Agents read them before touching the canvas. Combined with use_figma, agents now have both access and context they know how to work in Figma and they know how to work in your Figma.
这一洞见揭示了Figma for Agents的创新解决方案：通过让AI代理在设计前读取设计规范，同时提供对实际Figma系统的访问权限，解决了AI与设计系统整合的关键问题。这种方法代表了AI设计工具的重要进步，从通用生成转向特定品牌环境的理解。
data and analytics agents are essentially useless without the right context – they aren't able to tease apart vague questions, decipher business definitions, and reason across disparate data effectively.
这是一个令人惊讶的洞察，揭示了当前AI数据代理面临的核心瓶颈。文章指出，即使是最先进的数据代理，缺乏适当的上下文也会使其变得毫无用处。这挑战了技术万能论的假设，强调了业务上下文在AI系统中的决定性作用。
Context is basically how many things a machine can keep in its operational memory - it's not so different from the very human cognitive load.
【启发】「上下文窗口 = 认知负荷」——这个类比是整篇文章最有洞察力的一句话。它把一个技术概念（context window）与一个人类体验（认知疲劳）无缝连接。启发在于：所有帮助人类减少认知负荷的代码实践——模块化、清晰命名、单一职责——现在也在帮助 AI 减少 token 消耗。「对人友好的代码 = 对 AI 友好的代码」，这个等式比我们想象的成立得更彻底。
There's an old saying that content is king. With agents, context is.
在 LLM 时代，这是对“上下文窗口”重要性最精辟的注解。Agent 不具备人类的隐性知识和环境感知能力，因此显式的上下文（如 context.json）成为了其行动的基石。这提醒我们，在设计 AI 辅助系统时，构建高质量的上下文生成机制往往比优化模型本身更为关键。
Create multilingual experiences that go beyond translation and understand cultural context.
Gemma 4 E2B/E4B 原生预训练 140+ 语言，且强调「超越翻译、理解文化语境」。对 AI 硬件产品而言这个参数意义重大：一个能在设备端离线处理中文、理解文化背景的 2-4B 模型，意味着本地化 AI 硬件（录音笔、学习机、会议设备）无需依赖国内厂商 API，直接用 Gemma 4 就能构建多语言理解能力。
Now, let’s modify the prompt by adding a few examples of how we expect the output to be. Pythonuser_input = "Send a message to Alison to ask if she can pick me up tonight to go to the concert together" prompt=f"""Turn the following message to a virtual assistant into the correct action: Message: Ask my aunt if she can go to the JDRF Walk with me October 6th Action: can you go to the jdrf walk with me october 6th Message: Ask Eliza what should I bring to the wedding tomorrow Action: what should I bring to the wedding tomorrow Message: Send message to supervisor that I am sick and will not be in today Action: I am sick and will not be in today Message: {user_input}""" response = generate_text(prompt, temp=0) print(response) This time, the style of the response is exactly how we want it. Can you pick me up tonight to go to the concert together?
But we can also get the model to generate responses in a certain format. Let’s look at a couple of them: markdown tables
Hoffman, R., Mueller, S., Klein, G., & Litman, J. (2021). Measuring Trust in the XAI Context. PsyArXiv. https://doi.org/10.31234/osf.io/e3kv9