The Insights page turns your team’s captured session history into a read-out of spend and run health. Where the Sessions list answers “what happened in this run,” Insights answers the questions that come after a few weeks of real usage: what is all this agent work costing, where is the time and money actually going, and is it producing finished work?
What’s in Insights
Everything on the page is scoped to a time window you pick — the last 7 or 30 days — and every aggregate links back to the real sessions behind it.
The stat strip
Five topline numbers for the window: total spend, tokens used, agent time, tool calls, and success rate. Each comes with a per-session average and a delta against the previous period of the same length.
The absolute numbers tell you scale; the deltas tell you direction. Spend up 200% is a very different story depending on whether success rate held — one is a team adopting agents for more work, the other is money going into runs that don’t finish.
Spend over time
A daily cost chart for the window. The shape is the point: steady daily spend reads differently from one enormous spike. Click a day to pin its most expensive sessions and jump straight into any of them — the fastest route from “what happened Monday?” to the actual run.
Spend by model
Total cost per model, with session counts. This is where you sanity-check your model mix: if the most expensive model is absorbing routine work that a cheaper one handles fine, it shows up here first.
Longest and most expensive sessions
A ranked list you can flip between longest (by turn count) and most expensive. Long isn’t automatically bad — some work is genuinely deep — but a high turn count is often the first symptom of an agent looping, retrying, or grinding against missing context.
Suggestions
Prioritized observations the console pulls out for you: a session that cost several times your median, spend that went to runs that failed or were abandoned, and similar anomalies. Each suggestion links to the session it’s about, so the next step is always “open it and read the chain.” When nothing crosses a threshold, you get a calm all-clear.
- One session cost 468× your median$160.24 in a single run this period.
- $2.73 on runs that didn't finish6 sessions failed or abandoned. Worth a look at what went wrong.
Understanding the numbers
A few things to keep in mind when reading the page:
- Insights only sees what you capture. Every number comes from sessions run through the
paperctl CLI. If half the team runs agents outside ofpaperctl start, the dashboard undercounts by exactly that much — the fix is capture coverage, not the chart. - Deltas compare like with like. Each stat is measured against the previous period of the same length, so “up 24%” on the 7-day view means versus the 7 days before it.
- Averages are per-session. The small figures under each stat (average spend, average duration, average tool calls) describe a typical run, which is often more actionable than the total.
- Aggregates are entry points, not conclusions. The chart tells you Monday was expensive; only the session detail page tells you why. When a number surprises you, click through before drawing a conclusion.