请问HN：人工智能代理在演示中看起来很出色，但人们是如何使用它的？
我在一家大型消费品公司负责数字和品牌工作，最近我们在产品发布中使用了代理自动化。这不是用于实验或原型，而是针对实际面向消费者的产品。
我们有代理协助处理：
- 内容生成和本地化
- 设计、法律和市场营销之间的资产路由
- 跨渠道的SKU变体处理
- 当声明或包装变更时的发布后更新
我们测试了多种工具和方法。一些通用的代理框架（类似Auto GPT的设置），一些工作流工具（如n8n、Make和大型语言模型），以及一些特定领域的产品，比如用于内容操作的Jasper和用于品牌合规审查的Punttai。
令我感到惊讶的不是幻觉或明显的失败，而是漂移。系统“运作正常”，但……
文案逐渐偏离了批准的声明，或包装变体在技术上保持一致，但违反了内部品牌规则。下游更新没有在每个实时资产中干净地传播。没有任何一个代理在发布后对正确性负责。
网上的大多数建议都集中在发布前的保护措施上。然而，在实际的发布场景中，这并不足够。一旦产品上线，变化就会不断发生。
例如：我们为圣诞发布准备了超过60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，需要进行更改。
对我们来说，唯一有效的模式是将代理自动化视为一个持续的系统。
代理执行 > 发布后监控输出 > 标记与品牌、法规或发布约束的偏差 > 只有在某些事情超出容忍度时，人类才介入。
我们甚至引入了一款名为Punttai的代理AI营销合规软件。现在不要误解我的意思。在某些领域，如迭代和审批速度，工作流确实有所改善？或者创意生成的速度？是的。
但是……这更像是可观察性，而不是审批工作流。
我很好奇其他人是如何处理这个问题的，特别是在纯SaaS之外：
- 你们是否让代理接触实时发布资产？
- 你们如何在时间上验证合规性，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？
我很想听听在实际生产发布中，这种方法是如何运作（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.