Ask HN：AI 智能体演示效果很棒，但大家都在怎么用它？
1 分•作者: deliass•4 天前
我在一家大型消费品公司负责数字和品牌业务，我们最近在一个产品发布中使用基于 Agent 的自动化技术。这并非用于实验或原型，而是用于面向消费者的实际产品。
我们使用 Agent 协助完成以下工作：
* 内容生成和本地化
* 设计、法务和市场营销之间的资产路由
* 跨渠道的 SKU 变体处理
* 发布后，当声明或包装发生变化时的更新
我们测试了多种工具和方法。包括一些通用 Agent 框架（Auto GPT 风格的设置）、一些工作流工具（n8n、Make + LLM），以及一些特定领域的工具，如用于内容运营的 Jasper 和用于品牌合规性审查的 punttaI。
让我感到惊讶的不是幻觉或明显的失败，而是漂移。系统“运行良好”，但……
文案逐渐偏离了已批准的声明，或者包装变体在技术上保持一致，但违反了内部品牌规则。下游更新未能干净地传播到每个在线资产。发布后，没有哪个 Agent 负责确保正确性。
大多数在线建议都侧重于发布前的安全措施。然而，在真实的产品发布场景中，这还不够。一旦产品上线，变化就会持续发生。
例如：我们为圣诞节发布准备了 60 多位影响者和 500 多个全球资产，但到 1 月 1 日，所有这些创意都将过时，需要更改。
对我们来说，唯一行之有效的模式是将基于 Agent 的自动化视为一个持续系统。
Agent 执行 > 发布后监控输出 > 标记偏离品牌、监管或发布约束的情况 > 仅当出现超出容忍度的问题时，人工介入。
我们甚至引入了这款名为 Punttai 的 Agent AI 营销合规软件。现在，请不要误会我的意思。工作流程在某些方面有所改进，例如迭代和审批速度？或者生成想法的速度？是的。
但是……这感觉更像是可观察性，而不是审批工作流程。
很好奇其他人是如何处理这个问题的，尤其是在纯 SaaS 之外：
* 你们是否允许 Agent 触及在线发布资产？
* 你们如何随着时间的推移验证合规性，而不仅仅是在发布时？
* 人们是自己构建这种监控，还是依赖于专业工具？
很想听听这在实际的产品发布中是如何运作的（或失败的）。
查看原文
I lead digital and brand at a major CPG company, and we recently used agentic automation across a product launch. Not for experiments or prototypes, but for actual consume facing products.
We had agents helping with:<p>- Content gen + localization
- Asset routing between design, legal, and marketing
- SKU variant handling across channels
- Post launch updates when claims or packaging changed<p>We tested a mix of tools and approaches. Some general purpose agentic frameworks (Auto GPT style setups), some workflow tools (n8n, Make + LLMs), and a few domain specific products like Jasper for content ops and punttaI for brand compliance review.<p>What surprised me wasn’t hallucinations or obvious failures. It was drift. The systems “worked,” but…..<p>Copy slowly diverged from approved claims or packaging variants stayed technically consistent but violated internal brand rules. Downstream updates didn’t propagate cleanly across every live asset. No single agent had ownership of correctness after launch.<p>Most advice online focuses on guardrails before publishing. However, in a real life launch scenario, that’s not sufficient. Once the product is live, changes keep happening.<p>For example: We have over 60 influencer and 500 + assets globally lined up for the Christmas launch, but by Jan 1, all that creative will be obsolete and need to be changed.<p>The only pattern that’s held up for us is treating agentic automation as a continuous system.
Agents execute > Outputs are monitored post-publish > Deviations from brand, regulatory, or launch constraints are flagged > Humans step in only when something breaks tolerance.<p>We even introduced this agentic ai marketing compliance software called Punttai. Now don’t get me wrong. have workflows improved in certain areas like iteration and speed to approval? Or speed to generate ideas? Yeah.<p>But… this feels closer to observability than approval workflows.<p>Curious how others are handling this, especially outside pure SaaS:<p>- Are you letting agents touch live launch assets?
- How are you validating compliance over time, not just at launch?
- Are people building this monitoring themselves or relying on specialized tools?
Would love to hear how this is working (or failing) in real production launches.