Enterprise Agent Analytics: Analyze Any CustomGPT.ai Agent’s Usage and Conversations

Connect Enterprise Agent to another CustomGPT.ai agent. Turn its usage and conversations into a custom dashboard or report with one request.

Built-in analytics give you raw data about how your agent is being used, but the harder questions start after that. Which topics are growing fastest? What are people asking that the agent cannot answer? Are negative ratings concentrated around one type of question or one deployment? Which knowledge gaps should your team fix first?

Answering those questions usually means combining usage metrics with the underlying conversations, performing your own analysis, and turning the findings into something stakeholders can use.

With Enterprise Agent, you can create a dedicated analytics agent for another CustomGPT.ai agent. It connects to the data, analyzes it, and produces the dashboard or report you ask for, turning agent analytics into a finished deliverable instead of a spreadsheet you have to build yourself.

Direct answer: Create a second CustomGPT.ai agent in Plan & Act mode, connect it to your existing agent through an MCP server with read-only permissions (Read agent’s messages and Access agent’s analytics), and ask it to analyze usage and conversations together. It plans the steps, pulls the data, and returns a dashboard or report. On Premium and Enterprise plans, this can also run on a recurring schedule.

Ask Questions Beyond a Standard Dashboard

CustomGPT.ai already provides analytics for conversations, queries, queries per conversation, user feedback, daily trends, user locations, deployment sources, and query status. Customer Intelligence adds context such as user intent, emotion, language, whether a content source was found, which deployment was used, and which agentic actions ran.

A dedicated analytics agent lets you work across both layers at once.

You can ask it to compare this month with last month, find the most common unanswered questions, identify the themes behind negative feedback, or show whether a recent website deployment changed usage.

You can then ask follow-up questions without exporting the data or rebuilding the analysis elsewhere.

The numbers show what changed. The conversations help explain why.

The same Enterprise Agent capabilities power plenty of other use cases too. See what this looks like in practice for examples that read files and connect to external systems instead of another agent’s data.

One Request, Three Capabilities Working Together

This use case brings together Plan & Act, Smart Tasks, and a connection between two CustomGPT.ai agents through MCP.

Suppose you ask:

Build an executive dashboard for our customer-support agent covering the previous 30 days. Show usage trends, the most common user intents, negative-feedback patterns, unanswered questions, and the five highest-priority recommendations.

Access is read-only, by design

The analytics agent connects to the source agent through a CustomGPT.ai MCP server. You can enable only Read agent’s messages and Access agent’s analytics, while leaving Send a message disabled. The analytics agent can inspect what it needs for reporting without posting into the agent it analyzes.

The agent plans the work, so you don’t have to

Once the timeframe and request are clear, the agent determines the steps required and carries them out in order. Its plan might include retrieving aggregate analytics, fetching the relevant conversations, calculating trends, grouping queries by intent and outcome, identifying repeated knowledge gaps, and assembling the results into a dashboard.

From raw data to a finished deliverable

External connections retrieve the data through MCP. Secure code execution can clean, aggregate, compare, and transform it. Live Dashboards can turn the result into an interactive view with charts and filters. When a downloadable deliverable is more useful, the agent can create a report or data export instead.

When necessary information is missing, the agent can stop and ask rather than quietly make an assumption.

From Raw Activity to an Executive Review

The output is shaped by your request rather than a fixed reporting template.

A monthly dashboard could begin with total conversations, queries, queries per conversation, feedback, and changes from the previous period. A trend chart could show when usage rose or fell, while a deployment breakdown shows where those interactions came from.

The agent can then analyze the conversations behind the metrics. It might group requests by intent, surface recurring areas of confusion, identify questions for which no useful content source was found, and summarize the themes behind negative feedback.

Most importantly, it can turn the findings into recommended action: add documentation for a repeated unanswered topic, revise a response flow associated with poor feedback, expand content in a fast-growing language, or investigate failures concentrated in one deployment.

You can follow up immediately:

• Break the unanswered questions down by guest versus team member.
• Compare website-widget usage with API usage.
• Turn the recommendations into a prioritized content backlog.
• Send me the query groups and supporting examples as CSV.

The analytics agent continues from the information and context it has already gathered instead of forcing you to begin another reporting workflow.

Set Up Your Enterprise Agent Analytics

Step 1: Create the analytics agent

Create a new CustomGPT.ai agent in Plan & Act mode. It does not need knowledge-base sources of its own because its data comes from the agent it analyzes. See connect an analytics agent to your agent for the full walkthrough.

Step 2: Set its persona

Tell the agent that its role is to analyze another agent’s conversations and usage, confirm the reporting timeframe when one has not been provided, and produce executive-ready dashboards and reports. 

Step 3: Enable the Smart Tasks it needs

Enable external connections and live dashboards. Enable file creation as well when you need downloadable reports or exports.

Step 4: Open the source agent to MCP server

Go to Deploy > MCP server. Select Other, enable Read agent’s messages and Access agent’s analytics, leave Send a message disabled, and copy the server link after saving the permissions.

Step 5: Connect the analytics agent to it

On the analytics agent, go to Actions > Custom connectors, add an MCP connector, paste the link, and set end-user confirmation to No. This gives the analytics agent access to the source agent without asking for confirmation during every reporting request.

Step 6: Verify the connection

Ask for last week’s query volume, then ask for the five most common unanswered questions. The first checks access to analytics; the second checks access to conversation content. See verify your analytics agent connection for the full check.

For more detail on how CustomGPT.ai actions and MCP connections work, see:

• How Custom Actions Work
• Deploy Using MCP Server
• Connect Your Analytics Agent With an MCP Server
• MCP Server Permissions
• Plan & Act Mode Overview
• Smart Tasks Overview

Put the Report on a Schedule

Once the analytics agent produces the right report, Premium and Enterprise users can save the request as a Scheduled Task.

For example:

Analyze the previous seven days of support-agent usage, compare it with the seven days before that, create an executive dashboard, and post a five-point summary to our connected Slack reporting channel.

Set up the automated schedule inside the app.

Scheduled runs complete unattended, so the prompt should explicitly include the rolling timeframe, required analysis, output, and delivery destination.

That turns a one-off request into a repeatable operating report.

Frequently Asked Questions

Does the analytics agent need its own knowledge base?

No. The analytics agent’s data comes from the agent it connects to through MCP, not from documents or a website you upload to it directly. You still create it as a regular CustomGPT.ai agent with Plan & Act mode turned on, but it does not need its own knowledge-base sources to produce a report or dashboard.

Can the analytics agent post messages into the agent it is analyzing?

No, not unless that specific permission is enabled. The MCP connection between the two agents is scoped to Read agent’s messages and Access agent’s analytics, with Send a message left disabled by default. That means the analytics agent can read conversations and usage data without ever posting into the agent it is reporting on.

What happens if the analytics agent is missing information it needs to finish a report?

It stops and asks rather than guessing. If a request is ambiguous, for example a reporting timeframe was never specified, the agent asks for clarification instead of assuming a default and delivering a report built on that assumption, the same behavior Plan & Act mode uses for any task.

Can I get a downloadable file instead of an in-chat dashboard?

Yes. When file creation is enabled alongside the other Smart Tasks capabilities, the analytics agent can produce a downloadable report or a data export instead of, or in addition to, an interactive Live Dashboard, depending on what the specific request asks for.

Which plans support scheduling a recurring analytics report?

Premium and Enterprise plans. On those plans, once an analytics request produces the report you want, it can be saved as a Scheduled Task, so something like a weekly executive summary runs automatically on a repeating schedule instead of being requested by hand each time.

Does this replace CustomGPT.ai’s built-in analytics or Customer Intelligence?

No, it works on top of both. Built-in analytics and Customer Intelligence already track usage metrics and conversation-level context. The analytics agent combines that data with the underlying conversations themselves, so you can ask open-ended questions instead of only reading fixed charts and predefined metrics.

Do I need developer access to set this up?

No. Setup happens through the same no-code agent dashboard used to build any CustomGPT.ai agent: creating the analytics agent, turning on the Smart Tasks capabilities it needs, and connecting it to the source agent through an MCP server, all from the interface, with no code involved.

Conclusion

Built-in analytics show you the activity around your agent. Enterprise Agent lets you investigate that activity, combine the numbers with what users actually said, and create the exact agent analytics your team needs, not a fixed dashboard, the report your specific question calls for. 

For general Plan & Act setup, see how to set up a no-code AI agent that completes tasks. For more of what Enterprise Agent can do beyond analytics, see what this looks like in practice.

Start your free trial and build your first analytics agent.