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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
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Preces #:
242970796
€ 68
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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.
Produkta informācija
| 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?
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AI Developers
Developers looking to create complex multi-agent systems in Python will find this framework extremely beneficial and efficient.
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Research Scientists
Researchers studying distributed systems or multi-agent collaboration will gain significant insights and practical tools from this product.
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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.
PRODUKTA APRAKSTS
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
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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.
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Pros
- 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
Mīnusi
- Publication date is in the future, 2026
Product Price History
Svarīga informācija
- Ierobežojumi: attiecībā uz produktiem, kas tiek nosūtīti starptautiski, lūdzu, ņemiet vērā, ka jebkura ražotāja garantija var nebūt derīga; ražotāja servisa iespējas var nebūt pieejamas; produktu rokasgrāmatas, instrukcijas un drošības brīdinājumi var nebūt galamērķa valsts valodās; produkti (un pavadošie materiāli) var nebūt izstrādāti saskaņā ar galamērķa valsts standartiem, specifikācijām un marķēšanas prasībām; un produkti var neatbilst galamērķa valsts spriegumam un citiem elektriskajiem standartiem (ja nepieciešams, nepieciešams adapteris vai pārveidotājs). Par to, lai produktu varētu likumīgi ievest galamērķa valstī, ir atbildīgs saņēmējs. Pasūtot no Ubuy vai tā saistītajiem uzņēmumiem, saņēmējs ir ierakstu importētājs, un viņam jāievēro visi galamērķa valsts tiesību akti un noteikumi.
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€ 68
Pasūtiet tūlīt un saņemiet to Ceturtdiena, Septembris 24
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Funkcijas un priekšrocības
- 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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