> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agnost.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Conversations

> Review complete user interactions and understand where an agent succeeded or failed

A **conversation** is the user-facing view of one related sequence of agent
interactions. SDKs may call the same boundary a `conversation_id` or `session_id`.
Agnost AI groups those events into one timeline so you can review the user's goal,
the agent's responses, tool activity, and outcome together.

## Open a conversation

1. Open **User Stories** in the Agnost AI sidebar.
2. Apply the workspace, time-range, and metadata filters you need.
3. Select a row to inspect its messages, spans, errors, and available audio.

<Note>
  The dashboard currently labels this surface **User Stories**. In these docs,
  “conversation” is the canonical data term; a user story is the dashboard's
  narrative view of that conversation.
</Note>

## Conversation identity

Reuse one stable conversation identifier across every turn that belongs together.
The integration path determines the field name:

| Integration                | Conversation field                                                 |
| -------------------------- | ------------------------------------------------------------------ |
| Python direct tracking     | `conversation_id`                                                  |
| TypeScript direct tracking | `conversationId`                                                   |
| Ingestion API              | `session_id`                                                       |
| OpenTelemetry              | `session.id`, `conversation.id`, or a documented framework mapping |

Starting a new identifier on every turn fragments the timeline. Reusing one
identifier across unrelated users combines data that should remain separate.

## What to inspect

* Whether the user’s goal was resolved.
* Repeated questions, corrections, and abandoned flows.
* Failed or unexpectedly slow tool calls.
* The intent and violation evidence attached to the conversation.
* Agent-version, plan, surface, or cohort metadata that explains the outcome.

For the underlying records, continue to [Events](/using-events).

## Next steps

* [Events](/using-events): inspect the records behind a conversation.
* [Intents](/using-intents): group conversations by what users wanted.
* [Violations](/using-violations): find where expected behavior was not followed.