Agent Studio
Agent Studio is your monitoring and analytics dashboard for AI agent usage across your workspace.
Overview
Agent Studio gives you visibility into how your team uses AI across notebooks and conversations. Track usage, monitor costs, and review conversation history — all from a single dashboard.
Key Metrics
Four headline cards summarize your workspace's AI activity:
| Metric | Description |
|---|---|
| Conversations | Total AI conversations across all agent types |
| Total Tokens | Aggregated token usage |
| Active Users | Unique team members using the AI agent |
| Cache Hit Rate | Percentage of cached responses, reducing costs |
Each card includes a sparkline showing the trend over your selected time period.
Filtering
Filter the dashboard by:
- Agent type — All, Notebook, Semantic, or Conversation
- User — View activity for a specific team member
- Time period — 7 days, 30 days, 90 days, or all time
Filters are persisted in the URL, so you can bookmark or share specific views.
Usage Charts
Conversations Over Time
A stacked bar chart showing conversation volume by agent type. The chart automatically switches between hourly and daily resolution based on your selected time range.
Top Users
A horizontal bar chart showing the most active users by conversation count.
Token Usage by Model
A grouped bar chart breaking down token usage per model:
- Input (uncached) — Full-cost input tokens
- Input (cached) — Cached tokens that save 90% on input costs
- Output — Generated response tokens
Conversation History
A paginated table of recent conversations with:
- Timestamp
- User
- Agent type (Notebook, Semantic, or Conversation)
- First message preview
- Message count
Click any row to open the full conversation, including all messages with timestamps and role indicators. From the detail view, you can jump directly to the source notebook or conversation.
Use Cases
Cost Optimization
Monitor token usage and cache hit rates to understand your AI spending. High cache rates mean the agent is efficiently reusing prior context.
Team Adoption
Track active users and conversation volume to understand how your team adopts AI-assisted analysis.
Quality Review
Browse conversation history to review the questions your team asks and the answers they receive. This helps identify training opportunities and common patterns.