Evaluate your agent’s performance by using AI-generated tests cases or uploading your
own test cases in a CSV template. AI-generated test cases offer two data options: generating
tests based on subagents and actions, or generating tests based on the knowledge available to the
agent via an Agentforce Data Library.
Each testing approach helps you simulate real-world interactions and catch issues
early, but some are more useful than others depending on your testing goals.
Subagents and actions test cases are formatted as conversations, enabling evaluation
at both the single-utterance and multi-turn conversation level. We recommend generating subagents
and actions tests to evaluate how your agent handles specific intents or workflows, especially
after making updates or before launching an agent. This approach checks that the agent
triggers the correct behaviors based on user utterances.
Knowledge-based test cases are formatted as question-and-answer style utterances
taken from the knowledge information available to the agent. We recommend generating
knowledge-based tests to verify that your agent delivers accurate, content-driven responses
from connected knowledge sources, such as articles or PDFs. This approach is ideal for
confirming that important information, like company policies or product specifications, is
correctly retrieved and communicated.
If you need more control, download the provided CSV template, fill it with your
custom test scenarios, and upload it. This approach is best for tailored testing or validating
specific business logic.
Generate Tests from Subagents and Actions Evaluate how well your agent handles key subagents and actions. Generate relevant, test-ready utterances by using your subagent and action metadata as well as information from connected data libraries.
Generate Tests from Knowledge Sources When your agent is connected to an Agentforce Data Library, you can generate test scenarios directly from the content linked to that library. You can base test cases on multiple data sources including articles and knowledge fields or uploaded files.
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