Why Testing AI Agents Is More Conversation Than Code

When I first transitioned into testing enterprise conversational agents built on modern LLM orchestration layers, my very first question had absolutely nothing to do with large language models. I looked at our teams jira board, and at our sprint objectives, and asked: Where on earth are the test cases?

I was preparing to QA an autonomous customer service agent tasked with handling real-world account mutations—things like processing order cancellations, pulling dynamic inventory data, and executing subscription updates via backend web hooks. Having spent years in traditional software quality assurance, my brain was defaulted to look for familiar safety measures: strict Product Requirement Documents (PRDs), predictable deterministic API contract definitions, static staging databases, and absolute acceptance criteria. I thought I would just memorize a few trendy AI buzzwords and quickly get back to writing standard execution scripts.

Instead, my first onboarding architecture review of the conversational agent system flooded my screen with variables I had never encountered in a web app: user utterances, intent classification thresholds, context window limits, system prompts, automated fallback thresholds, and human-in-the-loop escalation routing