{"product_id":"artificial-neural-networks-for-developers-react-planning-graphs-and-evaluation-patterns-to-ship-better-ai-agents-9798296375711","title":"Artificial Neural Networks for Developers: ReAct, Planning Graphs, and Evaluation Patterns to Ship Better AI Agents","description":"\u003cp\u003e • Author(s): Clifford C. Sowders\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Neural Networks\u003c\/p\u003e\u003cp\u003e\u003cb\u003eArtificial Neural Networks for Developers: ReAct, Planning Graphs, and Evaluation Patterns to Ship Better AI Agents\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eWhat's the point of a smart model if it fumbles when it meets real-world complexity? Too many developers struggle with LLM prototypes that can answer trivia but collapse under the weight of actual workflows, flaky tools, and unclear goals. If you're building AI agents meant to perform-not just pretend-this book is your practical guide to making them reliable, scalable, and safe. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eArtificial Neural Networks for Developers\u003c\/b\u003e cuts through the hype and equips you with working patterns that bring structure, safety, and strategy to your AI systems. From planning graphs that turn chaos into coherence, to model-based reasoning powered by ReAct and Guard Nodes, you'll learn how to build agents that plan, call tools, and recover from failures-like a real teammate would. \u003cp\u003e\u003c\/p\u003eThis book doesn't stop at \"it works.\" You'll also learn how to prove it works-by wiring up evaluation harnesses, CI jobs, scorecards, and testable outputs across structured plans. Built for developers who think in systems, it delivers everything you need to ship confidently. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat you'll gain: \u003c\/b\u003e\u003cbr\u003eProven planning graph templates for tools, memory, retries, and decision-making \u003cp\u003e\u003c\/p\u003eStep-by-step patterns for implementing ReAct, CoT, tool-use with OpenAI functions, and guarded toolchains \u003cp\u003e\u003c\/p\u003eSecure-by-default design practices mapped to the OWASP LLM Top 10 \u003cp\u003e\u003c\/p\u003eReusable evaluation harnesses for WebArena, AgentBench, and BrowserGym \u003cp\u003e\u003c\/p\u003eLightweight training workflows with PEFT and QLoRA-including LoRA adapter scripts, alignment checklists, and safe deployment tips \u003cp\u003e\u003c\/p\u003eCI\/CD blueprints to continuously test, monitor, and deploy your agent workflows \u003cp\u003e\u003c\/p\u003eYou'll go from hard-coded trial-and-error scripts to structured, testable agents that integrate directly into your backend stack. Whether you're deploying a research assistant, workflow optimizer, or customer support agent, these patterns scale with you-from prototype to production.","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46333986865303,"sku":"9798296375711","price":1709.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798296375711.webp?v=1768671052","url":"https:\/\/atlanticbooks.com\/products\/artificial-neural-networks-for-developers-react-planning-graphs-and-evaluation-patterns-to-ship-better-ai-agents-9798296375711","provider":"Atlantic Books","version":"1.0","type":"link"}