## Opening the AI activity dashboard and confirming access
In Atloria, AI activity monitoring is typically handled from the admin side of the workspace rather than from everyday document editing screens. If you already worked through [Managing AI Usage and Request History](doc:managing-ai-usage-and-request-history), use that as your starting point for where AI-related controls live. For monitoring, open the main signed-in area and go to the **Admin** workspace. From there, look for cards and menu items related to usage reporting, especially **Analytics** and other AI-related settings areas.

The **Admin** workspace includes sections such as **Users & Permissions**, **Organizations**, **Documents**, **Projects**, and **Analytics**. Open **Analytics** when you want a reporting-style view of activity. Depending on your access level, you may also review AI-related information from workspace settings or project-level areas where AI features are managed. Administrators usually have the broadest visibility, while team leads may only see activity tied to their own workspace, team, or assigned projects.

When the dashboard opens, first confirm the filters at the top of the page before you interpret anything. If available in your Atloria workspace, check these controls:

- **Date range**
- **Team** or **Project**
- **User**
- Any tab or switcher for **Usage**, **Requests**, or **Activity**

These controls matter because the same screen can show very different totals depending on the selected scope.

On first load, expect a summary view rather than a full investigation screen. Atloria may show headline metrics such as:

- Total AI requests
- Active users
- Consumption totals
- Recent request entries

If the **Analytics & Insights** page currently shows a coming-soon message in your workspace, that means the reporting area is present in navigation but not yet available for detailed review. In that case, continue using the AI usage and request history areas you already learned in [Managing AI Usage and Request History](doc:managing-ai-usage-and-request-history).

[SCREENSHOT: Admin workspace with the Analytics card highlighted]

## Reviewing AI usage totals across teams, users, and time periods
Once you are in the AI usage or activity area, start with the summary metrics before opening individual request records. These top-level numbers help you answer basic questions quickly: who is using Atloria’s AI features most, whether usage is increasing, and whether activity is spread across teams or concentrated in one area.

Read the top cards first. In a typical usage view, these cards summarize:

- Total request count
- Overall AI consumption
- Active users during the selected period
- Team, workspace, or project totals

If your Atloria workspace includes breakdown cards, compare them side by side instead of looking at one number in isolation. A high request count from one project may be normal if that project has many contributors, while a smaller team with the same volume may deserve a closer look.

Use the **Date range** control to compare patterns over time. Daily views help you investigate a sudden spike. Weekly and monthly views are better for spotting sustained changes, such as a team adopting AI-assisted drafting more heavily over several release cycles. When you change the date range, recheck the totals and any trend line or summary table below it.

Then narrow the scope with the available filters:

1. Select the broadest level first, such as the full organization or workspace.
2. Switch to a specific **Team** or **Project** to see where usage is concentrated.
3. Apply a **User** filter if you need to understand one person’s activity.
4. Compare the filtered totals against the broader view.

Trend charts and summary tables are most useful when read together. The chart shows when activity rose or dropped, while the table helps you identify which users or groups drove that change. If one team appears at the top of both the chart and the breakdown list, that is usually where you should investigate next.

[SCREENSHOT: AI usage dashboard showing summary cards, date range selector, and team filter]

## Inspecting individual request history and response details
After you identify a time period or team worth reviewing, move from summary totals into the request history list. This is where you confirm what actually happened rather than relying only on totals. In Atloria, the request list is the most useful view for checking whether a spike came from normal drafting work, repeated retries, or failed AI actions.

Open the **Requests** or **Activity** tab if your workspace separates summary reporting from request-level history. In the list, review the visible columns first. Depending on what your Atloria workspace shows, common details include:

| Column | What to look for |
|---|---|
| **Timestamp** | When the request happened |
| **User** | Who submitted it |
| **Model** | Which AI option handled the request |
| **Status** | Whether it completed or failed |
| **Project** or **Workspace** | Where the request came from |

Use filters before opening individual rows. This is faster than scrolling through a long list. Narrow the log by:

- **User**
- **Status**
- **Model**
- **Date**
- **Project** or **Workspace**, if available

Once you find the request you need, select the row to open its detail view. The detail panel or detail page may include the original prompt, the generated response, how long the request took, and usage-related details tied to that interaction. This view is especially helpful when a user reports that AI output looked incomplete or when a team lead wants to confirm whether a request actually ran.

Pay close attention to the **Status** indicator. Completed requests show successful activity. Failed, canceled, or rate-limited requests point to a different kind of issue. If several entries from the same user or project share the same non-success status, you are no longer looking at normal usage volume—you are looking at a pattern that needs follow-up.

[SCREENSHOT: Request history list with filters and one request detail panel open]

## Understanding activity patterns and spotting unusual usage
AI monitoring becomes more useful when you stop looking at single requests and start comparing patterns. In Atloria, unusual activity is often easier to spot when you move back and forth between the summary dashboard and the request history list. One view tells you that something changed; the other tells you what caused it.

Start by comparing high-volume users or teams against their usual baseline. A writer working on a large documentation release may naturally generate more AI requests than usual for a few days. That is different from a sudden jump with no matching project activity. Look for changes that stand out from the team’s normal rhythm rather than assuming every spike is a problem.

Watch for patterns such as:

- Repeated failed requests from the same user
- Short bursts of many requests in a narrow time window
- Heavy concentration in one project or workspace
- Activity appearing outside the team’s normal working period
- One AI feature driving most of the consumption

If your Atloria view includes model-level or feature-level breakdowns, use them to understand what kind of work is generating demand. For example, chat-style assistance, drafting, or summarization may each create different request patterns. A rise in drafting activity during a release cycle may be expected. A burst of repeated failed requests in one feature is more likely to indicate confusion, misuse, or a workflow problem.

The most reliable way to judge unusual usage is to compare aggregate and detailed views together:

1. Use the summary dashboard to find spikes, dips, or top users.
2. Open the matching request history for that same period.
3. Check whether the entries reflect real documentation work.
4. Confirm whether statuses, timing, and volume look consistent.

This combined approach helps you separate legitimate high activity from wasteful retries, accidental overuse, or behavior that needs policy review.

## Using usage insights to manage AI-assisted operations responsibly
The goal of monitoring is not just to collect numbers. In Atloria, usage insights are most valuable when they help you improve how teams use AI in documentation work. Once you identify patterns, turn them into practical decisions about guidance, access, and review expectations.

Use per-user and per-team activity data during coaching conversations. If support agents, writers, or project contributors rely heavily on AI for first drafts, that may be completely appropriate. What matters is whether the request history shows productive use or repeated low-value activity. For example, a team with steady completed requests tied to active project work may simply be using Atloria effectively. A user with many failed or repeated requests may need help choosing the right workflow.

You can use monitoring results to support decisions such as:

- Clarifying when AI drafting is appropriate
- Reinforcing manual review before publishing
- Reviewing who needs access to AI-assisted features
- Identifying teams that would benefit from additional guidance
- Escalating recurring concerns to administrators

Request patterns can also reveal where AI is helping most. If one team consistently uses AI during document creation and still moves smoothly through review, that is a strong signal that the workflow is working. If another team shows heavy request volume but frequent retries or poor output quality, that may indicate a need for tighter review steps or better prompt habits.

When activity raises concerns—such as overuse, repeated failed interactions, or prompts that suggest sensitive-content handling—coordinate with administrators through the appropriate admin areas. Atloria’s **Admin** workspace, including **Users & Permissions**, **Organizations**, **Security & Audit**, and **Analytics**, gives you the right places to align usage findings with access and policy decisions. For broader admin follow-up, see [Using the Admin Workspace](doc:using-the-admin-workspace) and [Reviewing Security and Audit Controls](doc:reviewing-security-and-audit-controls).

## Resolving common issues when reviewing AI activity
If AI activity does not look right in Atloria, the issue is often caused by filters, visibility limits, or timing differences between summary totals and request-level history. Before assuming data is missing, work through the screen controls carefully.

If no activity appears in the dashboard, start with the filters at the top of the page. A narrow **Date range** or the wrong **Team** or **Project** can make the dashboard look empty even when requests exist. Also confirm that you are looking in the correct workspace area for AI monitoring rather than a general admin page that does not yet include live reporting. If your **Analytics & Insights** page shows a coming-soon notice, use the AI usage and request history screens instead.

If a team lead cannot see expected request history, the most likely cause is limited visibility. Team leads may only be able to review activity for assigned teams, workspaces, or projects. In that case:

- Check whether the correct team or project is selected
- Remove extra user filters
- Confirm the person is opening the right admin or workspace area
- Ask an administrator to verify access if broader visibility is needed

If usage totals do not match the recent request list, compare like with like. Summary cards may reflect an aggregated period, while the request list may show only the currently filtered entries. Some summaries may also differ from visible request rows if failed requests are counted separately or if totals refresh on a delay.

If you cannot find a specific request, narrow the search instead of broadening it. Try a shorter time window, then add the **User** filter and **Status** filter. Requests with failed or rate-limited outcomes are easier to find when you isolate those statuses directly.

[SCREENSHOT: Filter bar with date range, team, user, and status controls]

## Overview
This guide focuses on monitoring AI activity after AI usage tracking is already familiar. Instead of repeating the basics from [Monitoring AI Usage and Request History](doc:monitoring-ai-usage-and-request-history) and [Managing AI Usage and Request History](doc:managing-ai-usage-and-request-history), the emphasis here is on reading activity patterns, checking request details, and using those findings to support responsible team operations.

In Atloria, AI monitoring usually involves two connected views:

- A summary area that shows totals, trends, and top usage patterns
- A request history area that shows individual AI interactions

You use the summary view to answer questions like “Which team is driving most AI activity this week?” or “Did usage spike after a release started?” You use the request list to answer more specific questions like “Which user submitted these requests?” or “Were these requests completed, failed, or retried?”

This matters most for administrators and team leads who need to balance productivity with oversight. Atloria supports AI-assisted work across documentation, support, and project workflows, so request volume alone does not tell the full story. A large increase may reflect healthy adoption, or it may point to repeated retries, unclear guidance, or access that needs review.

As you work through this guide, stay focused on three tasks:

- Confirm you are viewing the right scope with the right filters
- Compare totals and trends before judging individual behavior
- Use request details to validate whether unusual activity is expected

The next document, [Reviewing AI Usage and Request Activity](doc:reviewing-ai-usage-and-request-activity), builds on this by helping you turn monitoring findings into a more formal review process.

## Prerequisites
Before you monitor AI activity in Atloria, make sure the basics are already in place. This guide assumes you can sign in successfully, reach the authenticated workspace, and open the admin or workspace areas available to your role. If you need help with account access first, use [Signing In to Atloria and Solving Access Problems](doc:signing-in-to-atloria-and-solving-access-problems).

You should also already be familiar with the AI usage screens covered earlier in this documentation set. This guide does not repeat setup or basic navigation steps from [Managing AI Usage and Request History](doc:managing-ai-usage-and-request-history). Instead, it focuses on how to interpret what you see once you are already in the monitoring area.

Before you begin, confirm the following:

- You can access Atloria’s signed-in workspace
- You have a role that allows you to view AI usage or admin reporting
- AI-assisted features are in use by your team, workspace, or project
- You know which team, project, or user group you want to review
- You are prepared to compare both summary totals and request-level history

It also helps to know where related admin areas are located. In Atloria, the **Admin** workspace includes cards such as **Users & Permissions**, **Organizations**, **Documents**, **Projects**, and **Analytics**. Depending on your permissions, you may use these areas alongside AI activity monitoring when you need to follow up on unusual usage.

If your workspace’s **Analytics & Insights** page is not yet active and only shows a placeholder message, you can still use the available AI usage and request history screens to perform the monitoring steps described in this guide.