Synopsis
Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) — efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough comes when SLMs are orchestrated together into multi-agent workflows.
This book provides a comprehensive guide to building, scaling, and governing multi-agent systems with SLMs. Written for engineers, researchers, and applied AI practitioners, it explains how to design efficient workflows where multiple specialized agents collaborate, validate each other, and outperform a single large model.
Key areas include:
Why SLMs matter: cost and latency trade-offs vs. LLMs, and where they win in practice.
Agentic patterns: from ReAct and Reflexion to Graph-of-Thoughts, voting councils, and self-consistency.
Architectures: pipelines, hubs, and councils with robust hand-offs, retries, and deadlines.
Infrastructure: serving SLMs efficiently with quantization, batching, and optimized runtimes.
Memory and retrieval: vector databases, summarization strategies, and privacy-aware storage.
Frameworks in action: LangGraph, AutoGen, CrewAI, DSPy, LlamaIndex, smolagents.
Evaluation & observability: benchmarks, CI/CD test gates, dashboards, and regression alerts.
Governance & safety: guardrails, auditability, and policies for production systems.
Case studies & playbooks: real-world workflows in customer support, analytics, CI/CD, and enterprise RAG.
Unlike books that focus only on frameworks or introductions to small models, this work is practical and production-oriented. It bridges the gap between theory and deployment, offering actionable patterns, design strategies, and reliability practices that today’s AI engineers demand.
Whether you are an AI engineer seeking efficiency, a researcher interested in orchestration, or a professional preparing for the shift from monolithic LLMs to collaborative SLM ecosystems, this book gives you the tools to build smarter, faster, and more efficient AI systems.
"synopsis" may belong to another edition of this title.
Search results for Multi-Agent Workflows with SLMs: Smarter, Faster, and...
Stock Image
Multi-Agent Workflows with SLMs: Smarter, Faster, and More Efficient AI Systems
Print on DemandSeller: California Books, Miami, FL, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # I-9798299481280
Stock Image
Multi-Agent Workflows with SLMs (Paperback)
Print on DemandSeller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) - efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough comes when SLMs are orchestrated together into multi-agent workflows.This book provides a comprehensive guide to building, scaling, and governing multi-agent systems with SLMs. Written for engineers, researchers, and applied AI practitioners, it explains how to design efficient workflows where multiple specialized agents collaborate, validate each other, and outperform a single large model.Key areas include: Why SLMs matter: cost and latency trade-offs vs. LLMs, and where they win in practice.Agentic patterns: from ReAct and Reflexion to Graph-of-Thoughts, voting councils, and self-consistency.Architectures: pipelines, hubs, and councils with robust hand-offs, retries, and deadlines.Infrastructure: serving SLMs efficiently with quantization, batching, and optimized runtimes.Memory and retrieval: vector databases, summarization strategies, and privacy-aware storage.Frameworks in action: LangGraph, AutoGen, CrewAI, DSPy, LlamaIndex, smolagents.Evaluation & observability: benchmarks, CI/CD test gates, dashboards, and regression alerts.Governance & safety: guardrails, auditability, and policies for production systems.Case studies & playbooks: real-world workflows in customer support, analytics, CI/CD, and enterprise RAG.Unlike books that focus only on frameworks or introductions to small models, this work is practical and production-oriented. It bridges the gap between theory and deployment, offering actionable patterns, design strategies, and reliability practices that today's AI engineers demand.Whether you are an AI engineer seeking efficiency, a researcher interested in orchestration, or a professional preparing for the shift from monolithic LLMs to collaborative SLM ecosystems, this book gives you the tools to build smarter, faster, and more efficient AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798299481280
Stock Image
Multi-Agent Workflows with SLMs
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9798299481280
Buy New
£ 23.28
£ 4.16 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
Multi-Agent Workflows with SLMs (Paperback)
Print on DemandSeller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) - efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough comes when SLMs are orchestrated together into multi-agent workflows.This book provides a comprehensive guide to building, scaling, and governing multi-agent systems with SLMs. Written for engineers, researchers, and applied AI practitioners, it explains how to design efficient workflows where multiple specialized agents collaborate, validate each other, and outperform a single large model.Key areas include: Why SLMs matter: cost and latency trade-offs vs. LLMs, and where they win in practice.Agentic patterns: from ReAct and Reflexion to Graph-of-Thoughts, voting councils, and self-consistency.Architectures: pipelines, hubs, and councils with robust hand-offs, retries, and deadlines.Infrastructure: serving SLMs efficiently with quantization, batching, and optimized runtimes.Memory and retrieval: vector databases, summarization strategies, and privacy-aware storage.Frameworks in action: LangGraph, AutoGen, CrewAI, DSPy, LlamaIndex, smolagents.Evaluation & observability: benchmarks, CI/CD test gates, dashboards, and regression alerts.Governance & safety: guardrails, auditability, and policies for production systems.Case studies & playbooks: real-world workflows in customer support, analytics, CI/CD, and enterprise RAG.Unlike books that focus only on frameworks or introductions to small models, this work is practical and production-oriented. It bridges the gap between theory and deployment, offering actionable patterns, design strategies, and reliability practices that today's AI engineers demand.Whether you are an AI engineer seeking efficiency, a researcher interested in orchestration, or a professional preparing for the shift from monolithic LLMs to collaborative SLM ecosystems, this book gives you the tools to build smarter, faster, and more efficient AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798299481280
Buy New
£ 26.49
£ 37 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Seller Image
Bernie, B: Multi-Agent Workflows with SLMs
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware - Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) - efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough comes when SLMs are orchestrated together into multi-agent workflows. Seller Inventory # 9798299481280