docs(ai-monitoring): Add setUser / user attribution to Conversations docs

docs/ai/monitoring/conversations/index.mdx

@@ -12,6 +12,8 @@ keywords:
   - gen_ai.conversation.id
   - conversation tracking
   - LLM conversations
+  - setUser
+  - user attribution
 ---
 
 <Alert>
@@ -47,6 +49,26 @@ Conversations and traces are independent concepts. A single conversation can spa
 
 The reverse is also true: a single trace can contain spans from different conversations. For example, if a user starts a new chat session without refreshing the page, the new conversation's spans appear in the same trace as the previous one.
 
+## User Attribution
+
+The Conversations view includes a **User** column that shows which user initiated each conversation. To populate it, call `setUser` (JavaScript) or `set_user` (Python) once per request or session, before any AI calls:
+
+```javascript
+// JavaScript / Node.js
+import * as Sentry from "@sentry/node";
+
+Sentry.setUser({ id: "user_123", email: "jane@example.com", username: "jane" });
+```
+
+```python
+# Python
+import sentry_sdk
+
+sentry_sdk.set_user({"id": "user_123", "email": "jane@example.com", "username": "jane"})
+```
+
+Any of `id`, `email`, or `username` is sufficient. If no user is set, the column displays "Unknown".
+
 ## Conversations List
 
 The [Conversations](https://sentry.io/orgredirect/organizations/:orgslug/explore/conversations/) page shows the most recent conversations that match your filters.
@@ -55,6 +77,7 @@ The [Conversations](https://sentry.io/orgredirect/organizations/:orgslug/explore
 
 Each row in the list displays:
 
+- **User** — the user who initiated the conversation, populated by `setUser` / `set_user`
 - **First input** — the first user message in the conversation
 - **Last output** — the most recent assistant response
 - **Cost** — estimated dollar cost and token usage

docs/platforms/javascript/common/ai-agent-monitoring/index.mdx

@@ -190,6 +190,18 @@ Sentry.setConversationId(null);
 </SplitSection>
 </SplitLayout>
 
+## Identifying Users in Conversations
+
+The [Conversations](/ai/monitoring/conversations/) view includes a **User** column. To populate it, call `setUser` once per request or session, before any AI calls:
+
+```javascript
+import * as Sentry from "___SDK_PACKAGE___";
+
+Sentry.setUser({ id: "user_123", email: "jane@example.com", username: "jane" });
+```
+
+Any of `id`, `email`, or `username` is sufficient — Conversations displays whichever fields are present. See the [setUser API reference](/platforms/javascript/apis/#setUser) for the full list of supported fields.
+
 ## Manual Instrumentation
 
 If you're using a library that Sentry does not automatically instrument, you can manually instrument your code to capture spans. For your AI agents data to show up in the [AI Agents Dashboards](https://sentry.io/orgredirect/organizations/:orgslug/dashboards/?filter=onlyPrebuilt&query=agents&sort=mostPopular), spans must have well-defined names and data attributes.

docs/platforms/python/tracing/instrumentation/custom-instrumentation/ai-agents-module.mdx

@@ -227,6 +227,18 @@ response = openai.responses.create(
 )
 ```
 
+## Identifying Users in Conversations
+
+The [Conversations](/ai/monitoring/conversations/) view includes a **User** column. To populate it, call `sentry_sdk.set_user` once per request or session, before any AI calls:
+
+```python
+import sentry_sdk
+
+sentry_sdk.set_user({"id": "user_123", "email": "jane@example.com", "username": "jane"})
+```
+
+Any of `id`, `email`, or `username` is sufficient — Conversations displays whichever fields are present.
+
 ## Common Span Attributes
 
 <Include name="tracing/ai-agents-module/common-span-attributes" />