Beyond the Demo: Building AI Agents That Actually Work
English | September 27, 2026 | ASIN: B0HL5DNDC6 | 198 pages | Epub | 3.11 MB
Most AI agents look impressive in a demo and fail in production. They answer from an outdated policy, quietly assume the wrong customer account, retry an action and create a duplicate, or announce that a ticket exists when they have only drafted one. Beyond the Demo is a practical engineering guide to building agents that finish a defined job under real constraints - and stop safely when they reach the limit of their authority or evidence.Instead of chasing a particular framework or SDK, this book treats an agent as an ordinary software system with a probabilistic component in the middle, and applies ordinary systems discipline to it. You follow one project from start to finish: Beacon, a fictional support assistant that answers policy questions, reads authorized account information, drafts support tickets, and creates a ticket only after a person approves the exact proposal.You will learn how to:Write an outcome contract that defines the job, the required evidence, the permitted actions, and the acceptable endingsDesign a bounded agent loop with budgets and explicit stopping conditionsMake tools trustworthy with schemas, authorization at the point of execution, and idempotent writesBuild context and retrieval (RAG) pipelines whose evidence you can inspect and citeManage state, memory, and durable work that survives retries, timeouts, and worker crashesPlan without runaway autonomyEvaluate outcomes and failure modes, not just fluent answersSecure the boundary around the model against prompt injection and hostile inputsPut humans at the right decision points, with approvals that cannot silently go staleTrace every important decision and control latency and costDecide when multiple agents actually help - and when they only add failure modesShip with canaries and rollback plans, and recover gracefully when things go wrongThe final chapter brings it all together to take Beacon from prototype to production. The appendices include a runnable control-plane lab written in standard-library Python (no API key or paid service required), a field glossary, a reusable release-review worksheet, and complete lab listings.Who this book is for: software engineers, ML and AI engineers, tech leads, and architects who can read Python and understand web services, APIs, and databases. No experience training language models is required.If you have built an agent that works on stage but not on Monday morning, this book shows you what to fix before you give it more autonomy.
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