Customer service
Post LinkedIn lead magnet · Customer service
There's a fundamental tension in AI Agent design today! And it becomes obvious only when you start building for production: The more strictly you enforce an instruction, the more you sacrifice contextual nuance. Think of it this way. When you are building a customer-facing Agent, some instructions are indeed non-negotiable. Thus, you want your Agent to enforce them strictly, even if it sounds robotic when doing so. For instance, instructions like compliance disclosures in finance or safety warnings in healthcare cannot tolerate any mistakes. But other instructions are gentle suggestions, like matching the customer's tone or keeping responses concise. These should influence the conversation, not dominate it. The problem is that most Agent architectures don't let you express this distinction that easily. Every instruction typically gets the same level of enforcement, so you're either forced to be strict about everything and sound robotic, or be flexible about everything and risk missing critical rules. And no, you can't just emphasize certain instructions in the prompt itself because the mere presence of an instruction in the prompt already biases the model's behavior. Emphasis just adds more bias on top of existing bias. But I find Parlant's latest control of "criticality levels" interesting (open-source with 18k stars). It lets you tell your agent how much attention to pay to each instruction. ``` agent.create_guideline( condition="Customer asks about medicines", action="Direct to healthcare provider", criticality=Criticality.HIGH ) agent.create_guideline( condition="Customer completes a purchase", action="Mention the loyalty program", criticality=Criticality.LOW ) ``` You can set an instruction's criticality as LOW, MEDIUM, or HIGH, which makes it easier to achieve the behavioral sweet spot you're looking for in the agent's conversations with users. In general, I love how they're evolving this framework and how the features naturally build up on a basic, solid philosophy from version to version. You can see the full implementation on GitHub and try it yourself. I've shared the repo link in the replies. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!
Mécanisme lead magnet
I've shared the repo link in the replies.
