Analyzing AI agent performance with the reporting dashboard


In the AI agents workspace, the Reporting dashboard provides a comprehensive, at-a-glance view of your organization’s overall AI agent performance. The information in this dashboard is updated hourly to help you stay on top of emerging trends in your data.


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In the AI agents workspace, the Reporting dashboard provides a comprehensive, at-a-glance
view of your organization’s overall AI agent performance. The information in this
dashboard is updated hourly to help you stay on top of emerging trends in your data.
This article contains the following topics:

Accessing the dashboard
About the reports
Filtering the reports
Changing a report's view
Exporting report data



Accessing the dashboard

The dashboard is available under Reporting in the left sidebar.
To access the dashboard



In the AI agents workspace, click
Reporting in the sidebar.







About the reports

The Reporting dashboard contains the following tabs:

Overview tab
Contact reasons tab
Custom resolutions tab
Conversation journey tab



Note: Conversations imported from third-party systems aren’t
included in the Reporting dashboard.



Overview tab

The Overview tab shows key metrics about volume, overall AI agent performance,
and BSAT scores. For the volume and performance metrics, results are also
graphed over time and you can change the report’s view to group the results by AI agent, channel,
or language.
You can filter the reports
on this tab by AI agent, Channel, Reply type, Use case, Language, and Label.


Performance

The left side of the Performance section shows the following metrics:


Total conversations: The number of conversations handled by your
AI agents.

Understood conversations: The percentage and number of
conversations where any of the following are true:
An answer was provided from a knowledge source.
A use case was triggered.
The conversation was directly escalated to a human agent without
an answer being provided or a use case being triggered.
Note: For AI agents with agentic
AI, this metric excludes small talk and
conversations escalated through the Escalation system reply.
Because the Escalation system reply is excluded, this metric
might be lower compared to other AI agents because manually
created use cases for escalations are no longer
triggered.




Unassisted conversations: The percentage and number of
conversations that triggered only 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 escalations: The percentage and number of conversations
that were escalated to a human agent.

Automated resolutions: The percentage and number of conversations
that included at least one identified use case (other than a system
reply) and a provided response, or else a sufficient knowledge answer
provided by agentic reasoning or the Generative replies / uGPT
reply.
You can filter this
report by the automated resolution tier
values:


Assisted escalation: The AI agent contributed to the
interaction before a human agent completed the resolution.

Contained resolution: The AI agent handled the
interaction to completion without the customer requesting
further assistance.

Verified resolution: The AI agent successfully resolved
the interaction.



The right side of the AI agent workload section shows the following BSAT metrics:


Ratings requested: The percentage and number of conversations
where customers were requested to provide a satisfaction score.

Ratings given: The percentage and number of conversations where
customers provided a response for BSAT, divided by the number of
conversations where feedback was requested.

BSAT score: The sum of the number of responses with a 4-5 score
provided, divided by the total number of responses.
The colored bar indicates the proportion of each feedback response from
5 to 1, shown in shades of green, yellow, and red.
Below the bar, each rating is broken out with its respective percentage
and number of responses relative to the total responses.






Contact reasons tab

The Contact reasons tab shows key metrics about the performance of use cases and
knowledge sources across your AI agents and channels.
From this tab, you can also jump to the use case suggestions report to quickly
create new use cases that aren’t currently covered.


Note: Use case–based metrics may differ between the
reporting dashboard and the conversation logs because of how
the data is filtered:
In the reporting dashboard, use cases are included when they’ve been
detected during a conversation or else triggered by a linked reply.
In the conversation logs, use cases are included only when they’ve
been detected during a conversation. Use cases triggered by a linked
reply aren’t included.



This tab contains the following reports:


Use case performance

Knowledge sources



Use case performance

The Use case performance report shows a table of all existing use cases and
their respective performance across your AI agents and channels. You can
filter this report
by AI agent, Channel, Reply type, Use case, Language, and Label.
This table includes the following columns:


Use case: The name of the dialogue or procedure.

AI agent: The AI agent that the dialogue or procedure was created
for.

Channel: The channel that the AI agent is configured for.
Possible values include Messaging and Email.

Reply method: How the reply was triggered. Possible values
include Dialogues and Procedures.

Total conversations: The number of conversations where the use
case was detected.

Automated resolutions: The number of automated resolutions across
conversations where the use case was detected.

Custom resolutions: (Appears only for customers who deployed an
AI agent on at least one messaging channel before September 8, 2025.)
The percentage of conversations where a custom resolution was applied.
For more information, see Custom resolutions tab.

Escalated conversations: The escalation rate across conversations
where the use case was detected.

BSAT: The BSAT score across conversations where the use case was
detected.


From this table, you can click a use case to view a detailed report about its
performance over time. This information helps you understand how a specific
use case has contributed to your organization’s overall automation
success.

You can filter the use case report by Conversation source, Segment, Language,
and Label.
The use case report includes the following information:


Use case automation over time: A column chart showing the total
volume of conversations that triggered this use case over the last week.
Overlaid line graphs show the number of those conversations that
resulted in either an automated resolution or an escalation.

Total conversations: Conversations that triggered this use case.

Escalated conversations: Conversations with this use case that
were escalated to a human agent.

Automated resolutions (AR): Conversations with this use case that
resulted in an automated resolution.

BSAT: Conversations with this use case with a BSAT rating of 4 or 5.

Conversations: A list of conversations that correspond to the
filter conditions set in the report, showing the following columns:

Conversation time: The date and time the conversation
started.

Handled: Whether the conversation was fully handled by
the AI agent. This includes messaging conversations with a
recognized use case and no escalation attempts, and emails where
an AI agent reply was sent.

Resolution: One of the following possible resolution
statuses:

Automated resolution: Evaluated by the LLM and
determined to be an automated resolution.

Not resolved: Evaluated by the LLM but determined
not to be an automated resolution, or else not
handled.

Escalated conversation: Escalated to a human
agent. Not evaluated by the LLM.



Reasoning data: The outcome of the LLM evaluation
describing why a conversation is considered to be an automated
resolution. Reasoning data is shown only for conversations that
are considered Handled.

BSAT: The conversation’s BSAT rating.



The conversations list has a maximum of 500 rows. Only the most relevant
first 500 conversations are shown, based on the filters and sort conditions
applied. To view additional conversations, use the conversations log.



Knowledge sources

The Knowledge sources report shows a table of imported knowledge sources and
their respective performance. You can filter this report by AI agent, Channel, Language,
and Label.
This table includes the following columns:


Article name: The title of the article. This will be listed
across all article sources included.

Knowledge base type: The knowledge source of the article.
Possible values include Web crawler, Zendesk, and CSV.

Knowledge base name: The name the knowledge base was given when
it was imported.

Usage: The percentage of conversations where the article was used
to generate a reply (out of the total number of conversations where an
AI-generated reply was provided).

Escalated conversations: The percentage of conversations where
the article was used to generate a reply and the conversation was later
escalated.

Automated resolutions: The percentage of conversations where the
article was used to generate a reply and the conversation counted as an
automated resolution.

BSAT: The average BSAT score across conversations where the
article was used to generate a response.






Custom resolutions tab



Note: This tab is available only to customers who deployed
an AI agent on at least one messaging channel before September 8,
2025.

The Custom resolutions tab shows KPIs of each of the custom resolution states. For each of
these KPIs, the results are also graphed over time and you can change the
report’s view to group the results by total volume, channel, AI agent, or
language.
You can filter the reports on this tab by AI
agent, Channel, Reply type, Use case, Language, and Label.


Custom resolution state KPIs

The Custom resolutions tab shows the following custom resolution state
metrics:


Total conversations: The number of conversations with an AI
agent.

Custom resolution rate: The percentage and actual number of AI
agent conversations that ended with a custom resolution state.

Informed: The percentage and actual number of AI agent
conversations where the AI agent provided guidance to the customer. For
more information, see Using the informed state.

Resolved: The percentage and actual number of AI agent
conversations that ended with a meaningful resolution and no further
questions from the customer.

Escalated via email: The percentage and actual number of AI agent
conversations that were converted into an email ticket. This state is
automatically set when the conversation flow reaches an Escalation block
in a dialogue with the “Send an email” option set.

Escalated to agent: The percentage and actual number of AI agent
conversations that were escalated to a human agent. This state is
automatically set when the conversation flow reaches an Escalation block
in a dialogue with the “Forward to an agent” or “Custom escalation”
option set.

Undefined: The percentage and actual number of AI agent
conversations that ended without a custom resolution state, often due to
drop-off or use cases missing a custom resolution state.

Unresolved: The percentage and actual number of AI agent
conversations where the issue was not answered or resolved.






Conversation journey tab

The Conversation journey tab helps you understand, in aggregate, the
conversational path that customers follow when they have a conversation with
your AI agent. This information provides insight into where you can:

Improve knowledge content to reduce escalations
Reduce the number of steps required to deflect an issue
Replicate successful conversation paths across different use cases
Report on use cases with the highest automation success



Note: This tab shows only information related to use
cases. AI-generated replies that exist outside of use cases (in either
dialogues or generative procedures) aren’t included in the reports on this
tab.

You can filter the reports
on this tab by AI agent, Conversation source, Channel, Language, and First use
case identified.
The Conversation journey tab is made up of a sankey diagram that shows use cases
on the left and conversational end states on the right. The width of the line
from each use case represents the relative volume of conversations with that use
case. Along the way, conversations may flow from one use case to another, before
ultimately reaching one of the following end states:


Assisted escalation: The conversation was escalated by the AI agent
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 successful Generative reply /
Knowledge reply was triggered.

A maximum of six use cases can be shown as part of a conversation path. Use cases
beyond that are truncated.
On the Conversation journey tab, you can:

Click on Single-step conversations to focus on conversations that
triggered one use case. This shows your AI agent’s efficacy in solving
one-touch interactions based on your customers’ questions.

Click on Multi-step conversations to focus on conversations that
triggered more than one use case. This shows which use cases naturally lead
into others, helping you build smoother transitions and pre-populate data
between these use cases to reduce conversational friction.

Click on a specific conversation path to see the journeys customers
take from beginning to end when starting a conversation with a specific use
case.
Hover over a conversational end state to view the conversational
paths and associated use cases that resulted in that outcome. By focusing
on:

Assisted escalations, you can identify and investigate use
cases that might benefit from increased automation so that human
agents don’t need to be involved.

Automated resolutions, you can identify use cases with the
fastest paths to automated resolutions, allowing you to replicate
efficient logic across your AI agent.







Filtering the reports

You can filter the dashboard’s reports by time frame or other attributes.
To filter the dashboard by time frame



In the Reporting
dashboard, 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 beginning and end dates on the calendar





To filter the dashboard by other attributes



In the Reporting
dashboard, click one of the following filters and select its
values as needed depending on the data you want to see:

AI agent: The AI agent who participated in the conversation.

Channel: The channel the conversation took place in. Values
can be Messaging or Email.

Reply type: The type of replies the AI agent can provide. Not
available in the Knowledge sources report. Possible values include:

Use case: Responses that triggered a use case.

Knowledge: Responses generated from a connected knowledge
source.

Hybrid: Responses that both triggered a use case and
were generated from a connected knowledge source.

Other: Responses where neither a use case nor a
knowledge source was used in the reply, such as small-talk
answers or custom instructions.



Use case: The use case or intent detected during the
conversation. (Not available in the Knowledge sources report.)

Language: The language detected during the conversation.

Label: The labels applied to the
conversation.


Click Apply.
Repeat the steps above to apply any additional filters as needed.




Note: If you clear the selections from all values for a given
filter, no report data is returned. At least one value from each filter must be
selected.




Changing a report's view

For the AI agent workload and
custom resolutions reports,
you can apply different views that change how the results are presented. Different
views let you focus on different aspects of the overall data.
To change a report’s view



In the Reporting
dashboard, select one of the following tabs:
For AI agent workload reports: Overview

For custom resolutions reports: Custom resolutions



Select one of the metrics.
Above the visualization on the left side, click the drop-down to select one
of the following report views:
By channel
By AI agent

By language



(Optional) Above the visualization on the right side, click Daily,
Weekly, Monthly, or Quarterly to change how the
report’s results are aggregated.





Exporting report data

You can export report data from the dashboard as a CSV file. This allows you to
further manipulate the results or upload them into your business intelligence tool
of choice.
To export report data



In the Reporting
dashboard, select the tab that contains the report you want to
export.
(Optional) Apply
filters to the tab.
Click Export CSV. The Export CSV panel opens.


In Report, select the report you want to export. The available
reports depend on which tab you've selected:

Overview tab
Total conversations
Understood conversations
Escalated conversations
Assisted conversations
Handled conversations
Automated resolutions
Rated conversations
BSAT score
BSAT score breakdown
Total conversations vs automation
All conversation data


Contact reasons tab
Use case performance with current filters
Use case performance without filters
Knowledge sources with current filters
Knowledge sources data without filters


Custom resolutions tab

Custom resolutions overview (includes data from the
chart)

Custom resolutions (includes data from the KPI cards
only)




If Break down by is available for your selected report, select one of
the following options: By AI agent, By language, By
channel

If Frequency is available for your selected report, select one of the
following options: Daily, Weekly, Monthly,
Quarterly

Note: This option controls the time frame the data
is aggregated by in the report export. It doesn't create a recurring
export of the selected data.


Click Save. Your report is downloaded as a zip file.





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