Conversational testing

June 24, 2019
|By Lukas Volz

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Conversational testing refers to the systematic testing of the processes defined in the conversation flow for an AI agent for naturalness and comprehensibility before they go into production. 

The goal is to identify early on whether the wording sounds natural, whether the dialogue achieves its objective, and whether the desired tone is maintained. This process is a central component of conversational design and supplements automated testing methods with human evaluation. In this way, conversational testing combines traditional quality assurance with the hybrid intelligence requirements of modern conversational AI.

 

What Conversational Testing Reveals

Conversational testing reveals areas where the AI agent's conversational capabilities still need improvement. These include: 

  • Convoluted or tediously long sentences that don't work in voice chat.
  • Missing or unclear follow-up questions when the intent is ambiguous.
  • Shifts in tone between formal and informal passages.
  • Gaps in the conversation flow where users cannot identify a logical next step.
  • Answers that are factually correct but miss the point.

Implications for Voice and Chat

In the voice channel, for example, with a voicebot in hotline triage, conversational testing is particularly valuable. Spoken language does not tolerate convoluted constructions, and users expect short, clear responses. 

In the context of chat and email, the focus shifts to readability, tone, and striking the right balance between precision and empathy. Here, too, testing reveals whether responses are perceived as helpful or whether users need to ask follow-up questions to clarify the issue.

 

Conversational Testing in Multi-Agent Setups

In complex scenarios, multiple specialized AI agents work together—for example, for authentication, case handling, and escalation. Conversational testing becomes particularly relevant here at the handoff stage, because gaps in communication between agents can quickly lead to repetitions or lost contextual information. In conjunction with Knowledge AI and defined AI workflows, this approach helps identify process boundaries and clearly delineate the areas of responsibility for each agent.

For effective implementation, an iterative approach is recommended: the results of testing are incorporated into revised training phrases, adjusted fallback paths, and refined workflow steps. This leads to continuous improvement, making conversational AI significantly more robust over time.

Frequently Asked Questions (FAQ)

It makes sense to use this approach once a conversation flow has been roughly established and the key responses have been formulated. In practice, testing takes place between the conversational copywriting phase and technical implementation in order to identify weaknesses early on. However, it can also be repeated later for new use cases or during major revisions to the dialogue.

Automated tests primarily assess the NLU model’s recognition performance and the technical stability of workflows. Conversational testing supplements this by incorporating human evaluation of tone, conversational flow, and perceived helpfulness. Both approaches are complementary and should be used together in professional conversational AI development.

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