Mira S Devlin (24 results)

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Paperback. Condition: new. Paperback. Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectIn Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generative AI into the world of agentic intelligence-where LLMs evolve from reactive tools into dynamic collaborators capable of grounding responses in truth, understanding context, and improving over time.This book doesn't just explain concepts-it helps you build them. Each chapter blends theory, diagrams, and applied examples to show how retrieval, reasoning, and reflection interact inside modern AI agents. Whether you're constructing a self-updating research assistant or a multi-agent workflow, you'll gain a deep understanding of how today's most advanced cognitive systems are designed.What You'll LearnThe Cognitive Core of AI AgentsUnderstand the architecture of transformers, tokenization, and attention.Explore the shift from static LLMs to adaptive, outcome-driven agents.Learn how retrieval, reflection, and reasoning form the four pillars of intelligence.Retrieval-Augmented Generation (RAG)Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Build a working RAG-powered knowledge bot capable of live data integration.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Represent relationships between data entities for context-rich reasoning.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsImplement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationArchitect intelligent teams of agents that can plan, delegate, and verify results.Understand communication protocols, cooperative memory, and role specialization.Use frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Each chapter concludes with an "Agent in Action" section-hands-on projects and guided workflows that turn abstract concepts into working systems you can build, extend, and deploy.Key Features: End-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.Framework-agnostic examples: Concepts applicable to GPT, Claude, Gemini, and open-source models.Practical code labs: Step-by-step walkthroughs in Python with modular components.Visual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout.Debugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples.Scalable design patterns: Extend single-agent models into multi-agent collaborative systems.About the Author: Mira S. Devlin is an AI systems architect specializing in the intersection of language models, retrieval pipelines, and knowledge reasoning frameworks.Who This Book Is For: AI developers, data scientists, and engineers who want to move beyond simple LLM prompts.Architects and product innovators building intelligent, explainable, and adaptive AI systems.Researchers and students seeking a structured understanding of retrieval-based reasoning and reflection.< Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New.

Paperback. Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: new. Paperback. Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectIn Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generative AI into the world of agentic intelligence-where LLMs evolve from reactive tools into dynamic collaborators capable of grounding responses in truth, understanding context, and improving over time.This book doesn't just explain concepts-it helps you build them. Each chapter blends theory, diagrams, and applied examples to show how retrieval, reasoning, and reflection interact inside modern AI agents. Whether you're constructing a self-updating research assistant or a multi-agent workflow, you'll gain a deep understanding of how today's most advanced cognitive systems are designed.What You'll LearnThe Cognitive Core of AI AgentsUnderstand the architecture of transformers, tokenization, and attention.Explore the shift from static LLMs to adaptive, outcome-driven agents.Learn how retrieval, reflection, and reasoning form the four pillars of intelligence.Retrieval-Augmented Generation (RAG)Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Build a working RAG-powered knowledge bot capable of live data integration.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Represent relationships between data entities for context-rich reasoning.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsImplement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationArchitect intelligent teams of agents that can plan, delegate, and verify results.Understand communication protocols, cooperative memory, and role specialization.Use frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Each chapter concludes with an "Agent in Action" section-hands-on projects and guided workflows that turn abstract concepts into working systems you can build, extend, and deploy.Key Features: End-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.Framework-agnostic examples: Concepts applicable to GPT, Claude, Gemini, and open-source models.Practical code labs: Step-by-step walkthroughs in Python with modular components.Visual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout.Debugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples.Scalable design patterns: Extend single-agent models into multi-agent collaborative systems.About the Author: Mira S. Devlin is an AI systems architect specializing in the intersection of language models, retrieval pipelines, and knowledge reasoning frameworks.Who This Book Is For: AI developers, data scientists, and engineers who want to move beyond simple LLM prompts.Architects and product innovators building intelligent, explainable, and adaptive AI systems.Researchers and students seeking a structured understanding of retrieval-based reasoning and Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Build AI systems that don't just respond-they collaborate, adapt, and evolve.Large Language Models changed the world.Multi-agent LLM ecosystems will redefine it.In Scaling LLM Agents, Mira S. Devlin takes you inside the next revolution of AI architecture-where networks of specialized LLM agents coordinate, reason, reflect, and self-optimize to accomplish what a single model could never do alone.This is not another 'prompting' book.This is a systems-engineering blueprint for building scalable, resilient, and tool-driven AI ecosystems that behave more like distributed organizations than standalone chatbots.Drawing from modern AI orchestration frameworks, emerging research on distributed cognition, and real-world production patterns, this book shows you how to design agent clusters, shared memory fabrics, routing layers, evaluators, and tool hubs that dynamically adapt and continuously improve.What You'll Learn- How to structure LLM agents into clusters with clear roles, capabilities, and communication protocols- Coordinator and scheduler patterns for robust multi-agent task execution- Decentralized routing fabrics for fast, scalable message passing- Shared vector memory systems for persistent state, grounding, and context fusion- Reflection and optimization loops that help agents correct themselves and learn from outcomes- Tool-driven orchestration using APIs, function calling, and external workflows- Monitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparent- Scalable system topologies for production workloads, real-time reasoning, and enterprise automationWho This Book Is For- AI engineers designing intelligent, high-performance systems- Software architects modernizing applications with agentic patterns- Founders and CTOs building AI-first products- Researchers exploring distributed cognition and emergent behaviors- Developers wanting to go beyond prompts and learn real LLM engineeringIf you've mastered prompting, tinkered with agents, or built early prototypes-this is the book that takes you into the next era: true multi-agent AI ecosystems that scale.Why This Book MattersThe future will not belong to the biggest model.but to the best-organized constellation of collaborating models.This book gives you the architecture, patterns, and practical frameworks to build them.Order your copy today and start building the AI ecosystems of tomorrow.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Amazon Book DescriptionAI can write code faster. But can your engineering process keep up Software development is entering a fundamental transition. AI coding assistants and autonomous development tools can generate code, tests, refactoring plans, documentation, and multi-file implementations at a speed that changes the economics of software production.But faster code generation does not automatically produce better software.Software Engineering in the AI Era examines what happens when implementation becomes dramatically easier-and why code review, testing, architecture, productivity measurement, and developer workflows must evolve with it.Mira S. Devlin presents a practical framework for engineering teams navigating AI-assisted development. Rather than treating AI as either a replacement for developers or simply a faster autocomplete tool, this book examines AI as a change to the entire software engineering system.You will explore how to: - Rethink code review when AI can generate substantial implementations- Build testing strategies that provide evidence rather than simply more test code- Preserve architectural boundaries as implementation becomes cheaper- Identify technical debt and emerging comprehension debt- Evaluate AI-assisted productivity without confusing output with engineering value- Design developer workflows around appropriate human checkpoints- Improve onboarding and knowledge transfer in AI-assisted teams- Understand the risks of generated assumptions, hidden dependencies, and plausible but incorrect implementations- Adapt engineering practices without abandoning proven software engineering fundamentals- Build workflows that balance development speed with correctness, maintainability, and accountabilityThe central idea is simple: When code becomes cheaper to produce, engineering judgment becomes more valuable.AI can generate an implementation. It cannot remove the engineering responsibility to determine whether that implementation is correct, appropriate, understandable, and safe to operate.This book is for software engineers, senior developers, architects, engineering managers, technical leaders, and teams adopting AI-assisted development who want to increase their leverage without allowing development speed to outrun engineering understanding.The future of software engineering is not simply about writing more code. It is about building better systems with greater leverage-and knowing what still requires human judgment. …

Paperback. Condition: New.

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Paperback. Condition: new. Paperback. Build AI systems that don't just respond-they collaborate, adapt, and evolve.Large Language Models changed the world.Multi-agent LLM ecosystems will redefine it.In Scaling LLM Agents, Mira S. Devlin takes you inside the next revolution of AI architecture-where networks of specialized LLM agents coordinate, reason, reflect, and self-optimize to accomplish what a single model could never do alone.This is not another "prompting" book.This is a systems-engineering blueprint for building scalable, resilient, and tool-driven AI ecosystems that behave more like distributed organizations than standalone chatbots.Drawing from modern AI orchestration frameworks, emerging research on distributed cognition, and real-world production patterns, this book shows you how to design agent clusters, shared memory fabrics, routing layers, evaluators, and tool hubs that dynamically adapt and continuously improve.What You'll LearnHow to structure LLM agents into clusters with clear roles, capabilities, and communication protocolsCoordinator and scheduler patterns for robust multi-agent task executionDecentralized routing fabrics for fast, scalable message passingShared vector memory systems for persistent state, grounding, and context fusionReflection and optimization loops that help agents correct themselves and learn from outcomesTool-driven orchestration using APIs, function calling, and external workflowsMonitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparentScalable system topologies for production workloads, real-time reasoning, and enterprise automationWho This Book Is ForAI engineers designing intelligent, high-performance systemsSoftware architects modernizing applications with agentic patternsFounders and CTOs building AI-first productsResearchers exploring distributed cognition and emergent behaviorsDevelopers wanting to go beyond prompts and learn real LLM engineeringIf you've mastered prompting, tinkered with agents, or built early prototypes-this is the book that takes you into the next era: true multi-agent AI ecosystems that scale.Why This Book MattersThe future will not belong to the biggest model.but to the best-organized constellation of collaborating models.This book gives you the architecture, patterns, and practical frameworks to build them.Order your copy today and start building the AI ecosystems of tomorrow. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Build AI systems that don't just respond-they collaborate, adapt, and evolve.Large Language Models changed the world.Multi-agent LLM ecosystems will redefine it.In Scaling LLM Agents, Mira S. Devlin takes you inside the next revolution of AI architecture-where networks of specialized LLM agents coordinate, reason, reflect, and self-optimize to accomplish what a single model could never do alone.This is not another "prompting" book.This is a systems-engineering blueprint for building scalable, resilient, and tool-driven AI ecosystems that behave more like distributed organizations than standalone chatbots.Drawing from modern AI orchestration frameworks, emerging research on distributed cognition, and real-world production patterns, this book shows you how to design agent clusters, shared memory fabrics, routing layers, evaluators, and tool hubs that dynamically adapt and continuously improve.What You'll LearnHow to structure LLM agents into clusters with clear roles, capabilities, and communication protocolsCoordinator and scheduler patterns for robust multi-agent task executionDecentralized routing fabrics for fast, scalable message passingShared vector memory systems for persistent state, grounding, and context fusionReflection and optimization loops that help agents correct themselves and learn from outcomesTool-driven orchestration using APIs, function calling, and external workflowsMonitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparentScalable system topologies for production workloads, real-time reasoning, and enterprise automationWho This Book Is ForAI engineers designing intelligent, high-performance systemsSoftware architects modernizing applications with agentic patternsFounders and CTOs building AI-first productsResearchers exploring distributed cognition and emergent behaviorsDevelopers wanting to go beyond prompts and learn real LLM engineeringIf you've mastered prompting, tinkered with agents, or built early prototypes-this is the book that takes you into the next era: true multi-agent AI ecosystems that scale.Why This Book MattersThe future will not belong to the biggest model.but to the best-organized constellation of collaborating models.This book gives you the architecture, patterns, and practical frameworks to build them.Order your copy today and start building the AI ecosystems of tomorrow. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…