Artificial Neural Networks for Developers: ReAct, Planning Graphs, and Evaluation Patterns to Ship Better AI Agents What'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. Artificial Neural Networks ...
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Artificial Neural Networks for Developers: ReAct, Planning Graphs, and Evaluation Patterns to Ship Better AI Agents What'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. Artificial Neural Networks for Developers 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. This 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. What you'll gain: Proven planning graph templates for tools, memory, retries, and decision-making Step-by-step patterns for implementing ReAct, CoT, tool-use with OpenAI functions, and guarded toolchains Secure-by-default design practices mapped to the OWASP LLM Top 10 Reusable evaluation harnesses for WebArena, AgentBench, and BrowserGym Lightweight training workflows with PEFT and QLoRA-including LoRA adapter scripts, alignment checklists, and safe deployment tips CI/CD blueprints to continuously test, monitor, and deploy your agent workflows You'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.
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