Reviewing conversation logs for AI agents


Conversation logs allow you to review all messages that have been exchanged between an AI agent and end users during automated conversations. From here, you can review details for specific messages and whole conversations as part of your ongoing AI agent monitoring and analysis.


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Conversation logs allow you to review all messages that have been exchanged between an AI
agent and end users during automated conversations. From here, you can review details
for specific messages and whole conversations as part of your ongoing AI agent
monitoring and analysis.
This article contains the following topics:

Viewing the conversation logs for an AI agent
Filtering the list of conversations
Reviewing details for a whole conversation
Reviewing details for a single message
Reviewing actions taken during conversations
Reviewing API integration information

Related articles:


Using labels to tag conversation content for AI
agents




Viewing the conversation logs for an AI agent

You can view the conversation logs for an AI agent to see the conversations they’ve
had with users and drill deeper into specific responses. Only conversations that
include end-user messages appear in the conversation logs.
To view the conversation logs for an AI agent


In the AI agents workspace, select the AI agent you want to work
with.

Click
Conversation logs in the sidebar. A list of conversations appears for the default time frame (the
past 7 days). If you don’t see any conversations, adjust the time
frame.

(For messaging AI agents only) Toggle the Status / Custom
resolutions setting at the bottom to switch between showing either
the conversation status or custom resolution values. For an explanation of
the values, see the table in the next section.






Filtering the list of conversations

In the conversation logs, you can filter the list of conversations by:

Time frame
Specific attributes



Filtering the list of conversations by time frame

You can filter the list of conversations by time frame.


Note: The time frame filter uses UTC (Coordinated
Universal Time), even though the conversation logs show timestamps in the
user's browser's time zone.

To filter the list of conversations by time frame



In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
In the upper-right corner, click Time frame.
Using one of the following methods, select the dates you want to view
conversations for:
On the right, select one of the predefined time frames:
Today, Yesterday, Last 7 days, Last
30 days, This month, Last month

Select specific start and end dates on the calendar.








Filtering the list of conversations by specific attributes

You can filter the list of conversations by specific attributes such as language,
status, labels, and more.
To filter the list of conversations by other filters


In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
In the upper-left corner, click Add filter.

Select and configure one or more of the following filters:








Filter
Configuration
details


Message text
In the Message text field, enter a
specific word or phrase you want to search for
within the conversations.


Language
Select the languages you want to
see conversations for.


Conversation source
Select one or more of the following
options:

Live conversations: Conversations
between AI agents and end users that are currently
ongoing.

AI agent: Conversations between the AI
agent and end users.

Test widget: Conversations created
through the test widget.

Imported: Conversations imported from a
different system.




Conversation attributes

Select one or more of the following
options:


Conversations with messages not
understood: Conversations with at least one
message that was not understood below the
threshold.


Actions applied: Conversations
with applied actions.


Conversations with interrupted
dialogues: Conversations where customers
interrupted the conversation (for example, by
triggering another use case or loops).


Conversations with drop-offs:
Conversations where users left before completing a
dialogue.


Hide in-progress sessions:
Hide conversations with sessions still within
their duration threshold.





Conversation status

Select one or more of the following
statuses:



Automated resolutions:
Conversations in which the AI agent responded and
no escalation was attempted. You can further
filter by:


Verified resolution:
Conversations that are considered verified
resolutions by Zendesk's large language model
(LLM).


Not-verified resolution:
Conversations that are not considered verified
resolutions by Zendesk's LLM.




Assisted escalation:
Conversations consisting of an attempt to escalate
to a human agent via messaging or email. You can
further filter by the following escalation types:


To an agent


Via email


Failure




Unassisted: Conversations in
which no use case or knowledge answer has been
provided.






Resource usage

Select whether any of the following
resource types were Used or Not used
to generate the AI agent’s answer:


Dialogue


Generative reply through a
dialogue


Procedure


Knowledge answer





BSAT

Select one or more of the last BSAT
rating responses, or select Not now.



Custom resolutions

Select one or more of the following
custom resolution states:


Resolved: A meaningful custom
resolution was provided, and there are no further
questions.

Informed: Instructions or guidance was
provided.

Escalated to agent: The conversation
was successfully transferred to a human
agent.

Escalated via email: The conversation
was successfully converted into an email ticket
for the support team.

Unresolved: The AI agent didn’t answer
or resolve the issue.

Escalation failed: The conversation
wasn’t escalated successfully because no agent was
available, or there was a technical error.

Undefined: No custom resolution is
associated with the conversation.




Labels
Select whether you want to see
conversations with Any of the selected
labels, All of the selected labels, or
None of the selected labels, then select
specific labels. See Using labels to tag
conversation content for AI agents.


Use cases
Select whether you want to see
conversations with any, all, or none of the
selected use cases,
then select specific use cases. This filter
applies only to use cases that were detected
during a conversation, not use cases that were
linked to from other replies.



Segments
Select whether you want to see conversations
with any, all, or none of the selected segments,
then select specific segments.


Suggestions

Select Include knowledge
suggestions to show use case
suggestions that have been categorized as
“Knowledge,” which means the customer’s question
could be answered by a topic in your help
center.
Select Include declined
suggestions to show use case suggestions that
have been declined.



Duration
Select an operator and a
number of seconds to determine the duration
of the conversations you want to see.


Platform conversation ID
In the Platform conversation
ID field, enter an ID from your CRM to see a
specific conversation. This is useful when
comparing logs between your CRM and your AI
agent.






Click Apply.






Reviewing details for a whole conversation

You can review the details for a conversation to get more information about the
overall interaction between the user and the AI agent.
To review details for a whole conversation


In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
Click the conversation you want to view. The conversation appears on the
right half of the page, and includes any messages sent as well as any
actions or events that were
applied.

In the upper-right corner, click Details.
Review the following details in the Conversation overview pane:

Date and time: The date and time the conversation took place.

Duration: How long the conversation lasted.

Test conversation: Whether the conversation was a test (as determined by
whether it was created using the Test AI agent, Test dialogue, or
Test branch options).

Labels: Which labels were added to the conversation. You can
add additional labels by clicking Add.

Platform conversation ID: A unique identifier for the
conversation in the CRM your AI agent is integrated with. This is
used for finding the same conversation in your CRM.

Status: The final status of the conversation. Possible values
include:

Unassisted: The conversation triggered only small
talk or system replies,
with the exception of a successful Generative replies / uGPT
reply. The AI agent performed no automation except for AI
agent-level actions that are triggered for all
conversations.

Assisted escalation: The conversation was escalated
to a human agent.

Automated resolution: The conversation included at
least one identified use case (other
than a system reply) and a response was provided, or else a
sufficient knowledge answer was provided by agentic
reasoning or the Generative replies / uGPT reply.

Resolution tier: Which resolution tier
the conversation falls into.




Click any of the following sections to expand them and view more details:

Conversation resolution: Shows the following information:

Status: The final status of the conversation.

Verified resolutions: Whether the conversation falls
into the Verified resolution
tier, including an AI-generated explanation of
why.

Calculated on: The date and time the automated
resolution decision was made.

Resources used: Which resources the AI agent used to
respond to the customer during the conversation. Possible
values include:

Knowledge replies: AI-generated answers based
on connected knowledge
sources.

Dialogues: Responses or actions executed as
part of a dialogue.

Procedures: Responses or actions executed as
part of a generative
procedure.



Resolution tier: Which resolution tier
the conversation falls into.



Segment matches: Shows which segments applied to the
conversation. You can click a segment name to be taken directly to
its configuration to view or edit it.

Session parameters: Shows the configured actions and
events that occurred
throughout the conversation, the active language set, and the last
detected language.







Reviewing details for a single message

You can review the details for a single message within a conversation to get more
information about why the message was generated (in the case of an AI agent message)
or how the AI agent interpreted the message (in the case of a user message).
To review details for a single message


In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
Click the conversation you want to view. The conversation appears on the
right half of the page, and includes any messages sent as well as any
actions that were
applied.

Hover over the message you want to see more information about and click the
View details icon ( ) that appears. The icon appears to the left
of AI agent messages and to the right of user messages.


Review the message details that appear in the pane on the right.
For user messages, the pane is called Message overview and
shows the following information:

Message: The exact text of the user’s message.

Presumed use case: (Appears only for zero-training and
agentic AI
agents.) The use case that best
fits the conversation. An AI-generated explanation describes
why the use case was selected.



For AI agent messages associated with a dialogue, the pane is
called Reply overview and shows the following details:

Details


Used reply: Dialogue the message came from.

Reply type: Type of reply.

Intent: Use case the dialogue is associated
with.



Edit reply


Edit dialogue: Opens the dialogue
builder for the dialogue that the message
came from.





For AI agent messages associated with a generative procedure, the
pane is called AI agent message overview and shows the
following details:

Details


Use case: Which use case the generative
procedure is associated with.

Plan: The AI agent’s reasoning in determining
how to resolve the user’s issue.

Procedure step: Click to view the specific
step within a generative procedure that produced the
message.

Response before customization: The response
text that the AI agent generated before the AI agent
persona or any instructions
were applied.



Active instructions: Any instructions you’ve created
that are currently active. These instructions affect the AI
agent’s ultimate response to the user.

Personalization

AI agent name: Name of the AI agent.

Company name: Name of your company.

Tone: Tone of
voice you selected for the AI agent.

Answer length: Answer
length you selected for the AI
agent.












Reviewing actions taken during conversations

In the conversation logs, you can find detailed information about which actions were applied to a conversation.
This includes configured actions, custom actions, and action flows.
Note: For API integration information, see the section below
instead.

To review actions in conversations logs

In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
Select a conversation. In the conversation pane that appears, any actions
executed during the conversation appear in gray boxes.


Select a gray action box to see which specific actions were executed.

Click the action name to open a panel with the action's details.
The top section of the panel
shows when the action was executed and which dialogue or generative
procedure triggered it. For configured actions only, the action could also
have been triggered at the AI agent level or the use case level.
The
middle section shows the action's name, configuration details, and (for
custom actions and action flows) whether the Success or Fallback scenario
was triggered.
The last section includes a link to open the action's
configuration page.

(Optional) Click View action, View custom action, or View
action flow to open the action's configuration page. Custom actions
and action flows open in Admin Center. See Editing a custom action or Editing and managing action
flows.
Configured actions open an Action details
pane.

From this pane, you can:


Click Edit action to edit the configured action.
Select any of the entries under References to open the action at the
AI agent level, use case level, or block level.






Reviewing API integration information

You can review information about API requests that take place during conversations
between your AI agent and customers. This information includes when the calls were
made and details of the calls, including request and session parameters, the
response, any errors, and more. This information can be especially useful for
troubleshooting.


Note: Legacy API integrations don’t appear in conversation
logs.

To review API request information


In the AI agents workspace, select the AI agent you want to work
with.
Click
Conversation logs in the sidebar.
Click the conversation you want to view. Look for entries in the
conversation labeled Integration triggered or Integration
parameter requested.

Click an integration entry to see the specific API request or
requests.

Click an API request to open the Integration details panel.
Here, you can review the
following details about the API request:

Date and time: The timestamp of when the API request was
triggered during the conversation.

Source: The name of the dialogue that triggered the API
request. You can click the name to open the dialogue in the dialogue
builder.

API integration: The name of the integration.

Scenario: Which scenario (success, failure, fallback, or
custom) was triggered as part of the API integration block in the
dialogue.

Request parameters: Any request parameters
(inputs) that were configured for the API request. Errors due to
missing required values or data type mismatches are highlighted in
red.

Session parameters: Any session parameters
(outputs) that were configured for the API request.

API error: (Appears only if an error occurred.) Details about
the error.


(Optional) Click View integration to open the integration in the
integration builder.




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