Evaluation Strategies for Generative Agents in Conversational Agents/Dialogflow CX

Google Cloud Build with AI Conversational Agents (Dialogflow CX)
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Hi everyone,

I’m currently working on a setup using Generative Agents/Playbooks in Dialogflow CX console and I’ve run into a challenge regarding automated evaluation.

Traditionally, the built-in Test Cases tool works OK for flow-based agents where we can assert specific intents or parameters. However, with generative responses, this “Golden Dataset” approach becomes tricky. Even if the agent provides a correct and helpful answer, it rarely matches the “Expected Response” in a Test Case word-for-word, leading to false negatives.

I’m looking for insights on how you handle automated evaluation at scale for low-code/generative bots. Specifically:

  1. LLM-as-a-judge: Have any of you integrated external scripts (using Vertex AI/Gemini) to compare the agent’s output against a Golden Dataset using semantic similarity or rubric-based scoring?

  2. Continuous Integration: How are you triggering these evaluations? Are you exporting the agent and running tests via the API, or using the “Experimentation” features in the console?

  3. Metrics: Besides ROUGE or BLEU, which metrics have you found most reliable for “groundedness” and “helpfulness” in a CX context?

I’d love to hear if you are sticking to manual “Side-by-Side” (SxS) testing or if you’ve built a custom pipeline to bridge the gap that the current Test Case tool leaves behind.

Looking forward to your thoughts!

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Hi @abbynormal You are right that golden-response matching does not work well for generative agents, since correct answers rarely match word-for-word.

A common approach is LLM-as-a-judge: export conversation outputs via API and evaluate them with a secondary model (for example through Vertex AI) using a structured rubric. Instead of ROUGE or BLEU, score dimensions like task completion, groundedness, helpfulness, and policy compliance. Rubric-based scoring is usually much more reliable.

For CI, many teams trigger scripted conversations against the agent endpoint after deployment, store the responses, and run automated evaluation in a pipeline. Console Experiments can help for comparisons, but API-driven testing is more flexible for continuous integration.

In practice, a combination of automated rubric scoring plus periodic human review tends to work best