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Paperback. Condición: 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.

Paperback. Condición: New.

Paperback. Condición: New.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Building LLM Agents with RAG, Knowledge Graphs & ReflectionA Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI AgentsBy Mira S. DevlinTransform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectLarge language models can generate text-but intelligence requires more than words.True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce.In 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)Master the techniques that make models factually grounded and transparent.Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsBuild agents that evaluate their own outputs and correct themselves.Implement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationUse frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Key FeaturesEnd-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.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. This book is written 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.Tech leaders and educators integrating agentic AI into enterprise or academic environments.You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or cloud environment Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: 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.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Building LLM Agents with RAG, Knowledge Graphs & ReflectionA Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI AgentsBy Mira S. DevlinTransform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectLarge language models can generate text-but intelligence requires more than words.True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce.In 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)Master the techniques that make models factually grounded and transparent.Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsBuild agents that evaluate their own outputs and correct themselves.Implement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationUse frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Key FeaturesEnd-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.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. This book is written 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.Tech leaders and educators integrating agentic AI into enterprise or academic environments.You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or clou Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. Building LLM Agents with RAG, Knowledge Graphs & ReflectionA Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI AgentsBy Mira S. DevlinTransform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectLarge language models can generate text-but intelligence requires more than words.True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce.In 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)Master the techniques that make models factually grounded and transparent.Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsBuild agents that evaluate their own outputs and correct themselves.Implement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationUse frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Key FeaturesEnd-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.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. This book is written 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.Tech leaders and educators integrating agentic AI into enterprise or academic environments.You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or clou Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condición: New.

Paperback. Condición: New.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Most software engineers are taught how to work hard.Very few are taught how to think differently as responsibility grows.At some point in an engineering career, effort alone stops explaining outcomes. The problems become less clear. The feedback becomes vague. Expectations shift quietly-from delivering tasks to shaping outcomes.Thinking Like a Staff Software Engineer is written for that moment.This book is not about tools, frameworks, or promotion checklists. It is about the judgment, leverage, and decision-making that experienced engineers are expected to develop-but rarely receive explicit guidance on.Drawing from real patterns seen across teams and organizations, this book explores why capable engineers stall, why traditional career advice breaks down, and how senior contributors learn to operate effectively when clarity is incomplete and authority is limited.You'll learn how experienced engineers: Interpret problems instead of simply executing tasksMove forward responsibly when requirements are unclearBalance speed with judgment and timingMake impact visible without self-promotionInfluence decisions without formal authorityChoose work that changes outcomes, not just fills timeBuild trust through consistency, follow-through, and predictabilityDesign careers that compound over years, not review cyclesThe chapters focus on common anti-patterns-such as over-reliance on busyness, heroics, waiting for permission, or avoiding conflict-and show how effective engineers correct them through better framing, communication, and system-level thinking.This book is especially relevant for engineers navigating the transition from mid-level to senior, senior to staff, or anyone operating in ambiguous, cross-team, high-leverage roles. Titles vary across companies, but the underlying expectations are remarkably consistent.If you are early in your career, this book may feel aspirational.If you are already senior, it will likely give language to experiences you recognize but haven't fully articulated.Thinking Like a Staff Software Engineer is a practical guide to growing impact without abandoning technical depth-and to becoming the kind of engineer others rely on when the work truly matters.Who this book is forSenior software engineers seeking greater impactEngineers operating without formal authorityIndividual contributors who want to remain technicalProfessionals focused on long-term career growthWho this book is not forBeginners learning programming fundamentalsReaders looking for step-by-step promotion checklistsManagement or people-leadership handbooksIf you're ready to move beyond execution and start shaping outcomes, order your copy today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: 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.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Artificial intelligence is entering a new era-one defined not by static models that generate text, but by intelligent agents that retrieve knowledge, reason with context, use tools, collaborate with other agents, and learn from experience. These systems represent a fundamental shift from passive language models to autonomous, adaptive, and scalable cognitive ecosystems.Scaling LLM Agents: Distributed Cognition & Multi-Agent Ecosystems is a practical and forward-looking guide to building the next generation of AI systems. Written for engineers, researchers, and technical leaders, this book shows you how to transform large language models into coordinated networks of intelligent agents capable of solving complex, multi-step tasks in the real world.Through deep explanations, architectural diagrams, and hands-on mini-projects, you'll learn how to design agents that plan, coordinate, communicate, and continuously improve. You'll explore the mechanics of reinforcement learning, multi-agent orchestration, multimodal cognition, scalable deployment, safety systems, and ethical governance-with a focus on actionable engineering patterns rather than abstract theory.Inside, you'll discover how to: Build reward-driven, self-improving agents using RL and RLHFOrchestrate teams of agents with LangChain, LangGraph, and CrewAIIntegrate agents with search, spreadsheets, APIs, and automation toolsDesign multimodal agents that understand vision, speech, and textDeploy agents to production with Docker, Kubernetes, and cloud platformsMonitor performance with logging, observability, and tracing systemsImplement security, privacy, and ethical guardrails for autonomyArchitect cognitive systems that learn, adapt, and persist over timeEach chapter concludes with an "Agent in Action" mini-project, giving you a repeatable blueprint to build production-ready systems-including a fully deployed RAG-powered agent, a multimodal explainer, and a configurable research analyst capable of retrieving, summarizing, and citing real-world data.More than a technical manual, this book examines the larger transformation happening in AI. You'll explore emerging frontiers such as symbolic-neural hybrids, lifelong memory systems, cognitive architectures, and the path toward general-purpose autonomous intelligence-alongside the ethical questions and design responsibilities that accompany progress.Whether you are building a startup product, deploying enterprise agents, or exploring cutting-edge research, this book gives you the tools, clarity, and mental models to design scalable, intelligent, and trustworthy AI ecosystems.If you're ready to move beyond simple prompts and explore what happens when AI becomes collaborative, embodied, and adaptive, this book is your roadmap. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Most software engineers are taught how to work hard.Very few are taught how to think differently as responsibility grows.At some point in an engineering career, effort alone stops explaining outcomes. The problems become less clear. The feedback becomes vague. Expectations shift quietly-from delivering tasks to shaping outcomes.Thinking Like a Staff Software Engineer is written for that moment.This book is not about tools, frameworks, or promotion checklists. It is about the judgment, leverage, and decision-making that experienced engineers are expected to develop-but rarely receive explicit guidance on.Drawing from real patterns seen across teams and organizations, this book explores why capable engineers stall, why traditional career advice breaks down, and how senior contributors learn to operate effectively when clarity is incomplete and authority is limited.You'll learn how experienced engineers: Interpret problems instead of simply executing tasksMove forward responsibly when requirements are unclearBalance speed with judgment and timingMake impact visible without self-promotionInfluence decisions without formal authorityChoose work that changes outcomes, not just fills timeBuild trust through consistency, follow-through, and predictabilityDesign careers that compound over years, not review cyclesThe chapters focus on common anti-patterns-such as over-reliance on busyness, heroics, waiting for permission, or avoiding conflict-and show how effective engineers correct them through better framing, communication, and system-level thinking.This book is especially relevant for engineers navigating the transition from mid-level to senior, senior to staff, or anyone operating in ambiguous, cross-team, high-leverage roles. Titles vary across companies, but the underlying expectations are remarkably consistent.If you are early in your career, this book may feel aspirational.If you are already senior, it will likely give language to experiences you recognize but haven't fully articulated.Thinking Like a Staff Software Engineer is a practical guide to growing impact without abandoning technical depth-and to becoming the kind of engineer others rely on when the work truly matters.Who this book is forSenior software engineers seeking greater impactEngineers operating without formal authorityIndividual contributors who want to remain technicalProfessionals focused on long-term career growthWho this book is not forBeginners learning programming fundamentalsReaders looking for step-by-step promotion checklistsManagement or people-leadership handbooksIf you're ready to move beyond execution and start shaping outcomes, order your copy today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. Most software engineers are taught how to work hard.Very few are taught how to think differently as responsibility grows.At some point in an engineering career, effort alone stops explaining outcomes. The problems become less clear. The feedback becomes vague. Expectations shift quietly-from delivering tasks to shaping outcomes.Thinking Like a Staff Software Engineer is written for that moment.This book is not about tools, frameworks, or promotion checklists. It is about the judgment, leverage, and decision-making that experienced engineers are expected to develop-but rarely receive explicit guidance on.Drawing from real patterns seen across teams and organizations, this book explores why capable engineers stall, why traditional career advice breaks down, and how senior contributors learn to operate effectively when clarity is incomplete and authority is limited.You'll learn how experienced engineers: Interpret problems instead of simply executing tasksMove forward responsibly when requirements are unclearBalance speed with judgment and timingMake impact visible without self-promotionInfluence decisions without formal authorityChoose work that changes outcomes, not just fills timeBuild trust through consistency, follow-through, and predictabilityDesign careers that compound over years, not review cyclesThe chapters focus on common anti-patterns-such as over-reliance on busyness, heroics, waiting for permission, or avoiding conflict-and show how effective engineers correct them through better framing, communication, and system-level thinking.This book is especially relevant for engineers navigating the transition from mid-level to senior, senior to staff, or anyone operating in ambiguous, cross-team, high-leverage roles. Titles vary across companies, but the underlying expectations are remarkably consistent.If you are early in your career, this book may feel aspirational.If you are already senior, it will likely give language to experiences you recognize but haven't fully articulated.Thinking Like a Staff Software Engineer is a practical guide to growing impact without abandoning technical depth-and to becoming the kind of engineer others rely on when the work truly matters.Who this book is forSenior software engineers seeking greater impactEngineers operating without formal authorityIndividual contributors who want to remain technicalProfessionals focused on long-term career growthWho this book is not forBeginners learning programming fundamentalsReaders looking for step-by-step promotion checklistsManagement or people-leadership handbooksIf you're ready to move beyond execution and start shaping outcomes, order your copy today. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condición: 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.

Paperback. Condición: new. Paperback. Artificial intelligence is entering a new era-one defined not by static models that generate text, but by intelligent agents that retrieve knowledge, reason with context, use tools, collaborate with other agents, and learn from experience. These systems represent a fundamental shift from passive language models to autonomous, adaptive, and scalable cognitive ecosystems.Scaling LLM Agents: Distributed Cognition & Multi-Agent Ecosystems is a practical and forward-looking guide to building the next generation of AI systems. Written for engineers, researchers, and technical leaders, this book shows you how to transform large language models into coordinated networks of intelligent agents capable of solving complex, multi-step tasks in the real world.Through deep explanations, architectural diagrams, and hands-on mini-projects, you'll learn how to design agents that plan, coordinate, communicate, and continuously improve. You'll explore the mechanics of reinforcement learning, multi-agent orchestration, multimodal cognition, scalable deployment, safety systems, and ethical governance-with a focus on actionable engineering patterns rather than abstract theory.Inside, you'll discover how to: Build reward-driven, self-improving agents using RL and RLHFOrchestrate teams of agents with LangChain, LangGraph, and CrewAIIntegrate agents with search, spreadsheets, APIs, and automation toolsDesign multimodal agents that understand vision, speech, and textDeploy agents to production with Docker, Kubernetes, and cloud platformsMonitor performance with logging, observability, and tracing systemsImplement security, privacy, and ethical guardrails for autonomyArchitect cognitive systems that learn, adapt, and persist over timeEach chapter concludes with an "Agent in Action" mini-project, giving you a repeatable blueprint to build production-ready systems-including a fully deployed RAG-powered agent, a multimodal explainer, and a configurable research analyst capable of retrieving, summarizing, and citing real-world data.More than a technical manual, this book examines the larger transformation happening in AI. You'll explore emerging frontiers such as symbolic-neural hybrids, lifelong memory systems, cognitive architectures, and the path toward general-purpose autonomous intelligence-alongside the ethical questions and design responsibilities that accompany progress.Whether you are building a startup product, deploying enterprise agents, or exploring cutting-edge research, this book gives you the tools, clarity, and mental models to design scalable, intelligent, and trustworthy AI ecosystems.If you're ready to move beyond simple prompts and explore what happens when AI becomes collaborative, embodied, and adaptive, this book is your roadmap. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.