Gina Wassyng, Senior AI Trainer & Head of Quality at Envive AI

AI agents are becoming the primary interface through which shoppers interact with brands to make buying decisions—it’s estimated that up to 60% of shoppers state that they prefer interacting with AI agents during the exploratory phase of purchasing.¹ As agentic commerce evolves toward autonomous recommendations, real-time personalization, and behaving as natural extensions of brands, the stakes for getting agent behavior right have never been higher.

Ensuring safe, accurate and relevant agent responses is both a business-critical requirement and an outcome I take seriously as Head of Quality. Sales assistant chatbot responses carry real risk when they go wrong, such as:

• Providing medical or legal advice to customers—particularly vulnerable or underage parties
• Providing inaccurate product information
• Providing discounts or promises when breached by malicious attacks

Not only do dangerous or inaccurate responses lead to increased drop-offs and cart abandonment, but they also pose real threats to end users and companies in terms of cost and safety.

As Head of Quality responsible for over 30 sales agents representing top e-commerce brands, how do I ensure that our agents maintain accuracy rates equivalent to those of a human shopping assistant without relying on humans to manually assess thousands of conversations for factuality, relevance, helpfulness, tone, and safety? 

We accomplish this through a strict evaluation framework grounded in synthetic customer simulation, LLM-as-a-judge, and humans-in-the-loop.

Accuracy & Intent

• Synthetic customer simulation: My team of AI Trainers and QA specialists spend significant time developing optimal, high-intent scenarios to evaluate our sales agents’ behavior. We deploy a purpose-built user agent that role-plays as a real shopper—asking the kinds of targeted, high-intent questions that customers ask during their actual buying journeys: product comparisons, sizing questions, ingredient concerns, gift recommendations, and more. This approach, now widely recognized across the industry as a best practice for agent testing, lets us stress-test agent behavior at scale before any real customer is exposed to it—all with the goal of improving shopper confidence and add-to-cart rates.
  
  
• LLM-as-a-judge: We evaluate these synthetic customer simulations using LLM-as-a-judge against a defined set of criteria, utilizing several frontier LLMs as a judge. My goal is to always assess for top performance and alignment with humans to ensure that our sales agent responses:  
  
  • Provide relevant product recommendations during the shopping journey
  • Are accurate according to the brand’s data
  • Align with custom brand voice requirements
  • Never compromise on safety
    
    
• Human-in-the-loop: Our QA team reviews flagged conversations to identify, diagnose, and correct failure modes. Humans handle what agents can’t—catching nuanced failures, labeling edge cases, and feeding findings back into an evolving test case library that gets smarter with every onboarding. The level of evaluation rigor my team applies is above and beyond what other GPT-wrapper companies are doing today.

In our evaluations, we help identify knowledge gaps in both the sales agent and merchant product catalogs, enabling us to bolster response accuracy and helpfulness. For example, in a recent evaluation of a leading footwear brand, we increased their agent eval performance score from 83% to 90% by optimizing their data and content ingestion.

Safety

Ensuring safe LLM responses is critical for companies selling in the agentic era. Specific concerns include protecting end users and company information, and preventing illegal activity. Envive evaluates safe agent responses through a variety of test scenarios, such as: 

• Health or Medical Advice
• Legal Advice
• Pricing Manipulation
• Autonomous Action Boundaries
• Competitor Questions
• Data Privacy
• Injection Attacks

To make this concrete: when a shopper asks a supplements brand agent about medication interactions or whether a product is safe to take while pregnant, a failing agent might provide specific health guidance or simply claim “I don’t know how to answer that,” creating liability and real risk for the end user. A passing agent recognizes the boundary, redirects warmly without abandoning the conversation, and encourages the customer to consult a healthcare professional. That distinction is exactly what our safety test cases are designed to surface, and it’s the kind of failure that never shows up in a product demo.

Proprietary Dataset Built Across Verticals

One of Envive’s most significant advantages in our evaluation process is our training data. Over time, we systematically collected real-world conversation data and transformed them into targeted test cases across a wide range of e-commerce verticals—apparel, skincare, supplements, wine, baby products, and more.

These cross-vertical datasets are proprietary and developed in-house. It means our agents have been trained and evaluated against the kinds of nuanced, domain-specific conversations that actually happen in each category, rather than generic e-commerce scenarios. A supplements shopper asking about ingredient stacking is a fundamentally different conversation than a new parent asking about sleep safety for a baby monitor. Our rigorous evaluation methods ensure that our agents respond to customers in highly personalized, performance-driven ways, always on-brand.

New players entering the AI commerce space are starting from zero. They’re building on general-purpose LLMs without vertical-specific signals. With every new merchant we deploy, every evaluation cycle we run, and every failure mode we document and resolve, we prove a compounding advantage—and it’s one of the reasons our agents perform at the level they do across such diverse brand categories.

Our Pillars: Quality and Safety

The difference between an agent that sounds good in a demo and one that reliably performs across thousands of real customer conversations comes down entirely to evaluation rigor.

Most GPT-wrapper deployments skip this layer entirely. They tune on a handful of example conversations and move on. At Envive, our evaluation framework is a continuous, ongoing process—not a checkbox before launch. Every new merchant vertical, every agent update, and every observed failure mode feeds back into a growing library of test cases that makes each subsequent deployment safer and more accurate.

For e-commerce brands, the question isn't whether to deploy an AI sales agent. It's whether the agent you deploy will represent your brand the way you'd want a top-performing human sales associate to. That's the standard I hold myself to—and the standard I believe the industry should demand.

Sources

1. Conversica. (2026, March 18). Nearly 60% of Buyers Prefer AI Agents in the Sales Process—New Report Demonstrates Their Value in Driving Sales Conversions https://www.businesswire.com/news/home/20250318144173/en/Nearly-60-of-Buyers-Prefer-AI-Agents-in-the-Sales-Process---New-Report-Demonstrates-Their-Value-in-Driving-Sales-Conversions