Review and analyze GenerativeAgent conversations to improve performance.
As you add use cases to GenerativeAgent and refine its configuration, you will need to see how GenerativeAgent handles real customer interactions. Conversation Explorer is a powerful tool that enables you to fine-tune and review GenerativeAgent’s customer interactions.
For each conversation, you can see the full model actions GenerativeAgent took which includes its input, knowledge, reasoning, actions, and output back to the customer.
Conversation Explorer also shows conversations that have been flagged as having quality issues.
To get started with Conversation Explorer:
Access Conversation Explorer
Request access to Conversation Explorer from your admin.
Once granted, you can access Conversation Explorer at the following URLs:
Find a conversation
Use the search and filter interface to locate specific interactions or patterns:
Review the interaction
Once you have found a conversation, you can see exactly how GenerativeAgent makes decisions:
Your admin must grant Conversation Explorer permissions before you can access the interface.
Conversation Explorer provides a search and filter interface to locate specific interactions or patterns.
Use the search bar to find conversations containing specific words or phrases. Enclose terms in quotes for exact matches.
To find Quality Issues:
You can share a conversation with others by clicking the “Copy Link” button when viewing a conversation.
You can also share your current filtered view by copying the URL of your current page.
Once you have found a conversation, you can see exactly how GenerativeAgent makes decisions by viewing its internal reasoning process via model actions.
Model actions are the input, knowledge, api calls, reasoning, and output of GenerativeAgent’s model while handling the customer interaction.
The information in the model actions can drive how you update the configuration of your tasks and functions.
Model actions are categorized into the following:
When enabling a model action category, there may be multiple model actions with the same category that will be displayed. e.g. enabling Functions will show both a “Function Call” for the request and a “Function Response” for the response.
Authentication
Actions occur when GenerativeAgent needs authentication data to call an API. Only used for API Connections that require client data.
Confirmation requests
Actions occur when GenerativeAgent needs to confirm an action with the customer.
Functions
Actions that occur when GenerativeAgent calls a function to handle the customer interaction.
The model actions will show:
Function calls may appear as multiple entries in the model action stream.
You can also see the “Raw” JSON interaction between GenerativeAgent and the function.
Errors
Actions occur when GenerativeAgent encounters an error.
HILA
Actions occur when GenerativeAgent needs consultation from a human agent as part of Human in the Loop.
Input variables
The input variables that GenerativeAgent uses to call a function.
Knowledge
The Knowledgebase articles that GenerativeAgent was given to answer the customer’s question.
Out-of-scope Customer Message
This occurs when GenerativeAgent determines that the customer’s question is out of scope for the current task.
Tasks
The task that GenerativeAgent is entering or changing into in order to handle the customer interaction.
Thoughts
GenerativeAgent’s internal thoughts and reasoning process.
Transfer to agent
This occurs when GenerativeAgent performs a transfer to agent to escalate the conversation to a human agent.
Unsafe Customer Input
This occurs when GenerativeAgent determines that the customer’s input is unsafe.
When looking at a conversation, model actions are displayed inline with the conversation flow. This allows you to understand exactly when and why the AI made specific decisions during the interaction.
To review model actions:
Open a conversation
In the center panel, enable the model actions you want to review
View the AI’s reasoning process inline with the conversation, showing the chronological flow of decisions
Click any model action to see detailed information.
This example shows a function response.
You can also see the “Raw” JSON interaction between GenerativeAgent and the function.
Our monitoring system can flag a conversation as having potential quality issues as determined by our quality evaluators. When quality issues are detected:
Our quality evaluators look for: