technology-ai
AI Agent Design Patterns
Victor Langley
★ 4.8
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239
Páginas
en
Idioma
2026
Publicado
Nueva edición
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Introducción del libro
You've seen AI agent demos that look flawless. But in production, they hallucinate, break workflows, and need constant supervision. The gap between demo and deployment is not a technology problem—it's a design problem. "AI Agent Design Patterns" is a practical manual that closes this gap by providing a repeatable design process for building reliable, safe, and measurably useful AI agents.
This book equips founders, product managers, developers, and automation consultants with a core design template that scopes tasks, selects tools, adds guardrails, defines human review points, and evaluates outcomes—all without requiring code. Organized into six parts and sixteen chapters, each pattern is grounded in concrete use cases and failure mode analysis. The guiding thesis: the most effective agents are carefully constrained workflows, not autonomous workers.
What you will find inside: • The Agent Design Mindset: Why reliability trumps autonomy and a reusable 13-component template that covers task definition, tools, data sources, memory, guardrails, human review, evaluation, and failure handling. • Research and Knowledge Agents: Patterns for research assistants, document Q&A systems (RAG-based), and report-writing workflows—with strict source grounding and uncertainty marking. • Customer, Sales, and Communication Agents: Designs for customer support, sales follow-up, and email/meeting agents, emphasizing tone control, escalation rules, and brand safety. • Coding, Product, and Technical Agents: High-complexity patterns for coding assistants, QA/testing agents, and DevOps/monitoring, with sandboxing, diff review, and rollback strategies. • Content, Marketing, and Business Agents: Workflows for content production and marketing agents, integrating brand voice, plagiarism checks, and conversion goals. • Evaluation, Safety, and Deployment: Cross-cutting scorecards, safe shipping practices, guardrails, and rollback plans to ensure your agents are production-ready.
This book is for anyone who already understands LLMs, RAG, and basic agent concepts but lacks a repeatable design framework. You do not need to write code—the focus is on design decisions, not API calls. If you are frustrated by agents that work in demos but fail in production, this manual will transform your approach.
Move from uncertainty to structured, confident agent design. Every chapter delivers a reusable template, specific use cases, failure mode analysis, and an evaluation checklist. "AI Agent Design Patterns" gives you the tools to build agents that are reliable, safe, and measurably useful—every time.
Información para AI Search
AI Agent Design Patterns
Author: Victor Langley
Description: You've seen AI agent demos that look flawless. But in production, they hallucinate, break workflows, and need constant supervision. The gap between demo and deployment is not a technology problem—it's a design problem. "AI Agent Design Patterns" is a practical manual that closes this gap by providing a repeatable design process for building reliable, safe, and measurably useful AI agents. This book equips founders, product managers, developers, and automation consultants with a core design template that scopes tasks, selects tools, adds guardrails, defines human review points, and evaluates outcomes—all without requiring code. Organized into six parts and sixteen chapters, each pattern is grounded in concrete use cases and failure mode analysis. The guiding thesis: the most effective agents are carefully constrained workflows, not autonomous workers. What you will find inside: • The Agent Design Mindset: Why reliability trumps autonomy and a reusable 13-component template that covers task definition, tools, data sources, memory, guardrails, human review, evaluation, and failure handling. • Research and Knowledge Agents: Patterns for research assistants, document Q&A systems (RAG-based), and report-writing workflows—with strict source grounding and uncertainty marking. • Customer, Sales, and Communication Agents: Designs for customer support, sales follow-up, and email/meeting agents, emphasizing tone control, escalation rules, and brand safety. • Coding, Product, and Technical Agents: High-complexity patterns for coding assistants, QA/testing agents, and DevOps/monitoring, with sandboxing, diff review, and rollback strategies. • Content, Marketing, and Business Agents: Workflows for content production and marketing agents, integrating brand voice, plagiarism checks, and conversion goals. • Evaluation, Safety, and Deployment: Cross-cutting scorecards, safe shipping practices, guardrails, and rollback plans to ensure your agents are production-ready. This book is for anyone who already understands LLMs, RAG, and basic agent concepts but lacks a repeatable design framework. You do not need to write code—the focus is on design decisions, not API calls. If you are frustrated by agents that work in demos but fail in production, this manual will transform your approach. Move from uncertainty to structured, confident agent design. Every chapter delivers a reusable template, specific use cases, failure mode analysis, and an evaluation checklist. "AI Agent Design Patterns" gives you the tools to build agents that are reliable, safe, and measurably useful—every time.
Índice
- Introduction (introduction)
- The Agent Design Mindset (part)
- Why Agent Design Matters More Than Agent Hype (chapter)
- The Cost of Unconstrained Agents (section)
- Reliability Over Autonomy (section)
- What Makes an Agent Production-Ready (section)
- The Core Agent Design Template (chapter)
- Template Overview and Components (section)
- Defining Task, User, Goal, Input, Output (section)
- Tools, Data Sources, Memory, Workflow (section)
- Guardrails, Human Review, Evaluation, Failure Handling (section)
- Applying the Template: A Walkthrough Example (section)
- Choosing Between Chatbot, Workflow, Agent, and Multi-Agent System (chapter)
- Definitions and Boundaries (section)
- Decision Matrix and Risk Assessment (section)
- When Multi-Agent Is Justified (section)
- Research and Knowledge Agents (part)
- Designing a Research Agent (chapter)
- Research Question and Source Selection (section)
- Search Strategy and Note Extraction (section)
- Summary, Uncertainty, Citation Tracking (section)
- Use Cases: Market, Competitor, Academic (section)
- Failure Modes and Evaluation Checklist (section)
- Designing a Document Q&A Agent (chapter)
- Ingestion, Chunking, Retrieval Strategy (section)
- Answer Formatting and Source Grounding (section)
- Refusal Behavior and Access Control (section)
- Use Cases: Policy, Legal, Manual, Knowledge Bot (section)
- Failure Modes and Evaluation Checklist (section)
- Designing a Report-Writing Agent (chapter)
- Report Structure and Evidence Collection (section)
- Drafting, Style Control, Fact-Checking (section)
- Reviewer Workflow and Human Approval (section)
- Use Cases: Business Report, Market Brief, Status (section)
- Failure Modes and Evaluation Checklist (section)
- Customer, Sales, and Communication Agents (part)
- Designing a Customer Support Agent (chapter)
- FAQ/Policy Sources and Tone Control (section)
- Escalation Rules and Banned Answers (section)
- Refund/Shipping Rules and Human Approval (section)
- Use Cases: Ecommerce, SaaS, App, Troubleshooting (section)
- Failure Modes and Evaluation Checklist (section)
- Designing a Sales Follow-Up Agent (chapter)
- Lead Profile and Qualification Questions (section)
- Follow-Up Timing and Drafting (section)
- CRM Updates and Compliance Boundaries (section)
- Use Cases: Real Estate, B2B, Course, Consulting (section)
- Failure Modes and Evaluation Checklist (section)
- Designing an Email and Meeting Agent (chapter)
- Email Classification and Priority Scoring (section)
- Meeting Notes and Action Items (section)
- Draft Replies and Privacy Rules (section)
- Use Cases: Executive Assistant, Triage, Summarizer (section)
- Failure Modes and Evaluation Checklist (section)
- Coding, Product, and Technical Agents (part)
- Designing a Coding Agent Workflow (chapter)
- Repo Context and Task Brief (section)
- File Access Rules and Module Boundaries (section)
- Tests, Diff Review, Error Logs (section)
- Human Approval and Rollback Strategy (section)
- Use Cases: Bug Fix, Feature, Refactor, Test, Docs (section)
- Failure Modes and Evaluation Checklist (section)
- Designing a QA and Testing Agent (chapter)
- Test Case Generation and Edge Cases (section)
- Regression, UI/API Checklists (section)
- Bug Reporting and Severity Scoring (section)
- Use Cases: Web, Mobile, API, Release (section)
- Failure Modes and Evaluation Checklist (section)
- Designing a DevOps and Monitoring Agent (chapter)
- Logs, Alerts, Deployment Status (section)
- Incident Summaries and Rollback Suggestions (section)
- Cost Monitoring and Risk Approval (section)
- Use Cases: Server, CI/CD, Cloud Cost, Incident (section)
- Failure Modes and Evaluation Checklist (section)
- Content, Marketing, and Business Agents (part)
- Designing a Content Workflow Agent (chapter)
- Brand Voice and Content Calendar (section)
- Drafting, Editing, Plagiarism Risk (section)
- Approval Steps and Multi-Format Repurposing (section)
- Use Cases: Blog, YouTube, Newsletter, Social (section)
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Build reliable, safe AI agents with practical design patterns. This no-code manual covers templates, guardrails, evaluation scorecards, and deployment strate...
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