Not a traditional coder, but building a backend-guarded sales AI agent—how are you guys structuring the guardrails?"
A developer, not a traditional coder, is building a backend-guarded sales AI agent and is seeking advice on structuring guardrails. The backend acts as a strict rule-enforcer for aspects like maximum discounts, specific product SKUs, and handling function calls for inventory or payments. The developer is asking how others implement these strict backend guardrails, specifically if they rely on function calling schemas, rigid system prompts, or specific frameworks to prevent the AI from misbehaving.
Why this oneThis post highlights a non-traditional coder's approach to building an AI agent, unlike many discussions that focus on developers with strong CS backgrounds.
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- 09/08, 21:37 UTC+0
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- 09/09, 17:00 UTC+0
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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?