What "good" looks like for AI Agents in customer service

Accuracy is only the beginning. To build truly effective AI Agents, organizations must establish a clear standard for what quality looks like in practice and how to maintain it as they scale.In this episode, boost.ai's Simeon Kristofferson (Customer Education Manager) and Shiv Chibber (Customer Engagement Manager) sit down to provide a practical mental model for assessing AI agent behavior. They move beyond basic metrics to help you identify issues early and prioritize the improvements that result in reliable, high-performing automation.Key Takeaways:Defining Quality: Why AI agent quality is notoriously difficult to pin down and what it actually means in a real-world service environment.Practical Assessment: A grounded approach to evaluating your agent’s performance to ensure it meets operational standards.Issue Prioritization: How to recognize performance gaps early and decide which optimizations will have the greatest impact on your results.Building for Reliability: Whether you are tuning an existing agent or launching your first, learn how to strengthen your evaluation process and move forward with confidence.