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Running Agentic AI Systems: Step-by-Step Walkthroughs, a Prompt Playbook, and an Actively Maintained GitHub Repo to Test, Harden, and Ship
LAK 986403
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This is not a theoretical essay. It is a practical field manual that treats agents as real systems, with brains, bodies, memories, limits, metrics, and failure modes you can actually manage.
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What Stands Out
Product Details
| Publisher | Independently published |
| Publication date | December 2, 2025 |
| Language | English |
| Print length | 266 pages |
| ISBN-13 | 979-8277080726 |
| Item Weight | 1.7 pounds (770 grams) |
| Dimensions | 8.5 x 0.6 x 11 inches (21.6 x 1.5 x 27.9 cm) |
Who Should Buy?
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AI Developers
This product provides resources and tools for developers looking to create and maintain AI agents effectively.
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Technical Teams
Teams involved in AI projects can benefit from the structured workflows and GitHub repo to improve collaboration.
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AI Researchers
Researchers can leverage the prompt playbook to explore and advance their work in developing agentic systems.
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Non-Technical Users
Users without technical backgrounds may find the product's complexity overwhelming and hard to implement.
Product Description
Running Agentic AI Systems: Step-by-Step Walkthroughs, a Prompt Playbook, and an Actively Maintained GitHub Repo to Test, Harden, and Ship Production-Ready AI Agents with Repeatable Workflows
Customer Questions & Answers
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Question:
Who is this product for?
Answer: This product is designed for leaders and engineers looking to build reliable, production-ready AI agents. -
Question:
What will I learn from this product?
Answer: You will learn to design, test, and deploy AI systems with proven workflows and methods to enhance efficiency. -
Question:
What makes this guide different from others?
Answer: It offers practical, actionable steps and a hands-on GitHub repo, rather than just a theoretical overview of AI.
Natural Language Processing Editorial Review
**Editorial Review of "Running Agentic AI Systems"** "Running Agentic AI Systems" has garnered substantial acclaim for its accessible and pragmatic approach to a complex subject. Reviewers have highlighted the book's clear step-by-step guides and comprehensive prompt playbook as invaluable resources for both novices and seasoned professionals in the field of artificial intelligence. The inclusion of an actively maintained GitHub repository further enhances its practicality, allowing users to build and test real AI agents efficiently. Readers appreciate the well-structured format, which not only demystifies the workings of agentic AI but also assists developers in overcoming common obstacles in system design and deployment. The book is described as a "field manual," emphasizing its hands-on approach that prioritizes real-world application over theoretical discussions. Users found that the content bridges the gap between design and production, empowering them to create stable, repeatable systems with confidence. This resonates particularly well with individuals engaged in the robotics and automation sectors, making it a must-have for anyone looking to enhance their understanding and capabilities in AI development. With its robust presentation, practical advice, and tangible tools, "Running Agentic AI Systems" has quickly risen to prominence on platforms like Amazon, securing ranks among the top new releases in its category. Overall, this book appears to be an essential addition for those looking to harness the power of agentic AI systems effectively. **
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Pros
- Clear, practical step-by-step guides
- Comprehensive prompt playbook
- Actively maintained GitHub repository for real-world testing
- Well-structured and easy-to-follow format
- Focus on practical application rather than theory
- Empowering for developers in creating stable systems
- Highly recommended for professionals in AI and automation sectors
Platform Trust & Buyer Confidence
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Product Price History
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LAK 986403
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Features & Benefits
- Step-by-step walkthroughs to build reliable AI systems.
- Focused prompt playbook for stable cognitive contracts.
- Guidance on integrating and optimizing foundation models.
- Strategies for handling complex tasks with transparency.
- Tools for measuring and improving AI agent performance.
- Practical manual for real-world application and production readiness.
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