Building AI Agents for Engineers
Building AI Agents for Engineers
Best Practices for Product Development129,99 €
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- ISBN: 978-1-56990-562-3
- Erscheinungsdatum: 08/2026
- Seitenanzahl: 250
- Auflage: 1
- Sprache: Englisch
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Produktsicherheit:
Carl Hanser Verlag GmbH & Co KG
Vilshofener Str. 10
81679 München
E-Mail: info@hanser.de
Sicherheitshinweis entsprechend Art.9 Abs. 7 Satz 2 GPSR entbehrlich
Produktinformationen "Building AI Agents for Engineers"
While rule-based automation follows predefined instructions to perform repetitive tasks, AI agents act autonomously and are able to make decisions in complex, dynamic situations. This book shows how to build AI agents for product development, particularly hardware development. It is aimed at development engineers, designers and engineering managers.
The book covers the following topics:
- Basic principles and architectures of agentic systems
- Technological components of agentic systems: frameworks and patterns for designing agents as well as interfaces to CAx, PLM, and ERP systems
- Governance, change management, and role responsibilities
- Outlook: vision of an Autonomous Data Pipeline
Step by step, the books demonstrates how AI agents can be developed by using low-code platforms. It also considers Multi-Agent Systems (MAS). MAS go one step further by combining several AI agents that interact with each other to achieve common or individual goals. The book provides criteria for selecting pilot projects and guides you through the project phases of the implementation. Use cases from the industry—from automotive tier suppliers to consumer goods OEMs up to engineering service providers—provide valuable practical insight. They show how AI agents can reduce costs in product development and provide a competitive advantage through shorter time-to-market cycles.
The book covers the following topics:
- Basic principles and architectures of agentic systems
- Technological components of agentic systems: frameworks and patterns for designing agents as well as interfaces to CAx, PLM, and ERP systems
- Governance, change management, and role responsibilities
- Outlook: vision of an Autonomous Data Pipeline
Step by step, the books demonstrates how AI agents can be developed by using low-code platforms. It also considers Multi-Agent Systems (MAS). MAS go one step further by combining several AI agents that interact with each other to achieve common or individual goals. The book provides criteria for selecting pilot projects and guides you through the project phases of the implementation. Use cases from the industry—from automotive tier suppliers to consumer goods OEMs up to engineering service providers—provide valuable practical insight. They show how AI agents can reduce costs in product development and provide a competitive advantage through shorter time-to-market cycles.
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Tobias Wigand
Tobias Wigand is a trained mechanical engineer with over 15 years of experience in product development. He currently works as an Account Executive at Synera GmbH.
Ram Dhiwakar Seetharaman
Ram Dhiwakar Seetharaman is Head of AI and Product Manager at Synera GmbH. He has a background in computational mechanics and has more than 5 years of experience in building AI for engineering.
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