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242970796
Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based agentic AI framework with tool use, memory, and multi-agent workflows
88% of respondents would recommend this to a friend
зүйлийн дугаар:
242970796
MNT 264642
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Яг одоо захиалаад ойрын хугацаанд хүлээн аваарай Бямба гараг, 9 дүгээр сар 26
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Engineer your own Python-based agentic AI framework with tool use memory and multi-agent workflows.
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What Stands Out
Customizable Framework
Build tailored multi-agent AI systems using a Python-based framework that allows for extensive customization and flexibility, addressing specific project requirements efficiently.
Tool Utilization
Incorporate advanced tool use capabilities that enhance agent functionality, enabling complex tasks and interactions that set this framework apart from traditional AI systems.
Memory Integration
Utilize integrated memory systems that improve learning and decision-making for agents, ensuring more intelligent and responsive interactions across collaborative multi-agent environments.
Бүтээгдэхүүний дэлгэрэнгүй мэдээлэл
| Publisher | Packt Publishing |
| Publication date | 27 Feb. 2026 |
| Language | English |
| Print length | 536 pages |
| ISBN-10 | 1806116472 |
| ISBN-13 | 978-1806116478 |
| Item weight | 912 g |
| Dimensions | 19.05 x 3.07 x 23.5 cm |
Who Should Buy?
-
AI Developers
Developers looking to create complex multi-agent systems in Python will find this framework extremely beneficial and efficient.
-
Research Scientists
Researchers studying distributed systems or multi-agent collaboration will gain significant insights and practical tools from this product.
-
Students in AI
Students aiming to learn about multi-agent AI systems can engage deeply with the practical applications and coding exercises.
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Beginners in Programming
Complete beginners might struggle with the complexity and technical requirements needed to effectively utilize this AI framework.
БАРААНЫ ТАЙЛБАР
Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based agentic AI framework with tool use, memory, and multi-agent workflows
Асуулга байна уу? Бидэнтэй чатлаарай
Хэрэглэгчийн асуулт, хариулт
-
Асуулт:
What programming language is required for this book?
Хариулт: Python coding skills are required. -
Асуулт:
Who is this book intended for?
Хариулт: It is designed for AI engineers, ML practitioners, software architects, and DevOps engineers. -
Асуулт:
Does the book include any free resources?
Хариулт: Yes, it includes a DRM-free PDF version and access to Packt’s next-gen Reader.
Introduction to Programming Editorial Review
Design Multi-Agent AI Systems Using MCP And A2A is a comprehensive guide for engineering your own Python-based agentic AI framework that incorporates tool use, memory, and multi-agent workflows. Published by Packt Publishing, this book delves into the intricacies of constructing robust multi-agent systems. With a substantial print length of 536 pages, it offers detailed insights and practices for both beginners and seasoned developers. Readers will appreciate the structured approach, allowing easy navigation through topics relevant to multi-agent frameworks. Its clear language ensures that complex concepts are accessible, making it a valuable resource for anyone interested in artificial intelligence.
Customer Reviews & Ratings
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Давуу тал
- In-depth guide on multi-agent systems
- Detailed discussions on agentic frameworks
- Accessible for beginners and experienced developers
- Well-structured approach for easy navigation
- Includes practical examples and insights
Сул талууд
- Publication date is in the future, 2026
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11 September 2026 · via Trustpilot
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MNT 264642
Яг одоо захиалаад ойрын хугацаанд хүлээн аваарай Бямба гараг, 9 дүгээр сар 26
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Онцлог & Давуу талууд
- Build a production-ready multi-agent AI framework from scratch.
- Create Python-based AI agents without external orchestration tools.
- Design adaptive systems using A2A messaging for collaboration.
- Integrate memory and context with the Model Context Protocol (MCP).
- Gain hands-on experience with annotated code and practical deployment patterns.
- Ideal for AI engineers, ML practitioners, and technical leaders.
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