A practical 
 guide to 
 building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents. This guide is designed for product and engineering teams exploring how to build their first agents, distilling and effectively. After reading this guide, you’ll have the foundational knowledge you need to confidently start building your first agent. 3 A practical guide to building agents What is an agent?

efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process high-quality prompts that guide LLMs to produce accurate outputs. This process involves tinkering to find the best prompt, optimizing prompt length, and evaluating a prompt’s writing style and structure in relation System, contextual and role prompting System, contextual and role prompting are all techniques used to guide how LLMs generate text, but they focus on different aspects: • System prompting sets the overall

Domain expertise Fine-tuned models better understand your industry’s terminology, style, and context. Consistent tone and style For a retailer, that could mean every product description stays true to brand other agents to get things done. We’ll continue to report back from the front lines of AI to help guide your own thinking. Product Note: Operator Operator is an example of OpenAI’s agentic approach

its findings, and create insightful multi-page, reports that you can turn into engaging podcast-style conversations… …It’s a step towards more agentic AI that can move beyond simple question- answering Performance = +225x Over Eight Years 106 1 GPT-MoE Inference Workload = A type of workload where a GPT-style model with a Mixture-of-Experts (MoE) architecture is used for inference (i.e., making predictions) than move slowly and miss the moment. We’ll be rolling out a few constructive constraints to help guide this shift…: • …AI use will be part of what we look for in hiring • AI use will be part of what

interconnects.ai/p/deepseek-r1-recipe-for-o1 https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of- experts 教程作者：郭震，工作 8 年目前美国 AI 博士在读，公众号：郭震 AI，欢迎关注获取更多原创教程。资 料用心打磨且开源，是为了帮助更多人了解获取

methods not delivering generalized performance 2 Why TVM? XTVM for Speech Synthesis - WaveRNN-style model architecture - Autoregressive sampling net running at faster than real-time - Compute split

“Context（上 下文）” 相关的 背景信息，比如 你自己或是你希 望它完成的任务 的信息。 "O"代表 “Objective （目标）” 明 确的指示告诉 AI你希望它做什 么。 "S"代表“Style （风格）” 想 要的写作风格， 如严肃的、有趣 的、创新性表达、 学术性…… "T"代表“Tone （语调）” 幽 默的？情绪化？ 有威胁性？ "A"代表 "Audience"， 受众是谁。