Not a traditional coder, but building a backend-guarded sales AI agent—how are you guys structuring the guardrails?"
一位非传统程序员正在构建一个由后端保护的销售AI代理，并寻求关于如何构建防护措施的建议。后端作为严格的规则执行者，负责控制最大折扣、特定产品SKU以及处理库存查询或支付生成等函数调用。该开发者询问其他人如何实施这些严格的后端防护措施，特别是是否依赖函数调用模式、严格的系统提示或特定框架来防止AI行为失控。
为什么是这条该帖子展示了非传统编码员构建AI代理的方法，不同于许多侧重于CS背景开发者的讨论。
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发布当时偏移：UTC+02026年9月8日 21:37 UTC
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Hey everyone! Wanted to share a thought process I've been working on, especially since I'm not a traditional programmer, but using AI as my co-pilot/mentor to actually build it.
The core idea: Never let the LLM have autonomy over the business logic.
Instead of letting an AI chat freely and potentially hallucinate discounts, stock, or policies, the architecture splits hard:
- The LLM: Strictly handles the frontend interface, natural language, empathy, and copywriting.
- The Backend / Guardrails: Acts as the strict rule-enforcer (e.g., max discount allowed, specific product SKUs, handling function calls to check inventory or generate PIX/payments).
Basically, the AI thinks it's selling, but the backend is holding the leash the entire time.
For those of you building AI agents or apps without a CS background (or even if you're a seasoned dev): how are you implementing these strict backend guardrails? Are you relying heavily on function calling schemas, rigid system prompts, or specific frameworks to keep the AI from going off the rails?