Building Multi-Agent Systems in Python : : Designing Collaborative AI and LLM Agents with MCP and A2A
Language: English
Published by Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 57.92
Quantity: 2 available
Add to basketItem description from seller
Neuware - This book is your definitive engineering blueprint for designing, testing, and deploying production-grade AI networks. It strips away the hype and provides a rigorous, code-first approach to building robust architectures, enforcing strict data schemas, and maintaining absolute control over non-deterministic systems.I know exactly what happens when you try to scale a single AI script. You feed an LLM a massive prompt to research, analyze, and draft a report. It works perfectly on your laptop. But when deployed to a live environment, the architecture breaks. The model loses focus, hallucinates facts, and crashes under external API rate limits.You find yourself fighting shrinking context windows and unpredictable token costs. I hit that exact same wall. The breakthrough did not come from finding a better language model; it came from changing the architecture entirely.By dividing labor into highly specialized roles-a researcher, an analyst, a writer-and connecting them with secure message brokers, the chaos vanished. The system became deterministic. If you are tired of building fragile AI prototypes and want to engineer truly reliable software, I wrote this specifically for you.What's insideThrough practical, conceptual Python blueprints, you will learn to construct resilient systems from the ground up: - Agent-to-Agent (A2A) Messaging: Build robust publisher-subscriber pipelines using rigid Pydantic schemas.- The Model Context Protocol (MCP): Connect to live databases using standardized v1.x client-server architectures.- Shared Memory States: Implement asynchronous, lock-protected whiteboards utilizing Redis.- Hierarchical Routing: Design strict chains of command to organize specialized workers.- Production Security: Containerize deployments with Docker, manage dynamic API keys via secret vaults, and enforce non-blocking Human-in-the-Loop (HITL) guardrails.This is designed for software architects, backend engineers, and Python developers ready to move beyond basic API wrappers. If you understand foundational asynchronous programming and want to build scalable, automated operations, this is your technical foundation.Stop wrestling with unpredictable AI and start engineering reliable distributed networks. Grab your copy now, implement these blueprints, and deploy your first autonomous system today.…
Seller Inventory # 9798190165562
- Title
- Building Multi-Agent Systems in Python : : Designing Collaborative AI and LLM Agents with MCP and A2A
- Author
- Tommy M Cross
- Publisher
- Amazon Digital Services LLC - Kdp Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798190165562
- Item weight
- 281 grams
- Dimensions
- 244x170x9 mm
This book is your definitive engineering blueprint for designing, testing, and deploying production-grade AI networks. It strips away the hype and provides a rigorous, code-first approach to building robust architectures, enforcing strict data schemas, and maintaining absolute control over non-deterministic systems.
I know exactly what happens when you try to scale a single AI script. You feed an LLM a massive prompt to research, analyze, and draft a report. It works perfectly on your laptop. But when deployed to a live environment, the architecture breaks. The model loses focus, hallucinates facts, and crashes under external API rate limits.
You find yourself fighting shrinking context windows and unpredictable token costs. I hit that exact same wall. The breakthrough did not come from finding a better language model; it came from changing the architecture entirely.
By dividing labor into highly specialized roles—a researcher, an analyst, a writer—and connecting them with secure message brokers, the chaos vanished. The system became deterministic. If you are tired of building fragile AI prototypes and want to engineer truly reliable software, I wrote this specifically for you.
What's inside
Through practical, conceptual Python blueprints, you will learn to construct resilient systems from the ground up:
- Agent-to-Agent (A2A) Messaging: Build robust publisher-subscriber pipelines using rigid Pydantic schemas.
- The Model Context Protocol (MCP): Connect to live databases using standardized v1.x client-server architectures.
- Shared Memory States: Implement asynchronous, lock-protected whiteboards utilizing Redis.
- Hierarchical Routing: Design strict chains of command to organize specialized workers.
- Production Security: Containerize deployments with Docker, manage dynamic API keys via secret vaults, and enforce non-blocking Human-in-the-Loop (HITL) guardrails.
This is designed for software architects, backend engineers, and Python developers ready to move beyond basic API wrappers. If you understand foundational asynchronous programming and want to build scalable, automated operations, this is your technical foundation.
Stop wrestling with unpredictable AI and start engineering reliable distributed networks. Grab your copy now, implement these blueprints, and deploy your first autonomous system today.
"Synopsis" may belong to another edition of this title.
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