior software engineers and architects designing AI-agent control systems, and platform and developer-experience engineers building internal AI infrastructure. AI, risk, security, and governance specialists working in controlled or regulated environments will also benefit. Familiarity with software engineering principles and AI-assisted development workflows is recommended.

• Autore: Enrico Papalini
• Editore: Packt Publishing Limited
• Anno: 2026
• Rilegatura: Paperback / softback
• Pagine: 266 p.
• Dimensioni: 235 x 191 mm
• EAN: 9781808766411

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