See what the AI actually ran. Decide what to do next.
You are running AI inside real workflows — support, finance, supply, sales. Trefur shows you what each agent ran, what it cost, and where it stopped. In plain English, without a tracing tutorial.
What you are trying to do.
- →See what the AI actually performed during a customer interaction, not what the salesperson promised it would do.
- →Catch agents quietly stuck on tasks that should be done by now.
- →Tell finance which workflow is driving model spend and which ones broke even.
- →Hand engineering a precise repro when an agent answers wrong, instead of forwarding a screenshot.
- →Decide which workflows to expand AI into next, based on outcomes you can measure.
What hurts today.
You hear about failures from customers
The first time you learn the support agent gave a wrong refund answer is when the customer escalates. By then the bot has been wrong fifty times.
Engineering is the only group that can answer the question
Finance asks why model spend jumped. You ask engineering. They open three tools and come back two days later. The CFO has moved on.
You cannot tell which workflows are working
The agent handles 60% of tickets, supposedly. Without seeing the runs, you cannot tell if the other 40% are silently failing or just being routed.
Roadmap decisions are vibes
Do we expand AI into finance? Into supply? Into HR? You have no data on outcomes per workflow today — only invoices.
What Trefur gives you.
A plain-English view of what the agent ran
Open any run, see the steps in order: looked up the order, drafted the reply, sent the email. No JSON, no stack traces, no PhD.
Cost per workflow
Roll up cost by agent, by team, by environment. Hand finance a chart that answers the actual question.
Outcomes you can count
Tag runs with the business outcome — refund issued, ticket resolved, lead qualified. Trefur counts them per agent, per day.
Catch quiet stalls
Alerts fire when an agent's success rate, latency, or cost-per-run drifts outside its normal envelope. You hear about it before customers do.
Search by the thing you have
Customer email, support ticket ID, time window. Find the run, share the link with engineering, move on.
Stays out of the way of customers
Trefur captures spans behind the scenes. The customer experience is unchanged. Latency is unchanged. The agent runs the same way.
A week in the life with Trefur.
- Mon
A customer escalation lands in your inbox. You paste the support ticket ID into Trefur, see the agent picked the wrong refund rule. You forward the trace to engineering with a one-line summary.
- Tue
Weekly metrics review. You pull up the agent dashboard: 4,210 runs last week, 88% resolved without human, average cost $0.06, p95 latency 1.4 seconds. Numbers, not anecdotes.
- Wed
Finance asks who is driving the OpenAI spend. You filter by team in Trefur, send a screenshot of the breakdown by end of meeting.
- Thu
You propose expanding the assistant into onboarding. You cite the trace data: error rate trending down, cost-per-run flat, customer-rated outcomes positive. The CFO approves.
- Fri
An alert fires: the refund agent's success rate dipped 6% overnight. You loop in engineering with the failing traces already attached. Fixed before customers notice.
Stop asking engineering. Start seeing for yourself.
Free tier to evaluate. Team and Enterprise add retention, scheduled reports, and SSO.