Managing AI Usage and Request History

Opening the AI usage dashboard and understanding what it measures

In Atloria, open the main workspace and go to the Admin area. From there, select Analytics when you need a high-level view of usage reporting, or open your organization’s AI settings area if your team manages AI features there. The usage dashboard is the place to review overall AI activity rather than individual prompts one by one. If you need a refresher on where AI-related reporting fits into broader monitoring, see Monitoring AI Usage and Request History.

At the top of the dashboard, look for summary cards or headline totals that show broad activity for the selected period. These metrics typically help you answer questions such as:

• How many AI requests were made
• How many people actively used AI features
• How much recent activity happened during the selected period
• Whether usage is rising, steady, or slowing down

These totals are meant for pattern spotting. They help you understand volume and direction, not the details of a single interaction.

Use the date range control before drawing conclusions from the numbers. Changing the date range updates the totals, the chart, and the request list shown on the page. A short range helps with recent incidents or sudden changes. A longer range is better when you want to compare adoption over time or check whether a spike is part of a larger trend.

It helps to separate two views of the same information:

• Usage dashboard view: shows combined totals, charts, and trends across many requests
• Request History view: shows individual AI interactions in a list so you can inspect specific activity

Use the dashboard first when your question is “How much AI activity happened?” Switch to request history when your question is “What exactly happened in this request?”

Reviewing request history for individual AI interactions

When you need to inspect specific AI-assisted actions, open Request History from the AI administration area. This view shows a time-based list of activity, with the newest entries typically appearing first. Unlike the summary dashboard, this screen is built for investigation. Use it when you need to review a particular user’s activity, check what happened during a failed action, or confirm whether repeated requests came from the same workflow.

Start by narrowing the list with the controls available on the page. Common filters include:

• Search to find matching text
• Date range to limit the list to a specific period
• User to focus on one person or a smaller group
• Status to show only certain outcomes

These controls are especially useful when the history list is long. If you are investigating a recent issue, begin with the date filter and then add a user or status filter to reduce noise.

Open a single history entry to review its details. Depending on what Atloria shows in your workspace, you may see information such as the person who started the request, the time it happened, the related action or prompt context, and whether the response completed successfully. This is the best place to confirm what happened during one interaction rather than relying on summary totals.

The status shown in each entry helps you interpret the result quickly:

If you only need broad monitoring, stay in the dashboard. If you need evidence for a support review or policy check, request history is the more useful screen.

Analyzing activity trends to spot spikes, drops, and unusual patterns

The activity trend chart helps you move from raw totals to a clearer story about how AI features are being used over time. After choosing a date range, read the chart from left to right and look for changes in request volume. A steady line usually suggests consistent use. Sharp increases or sudden drops deserve a closer look, especially if they do not match your team’s normal work cycle.

Begin with the broad pattern. Ask whether the chart shows:

1. A gradual rise in usage over time
2. A sudden spike during a short period
3. A drop that may point to reduced adoption or an interruption
4. Repeated peaks that line up with a recurring workflow

If Atloria shows breakdown or grouping controls in the reporting area, use them to compare activity across different parts of your organization. Depending on what is available in your workspace, you may be able to compare usage by user, team, workspace, or feature area. These comparisons help you tell the difference between healthy growth and concentrated activity from a small number of people.

A spike does not always mean a problem. It may reflect stronger adoption, a new documentation effort, or a team using AI assistance more heavily during a release cycle. It can also point to repeated retries, inefficient prompting, or misuse. That is why the chart should always be checked alongside Request History.

For example, if the chart shows a sudden jump, open the matching time period in the history list and review the entries around that window. Look for patterns such as:

• Many requests from one user
• Repeated failed attempts
• A burst of similar actions in a short time
• Normal activity spread across several users

This comparison between chart trends and individual entries gives you a more reliable basis for follow-up than totals alone.

Using usage insights to manage AI-assisted workflows responsibly

Usage reporting is most useful when it leads to practical decisions. In Atloria, high request volume often points to workflows where teams rely heavily on AI assistance, such as drafting documentation, reviewing content, or supporting project setup. That does not automatically mean something is wrong, but it does tell you where closer review may be worthwhile.

Start with the busiest areas. If one team, workspace, or feature area generates far more requests than others, review whether that usage matches the team’s responsibilities. Heavy use may be expected during documentation generation or large content updates. It can also show that people are leaning on AI for tasks that may need clearer guidance, better prompts, or a more consistent review process.

Failed and repeated requests are often even more useful than high totals. Open the history list and look for clusters of unsuccessful entries or repeated attempts close together. Those patterns can reveal:

• Prompts that are too vague or inconsistent
• Workflows that encourage users to retry the same action
• Support issues that are driving repeated requests
• AI-assisted steps that need clearer internal guidance

You can also use the reporting view to identify who is generating the most activity. That helps administrators decide where to focus coaching, approval oversight, or policy reminders. In many teams, a small number of users account for a large share of requests. Reviewing those patterns can improve consistency without limiting everyone else.

For sensitive or high-impact AI use, keep a simple internal review routine tied to what you can actually see in the dashboard and history screens. For example:

• Review trend changes during regular admin check-ins
• Inspect unusual spikes in the history list
• Check repeated failures before they become a support burden
• Note recurring high-volume workflows for policy review

This kind of review keeps AI use visible and manageable without turning every request into a manual audit.

Controlling access and setting expectations for administrators and support leads

Not everyone in Atloria needs access to AI usage reporting. Because these screens can include user-linked activity and request details, access should be limited to people who have a clear reason to review them. In most organizations, that usually includes:

• Project administrators
• Documentation managers
• Support team leads
• Organization administrators responsible for AI settings or oversight

Each role may need a different level of review. Project administrators often focus on whether AI activity supports project work as expected. Documentation managers may watch for heavy usage around drafting and revision workflows. Support leads are more likely to open individual history entries when investigating failed requests or unusual bursts of activity.

Set clear expectations for what each group should check regularly:

Before anyone reviews prompts, responses, or user-linked records, communicate that these screens may show activity connected to named users and specific AI-assisted actions. Teams should understand that usage reporting is for operational review, support follow-up, and responsible oversight—not casual browsing.

It also helps to agree on a review rhythm. Many teams do well with:

• A weekly check of trend data
• A monthly review of broader usage patterns
• An incident-based review of request history when something goes wrong

If support leads cannot reach the right screens, coordinate with the people who manage admin access. For broader guidance on admin areas, see Using the Admin Workspace and Managing User Access and Administrative Permissions.

Resolving common issues when usage data or request history looks incomplete

If the AI usage dashboard appears empty or the request list seems incomplete, start with the simplest explanation: the current filters may be hiding the data you expect to see. In Atloria, the selected date range affects both charts and totals, so first confirm that you are looking at the right period. A short range can make it seem like no activity happened when the requests actually fall just outside the selected window.

If the dashboard still shows no activity, check whether the relevant users or projects are actively using Atloria’s AI features. A blank chart or zero totals may reflect a genuine lack of activity rather than a reporting problem.

When expected entries are missing from Request History, review every active filter on the page:

1. Clear the search box if you entered a keyword
2. Reset the date range to a wider period
3. Remove any user filter that limits the list
4. Remove any status filter that hides successful, failed, or limited requests

After clearing filters, reload the view and check the list again.

If chart totals do not seem to match your manual count from the history list, compare what each view includes. Summary reporting may count a broader set of activity than the list you are currently viewing, especially if your history screen is filtered to one user or one status. It is also important to check whether failed, blocked, limited, or retried requests appear in the same way across both views.

Access problems can also make data seem incomplete. If a support lead cannot open usage reporting or request history, review whether they have the right admin, analytics, or audit-related access for those screens. If needed, work with your organization’s admin team to confirm their permissions.

For related admin guidance, see Reviewing Security and Audit Controls and Monitoring Administrative Analytics and Activity.

Overview

This guide focuses on the day-to-day management side of AI reporting in Atloria. Instead of introducing the reporting screens from scratch, it shows how to use them to answer practical questions: who is using AI features, when request volume changes, which entries need closer review, and how administrators can respond when patterns look unusual.

The main areas covered are:

• Opening the AI usage dashboard from Atloria’s admin or AI-related workspace
• Reading top-level totals and understanding what the selected date range changes
• Switching from summary reporting to Request History when you need to inspect a specific interaction
• Using charts and filters to investigate spikes, drops, repeated failures, or concentrated usage
• Turning what you see into follow-up actions such as coaching, policy review, or support investigation
• Limiting access to the right administrative roles and setting a review routine
• Troubleshooting empty charts, missing entries, and permission-related access issues

Use this guide after you already know where the reporting screens are and what they generally show. If you need that foundation first, read Monitoring AI Usage and Request History.

This document stays focused on what administrators and support leads do inside the reporting views themselves. It does not cover broader AI setup, provider configuration, or project-specific content workflows. The goal is to help you manage AI-assisted work responsibly using the dashboard, chart views, and request-level history that Atloria makes available.

Prerequisites

Before working through the steps in this guide, make sure the following are already in place:

• You can sign in to Atloria and open the main authenticated workspace
• You have access to the Admin area, AI-related settings, or other reporting screens used by your organization
• Your team is already using Atloria features that generate AI request activity
• You understand the basic monitoring concepts covered in Monitoring AI Usage and Request History

It also helps if you are one of the people expected to review AI activity regularly, such as:

• An organization administrator
• A project administrator
• A documentation manager
• A support team lead

For this guide to be useful, there should be enough recent activity to review in the dashboard or Request History list. If your organization is just getting started with AI features, you may not see meaningful patterns yet.

Have these details ready before you begin an investigation:

• The approximate date range you want to review
• The user, team, or workspace you are checking, if known
• Whether you are looking for general trends or a specific request
• Any known issue window, such as a recent spike or failed action

If you are unsure whether your account has the right level of access, check with the people who manage admin permissions in Atloria before you begin. After you are comfortable managing usage and request history, continue with Monitoring AI Usage and Request Activity to follow activity patterns more closely.