Jev in Practice for AI Agents and Workflow Automation : A Developer's Guide to Routing, Evaluation, and Human Review
Language: English
Published by Independently Published Okt 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 39.21
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Add to basketItem description from seller
Neuware - An automated assistant can choose the right answer and still take the wrong action. What happens when it routes a request to the wrong team, repeats a tool operation after a timeout, or acts on evidence that has already changed Building dependable decision workflows takes more than a convincing model response. Developers need explicit contracts, permission checks, review pathways, controlled retries, and evaluation that measures what the software actually did. Without these boundaries, a well-formed prediction may become an unauthorized or duplicate effect.This practical developer guide shows how to place model-based interpretation inside a testable application workflow. Using a fictional service desk as a continuing example, it moves from a single routing decision to a structured system with policy enforcement, human review, durable execution, evidence retrieval, and operational monitoring.Work through the practical techniques to: - Build a narrow adapter that validates structured model responses and handles unavailable services explicitly.- Specify candidate options, precedence rules, missing evidence, and contract versions before implementation.- Connect predictions to registered operations while checking scope, identity, and current state.- Choose when to automate, defer, block, or request human review using measured tradeoffs.- Preserve operation identity through durable state, duplicate deliveries, timeouts, and bounded retries.- Assemble repeatable evaluation datasets and workflow tests that record failures as well as successes.- Plan staged releases with monitoring, rollback conditions, and a controlled effects switch.Fourteen chapters provide learning objectives, worked cases, implementation labs, practice questions, and answer keys. Diagrams and tables clarify component responsibilities. A complete offline capstone joins the major concepts into one executable teaching application, followed by a deployment checklist and glossary for reference.The intended reader is a software developer, integration engineer, or technical automation practitioner who already understands basic programming structures and wants a systematic way to reason about agent behavior. Examples illustrate architecture and tests; they are not evidence of a live service meeting production targets.>Get your copy and learn to distinguish a model prediction from an application decision-and an application decision from a safe, verified effect.…
Seller Inventory # 9798165824517
- Title
- Jev in Practice for AI Agents and Workflow Automation : A Developer's Guide to Routing, Evaluation, and Human Review
- Author
- Miro Thalden
- Publisher
- Independently Published Okt 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798165824517
- Item weight
- 354 grams
- Dimensions
- 254x178x11 mm
What happens when it routes a request to the wrong team, repeats a tool operation after a timeout, or acts on evidence that has already changed?
Building dependable decision workflows takes more than a convincing model response. Developers need explicit contracts, permission checks, review pathways, controlled retries, and evaluation that measures what the software actually did. Without these boundaries, a well-formed prediction may become an unauthorized or duplicate effect.
This practical developer guide shows how to place model-based interpretation inside a testable application workflow. Using a fictional service desk as a continuing example, it moves from a single routing decision to a structured system with policy enforcement, human review, durable execution, evidence retrieval, and operational monitoring.
Work through the practical techniques to:
• Build a narrow adapter that validates structured model responses and handles unavailable services explicitly.
• Specify candidate options, precedence rules, missing evidence, and contract versions before implementation.
• Connect predictions to registered operations while checking scope, identity, and current state.
• Choose when to automate, defer, block, or request human review using measured tradeoffs.
• Preserve operation identity through durable state, duplicate deliveries, timeouts, and bounded retries.
• Assemble repeatable evaluation datasets and workflow tests that record failures as well as successes.
• Plan staged releases with monitoring, rollback conditions, and a controlled effects switch.
Fourteen chapters provide learning objectives, worked cases, implementation labs, practice questions, and answer keys. Diagrams and tables clarify component responsibilities. A complete offline capstone joins the major concepts into one executable teaching application, followed by a deployment checklist and glossary for reference.
The intended reader is a software developer, integration engineer, or technical automation practitioner who already understands basic programming structures and wants a systematic way to reason about agent behavior. Examples illustrate architecture and tests; they are not evidence of a live service meeting production targets.
Written for developers and automation engineers comfortable with basic programming and web APIs, this is not a first programming course or a promise of measured model performance. The worked service-desk data and capstone predictions are synthetic, and live deployment requires further environment-specific controls.
Get your copy and learn to distinguish a model prediction from an application decision—and an application decision from a safe, verified effect.
"Synopsis" may belong to another edition of this title.
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