These essential tips assure AI agents function properly before and after they hit production.
The excitement surrounding generative AI in customer experience (CX) is undeniable. From ultra-responsive chatbots to autonomous “agentic” systems that can rebook flights or process refunds, the potential for scale and efficiency is transformative. However, as we move from scripted, deterministic logic to probabilistic large language models (LLMs), the traditional testing playbook isn’t enough any longer.
In a world where an AI agent might provide a different (yet correct) answer to the same prompt every time, or suddenly go haywire, how do you ensure it stays on brand, replies within the scope of company policy, and otherwise keeps out of trouble? As Metrigy has found in its CX research, new risks associated with the use of AI agents are driving many companies to adopt tools and processes aimed at assuring AI agents are performing as expected prior to deployment and that they keep doing so once implemented.
Drawing from insights shared in conversations with a host of industry experts, here are seven essential tips for assuring AI agents function properly before and after they hit production.
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