Agentic Frameworks in Distributed Computing: LLM Orchestration, Cooperative Task Delegation, and Decentralized Workflow Design for Experienced AI Prac

Name: Agentic Frameworks in Distributed Computing: LLM Orchestration, Cooperative Task Delegation, and Decentralized Workflow Design for Experienced AI Prac
Brand: Independently Published
SKU: 9798196605741
Price: 1701.0 INR
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by Ayla Song

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ISBN13: 9798196605741
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 12, 2026
Pages: 280
Original Price: GBP 14.7
Language: English
Edition: N/A
Item Weight: 490 grams
BISAC Subject(s): Data Science / Neural Networks

About The Book

The convergence of large language models with distributed computing infrastructure has produced a new class of engineering challenge one that demands more than familiarity with either domain in isolation. As organizations deploy increasingly autonomous AI systems across decentralized environments, the practitioners responsible for their architecture must navigate a complex intersection of coordination theory, systems design, and LLM-native engineering principles.
This book is written for that audience. Targeting experienced AI engineers, distributed systems architects, and senior technical practitioners, it presents a structured examination of agentic framework design within the context of real-world distributed computing constraints. The content moves deliberately through the technical layers that define modern multi-agent deployments: from the structural properties of LLM orchestration engines, to cooperative task delegation across heterogeneous agent networks, to the formal design of decentralized workflows that remain coherent under load, latency, and partial failure.
Each chapter addresses a distinct architectural concern with analytical precision. Readers will examine how LLM-powered agents are embedded within distributed runtimes, how cooperative delegation protocols are formalized and enforced, how workflow graphs are constructed to tolerate non-deterministic outputs, and how observability and governance are applied to agentic systems operating at production scale. The treatment is technical throughout grounded in systems thinking, informed by current framework implementations, and oriented toward practitioners who design rather than consume these technologies.
No prior exposure to agentic AI is assumed to be absent rather, prior depth in distributed systems or machine learning engineering is assumed to be present. This book extends that existing foundation into the rapidly evolving domain of autonomous, cooperative AI infrastructure.
For AI practitioners ready to engage with the architectural complexity of distributed agentic systems at a professional level, this book offers the analytical depth and structural clarity that the subject demands. Secure your copy and bring precision to one of the most consequential engineering domains in contemporary AI development.

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