Using the AI agent activity real-time monitoring dashboard (EAP)


The AI agent activity dashboard provides visibility into your AI agent activity, in real time. You can use this dashboard to monitor active AI agent conversations, review resolution performance, identify escalation patterns, and troubleshoot failures related to actions, integrations, and escalations.


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This feature is currently in an Early Access Program (EAP). You
can read the latest news and provide feedback in the EAP community.

The AI agent activity dashboard provides visibility into your AI agent
activity, in real time. You can use this dashboard to monitor active AI agent
conversations, review resolution performance, identify escalation patterns, and
troubleshoot failures related to actions, integrations, and escalations.
Additionally, the dashboard helps you see which use cases generate the most activity,
which knowledge sources support successful resolutions, and where assisted escalations
or failed events might point to opportunities for improvement. By using filtering and
drill-in insights, you can better understand activity across every channel, and optimize
AI agent performance at scale. To view this dashboard, you must:
Be using AI agents
Have AI agent tickets enabled
Have Explore admin or viewer access

Must not be using an Advanced AI agent that was onboarded before March 10th,
2026.


This article contains the following topics:

Turning on the AI agent activity real-time monitoring dashboard EAP
Accessing the AI agent activity real-time monitoring dashboard
Understanding the AI agent activity real-time monitoring dashboard reports
Current limitations



Turning on the AI agent activity real-time monitoring dashboard EAP

You can join the AI agent activity dashboard EAP from Admin Center.
To turn on the EAP

In Admin Center, click
Workspaces in the sidebar, then select Analytics.
On the Analytics page, select AI agent activity in real-time
monitoring (EAP).
Click Save.





Accessing the AI agent activity real-time monitoring dashboard

You access the AI agent activity dashboard from the real-time monitoring home page.
Access to the dashboard is independent from access to the AI agents feature.
Dashboard considerations include:
Users can see AI agents independent of any role
restrictions in the AI agents product.
Users can see ticket IDs independent of group or brand
restrictions.
Test conversations are not included in the dashboard reports.




To access the AI agent activity dashboard

In Analytics, click the real-time
monitoring home icon ( ) in the sidebar.
Locate the AI agent activity dashboard and click View.

The AI agent activity dashboard opens.



Understanding the AI agent activity real-time monitoring dashboard reports

The AI agent activity dashboard contains the following tabs:

Activity
Use cases
Knowledge sources
Events



Activity

The activity tab provides a live view of in-progress AI agent
conversations along with historical trends and failure reports. It helps you
monitor current volume, spot issues quickly, and understand AI agent performance
by channel, segment, and use case.


Tip: You can use the real-time monitoring assistant to
reassign tickets from AI agents to human agents on email and voice
channels.

You can filter this tab by AI agent, channel, segment, use case,
language, label, and time - conversation started (historical data).

The tab contains the following reports:
Real-time reports


Total conversations in progress: The number of
conversations in progress across email, messaging and voice. You can drill
into this report to see more information relating to the conversations, such
as ticket ID, AI agent, channel, and more.

Recent history reports


AI agent performance: Helps you to understand how often the
AI agent is resolving conversations on its own or handing them off for human
support. The report shows the following information:

Automated resolutions (AR): The percentage of
conversations that included at least one identified use case (other than a
system reply) and a provided response, or a successful knowledge
answer provided by agentic reasoning or the generative replies/uGPT
reply.

Assisted escalations: The number of conversations
that were escalated to a human agent.



Conversation status: Displays the distribution of
conversation outcomes over time including:

Automated resolutions (AR): The number of
conversations that included at least one identified use case (other than a
system reply) and a provided response, or else a successful
knowledge answer provided by agentic reasoning or the Generative
replies/uGPT reply.

Assisted escalations: The number of conversations
that were escalated to a human agent.

Unassisted conversations: The 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.



Total conversation in progress: Displays the number of
active conversations over time, broken down by channel.

Failed events: Displays the number of failed events over
time, including failed actions, integrations, and escalations.

Conversation status by use case: Shows an overview across
assisted escalations and automated resolutions by use case, helping you
compare which use cases are being handled successfully by the AI agent and
which ones may require more support.

Conversations in progress: Shows a table of active conversations in
progress. Use this table to further investigate live conversations. This
table includes information on ticket ID, AI agent, channel, segment, and
more.




Use cases

The use cases performance tab 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, segment, use case,
language, label, and time - conversation started (historical data).

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.
Values include messaging and email.

Reply method: How the reply was triggered. Values include
dialogues and procedures.

Conversations in progress: The number of conversations in
progress where the use case was detected.

Assisted escalations: The number of conversations that were
escalated to a human agent where the use case was detected.

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




Knowledge sources

The knowledge sources tab shows a table of imported knowledge sources and their
respective performance.
You can filter this report by AI agent, channel, language, and time -
conversation started (historical data).

The table includes the following columns:

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

Type: The knowledge source of the article. Possible
values include web crawler, Zendesk, and CSV.

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

Conversations in progress: 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).

Assisted escalations: The number of conversations where
the article was used to generate a reply and the conversation was later
escalated.

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





Events

The events tab gives you a historical view of actions, escalations, and
integrations triggered by AI agent activity. It helps you monitor success rates,
spot failure patterns, and identify the most common causes of integration
issues.
You can filter by AI agent, channel, segment, use case, language,
label, action, integration, and time - event occurred (historical data).

The tab includes the following reports:

Actions triggered: Displays the number of actions
triggered over time broken down by actions success rate and actions
failure rate.

Escalations triggered: Displays the number of
escalations triggered over time broken down by escalations success rate
and escalations failure rate.

Integrations triggered: Displays the number of
integrations triggered over time broken down by integrations success
rate and integrations failure rate.

Reasons for integration failure: Displays the most
common integration failure reasons.






Current limitations

The AI agents real-time monitoring dashboard currently has the following
limitations:
When you first activate the EAP, only the last seven days of
historical data is available. As you use the EAP, this will build up to 30
days of data.
Messaging conversations won't be included in reports if AI agent tickets are turned
off.
The report filters only show use cases, segments, labels, and language that
have already been used in conversations.




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