Harness Engineering: Building Reliable AI Agents That Actually Deliver - Couverture souple
Livre 2 sur 5: AI and Agentic Engineering
Synopsis
Everyone in the room saw the agent work. That is the problem.
In production it fails one run in twelve, never the same way twice, and you carry the pager. You are not stuck because the agent cannot do the work. You watched it do the work. You are stuck because doing it once, on stage, with you watching is the easy part, and doing it every time, while you sleep, is the job nobody named for you.
Harness Engineering names that gap: the demo cliff, and the climb back up. This is not a book about building an agent. You already built one. This is the book about making it deliver: the evals, verification, guardrails, observability, and recovery that turn an impressive toy into a production LLM application you would put your name on the pager for.
The engineer's toolkit for reliable AI agents that actually deliver:
- Reliability as a number: a success rate against a fixed task set, tracked run over run, so better is a measurement instead of a feeling.
- The eval gate: agent evals run like CI, blocking a bad change before it ships.
- The verification wall: cheap checks first, expensive ones behind them, catching wrong output before a user ever does.
- The recovery path for the failures you cannot prevent: retry with judgment, fall back, escalate, roll back.
- Run records: LLM observability and monitoring that capture what the agent actually did, so a bad run is a query instead of an archaeology dig.
- The reliability budget for one real agent: a target you can defend and the guardrails that hold it.
Read it and you will measure your agent instead of demoing it, ship changes behind an eval gate, catch wrong answers before your users do, and answer the 3 AM page with a query instead of a guess. A reliable agent still fails. Its failures are rare, cheap, caught before the user, and survivable when they are not. That is software reliability engineering, the discipline SRE brought to servers, applied to agents. This book is the engineering.
For software, ML, and platform engineers who already run agentic loops and now own one in production. Part of the AI and Agentic Engineering series.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Harness Engineering: Building Reliable AI Agents That Actually Deliver (AI and Agentic Engineering)
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Harness Engineering
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Harness Engineering (Paperback)
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Paperback. Etat : new. Paperback. Everyone in the room saw the agent work. That is the problem. In production it fails one run in twelve, and you carry the pager.You are not stuck because the agent cannot do the work. You watched it do the work. You are stuck because doing it once, on stage, with you watching is the easy part, and doing it every time, while you sleep, is the job nobody named for you. This book names it: the demo cliff. Harness Engineering is the climb back up.You already built an agent. This book is about making it deliver. Evals, verification, guardrails, observability, recovery: the scaffolding that turns an impressive toy into a system you would put your name on the pager for.A reliable agent still fails. Its failures are rare, cheap, caught before the user, and survivable when they are not. That is engineering, and this book is the engineering.What you buildReliability as a number: a success rate against a fixed task set, tracked run over run.An eval gate that blocks a bad change before it ships, run like CI.A verification wall that catches wrong output before a user does, cheap checks first, expensive ones behind them.A recovery path for the failures you cannot prevent: retry with judgment, fall back, escalate, roll back.Run records of what the agent actually did, so a bad run is a query instead of an archaeology dig.A reliability budget for one real agent: a target you can defend and the guardrails that hold it.Who it is forSoftware, ML, and platform engineers who already run agentic loops and now own one in production. You shipped the feature, the demo recording is still in the channel, and the 3 AM page lands on you. If that is your week, this is your book.About the authorWes Halloran writes practical books for working developers and engineering leaders in the shift to AI-agent development. He has shipped agents that failed in ways no demo hinted at, so this book trusts measured runs over impressive ones. He works from real tool behavior and verified workflows, and never accepts a green light he did not check.Part of the AI and Agentic Engineering series. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798181738430
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Harness Engineering : Building Reliable AI Agents That Actually Deliver
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Neuware - Everyone in the room saw the agent work. That is the problem. In production it fails one run in twelve, and you carry the pager.You are not stuck because the agent cannot do the work. You watched it do the work. You are stuck because doing it once, on stage, with you watching is the easy part, and doing it every time, while you sleep, is the job nobody named for you. This book names it: the demo cliff. Harness Engineering is the climb back up.You already built an agent. This book is about making it deliver. Evals, verification, guardrails, observability, recovery: the scaffolding that turns an impressive toy into a system you would put your name on the pager for.A reliable agent still fails. Its failures are rare, cheap, caught before the user, and survivable when they are not. That is engineering, and this book is the engineering.What you build- Reliability as a number: a success rate against a fixed task set, tracked run over run.- An eval gate that blocks a bad change before it ships, run like CI.- A verification wall that catches wrong output before a user does, cheap checks first, expensive ones behind them.- A recovery path for the failures you cannot prevent: retry with judgment, fall back, escalate, roll back.- Run records of what the agent actually did, so a bad run is a query instead of an archaeology dig.- A reliability budget for one real agent: a target you can defend and the guardrails that hold it.Who it is forSoftware, ML, and platform engineers who already run agentic loops and now own one in production. You shipped the feature, the demo recording is still in the channel, and the 3 AM page lands on you. If that is your week, this is your book.About the authorWes Halloran writes practical books for working developers and engineering leaders in the shift to AI-agent development. He has shipped agents that failed in ways no demo hinted at, so this book trusts measured runs over impressive ones. He works from real tool behavior and verified workflows, and never accepts a green light he did not check.Part of the AI and Agentic Engineering series. N° de réf. du vendeur 9798181738430
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