Skip to main content
”Conversations”, formerly known as Conversation Explorer, is now fully integrated into the AI-Console to provide a centralized platform for viewing, monitoring, and managing conversations. This integration also enables faster navigation, improved usability, and streamlines access control and permission management.
As you add use cases and refine GenerativeAgent’s configuration, you can review and fine-tune how the GenerativeAgent handles real 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. You can also see conversations that have been flagged as having quality issues.

​
Before you Begin

Before reviewing conversations, you need to:
  • Get Access to “Conversations” - Request access to “Conversations” from your admin. Once granted, you can access the “Conversations” section in the AI-Console.

​
Review Conversations

To get started reviewing Conversations:
1

Access Conversations

Once you have access to Conversations:
  1. Sign in to the AI-Console.
  2. From the AI-Console landing page, navigate to the “GenerativeAgent” section in the left-hand menu.
  3. On the left-hand panel, navigate to Conversations.
    Conversations
2

Find a conversation

Use the search and filter interface to locate specific interactions or patterns:
  • Use date filters to narrow your search
  • Search by conversation ID, customer name, or keywords
  • Filter by specific tasks, functions, or model actions
Search functionality
3

Review the interaction

Once you have found a conversation, you can see exactly how GenerativeAgent makes decisions:
  • Enable model actions to see GenerativeAgent’s reasoning
  • Click on model actions for detailed function responses
  • Check the quality tab for flagged interactions
Model actions in conversation
Your admin must grant “Conversations” permissions before you can access the interface.

​
Find conversations

You can use the search and filter interface to locate specific Conversations or patterns.

​
Search and filter options

Use the search bar to find conversations containing specific words or phrases. Enclose terms in quotes for exact matches.
Filter options
  • Date range: Select specific time periods
  • Task: Find conversations where specific tasks were performed
  • Functions: Locate conversations that called particular APIs
  • Conversation ID: Search for a specific conversation

​
Filter for flagged conversations

To find Quality Issues:
  1. Add the “GenerativeAgent Flags” filter
  2. Review flagged interactions to understand quality alerts
Flags filter

​
Share a conversation

You can share a conversation with others by clicking the “Copy Link” button when viewing a conversation.
Copy link
You can also share your current filtered view by copying the URL of your current page.

​
Analyze Model Actions

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 in conversation

​
Model actions categories

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.
Actions occur when GenerativeAgent needs authentication data to call an API. Only used for API Connections that require client data.
Actions occur when GenerativeAgent needs to confirm an action with the customer.
Actions that occur when GenerativeAgent calls a function to handle the customer interaction.The model actions will show:
  • The function name
  • Input parameters
  • Output
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.
Actions occur when GenerativeAgent encounters an error.
Actions occur when GenerativeAgent needs consultation from a human agent as part of Human in the Loop.
The input variables that GenerativeAgent uses to call a function.
The Knowledgebase articles that GenerativeAgent was given to answer the customer’s question.
This occurs when GenerativeAgent determines that the customer’s question is out of scope for the current task.
The task that GenerativeAgent is entering or changing into in order to handle the customer interaction.
GenerativeAgent’s internal thoughts and reasoning process.
This occurs when GenerativeAgent performs a transfer to agent to escalate the conversation to a human agent.
This occurs when GenerativeAgent determines that the customer’s input is unsafe.

​
Review model actions

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:
  1. Open a conversation
  2. In the center panel, enable the model actions you want to review
    Model actions filter
  3. View the AI’s reasoning process inline with the conversation, showing the chronological flow of decisions
  4. Click any model action to see detailed information. This example shows a function response.
    Model action details
    You can also see the “Raw” JSON interaction between GenerativeAgent and the function.

​
Quality issues

Our monitoring system can flag a conversation as having potential quality issues as determined by our quality evaluators. When quality issues are detected:
  • Inline indicators: Flagged messages appear with visual indicators directly in the conversation flow
  • Quality tab: The “Quality” tab provides detailed information about each flagged utterance.
  • Customizable flagging: Define which messages should be flagged to match your team’s needs.
Quality tab
Our monitoring system uses quality evaluators to flag conversations that may have quality issues. Work with your ASAPP Account team to understand the quality evaluators for your company.

​
Next steps

Improve Tasks

Refine tasks, functions, and knowledge base based on your analysis.

Previewer

Live test your configuration within the dashboard using the Previewer.

Troubleshooting

Fix problems identified through conversation review.