Built for the teams shipping AI.
Agent observability looks different depending on the chair you sit in. Engineering wants the trace tree. SRE wants the alert envelope. Ops wants the plain-English run. Finance wants the cost roll-up.
Trefur is one product that lets every team see what they need to see — without forcing anyone else to change framework, vendor, or backend.
What every team gets, regardless of where they sit.
Pick a chair below for the team-specific story. But these four outcomes are common to all of them — and they're the reason Trefur exists.
One trace shape, every team can read
Engineers, SRE, ops, finance all open the same trace and see what makes sense for them — the span tree, the alert envelope, the cost roll-up, the plain-English run. No tab-switching, no exporting CSVs.
Adopt without re-platforming
Drop the SDK in the runtime you already ship. OpenTelemetry-native, framework-agnostic, vendor-agnostic. Adding Trefur doesn't make your stack heavier — it makes it legible.
Catch failures before the customer does
Alert envelopes on retry rate, cost-per-run, latency, and error-rate. Route to the right oncall channel with the trace already attached. Stop learning about agent regressions from support tickets.
Make model-vendor decisions on data
Compare OpenAI, Anthropic, Bedrock, Azure OpenAI, Gemini on the actual workload your team runs — cost, latency, success rate. Swap vendors without rebuilding dashboards.
Pick the chair you sit in.
Each page is written for that team, in the words that team uses — with concrete pains, concrete capabilities, and a day-in-the-life of what working with Trefur looks like.
AI engineers
You're shipping production agents. You need to see every decision, every tool call, every retry — and you need to see it in the runtime you already use.
Platform & infra teams
Many teams, many agents, many models. You need one trace format, one alerting story, and one pane of glass — without forcing every team onto the same framework.
SRE / oncall
Agents page differently than services. You need alerts that route to the right oncall, with the trace already pulled up, not a wall of LLM noise.
Governance & safe AI
AI is showing up in your tools faster than anyone can track. You need an inventory of every agent and copilot — including the ones nobody onboarded — plus audit-ready evidence and a way to catch unsafe behavior early.
Business & ops
You're running AI inside real workflows — support, finance, supply, sales. You need to know what the agent actually executed, what it cost, and where it stopped.
Startups
You're building AI-native. Your traces are your customer support, your QA, and your roadmap signal. You need them on day one, not after the first outage.
Enterprise
Many runtimes, many vendors, many regions. You need one observability layer that reads what every team already emits, without re-platforming any of them.
One incident, four chairs.
A refund bot picked the wrong rule and issued a small wrong refund. Here's how the four teams above touch the same data over a Tuesday — and how Trefur is the shared substrate underneath.
A customer escalation lands
Paste the support ticket ID into Trefur. Find the run. See the agent picked the wrong refund rule on step 3. Forward the trace to engineering with one line.
Engineering opens the trace
Spans show the tool returned 409 conflict, the model retried with the same args, and the third attempt succeeded with the wrong account. The bug is obvious. Fix takes 20 minutes.
SRE catches the rest before tickets land
Alert envelope on the refund-bot retry rate has been firing since 08:00. The trace is already attached. SRE pauses the deploy, the fix lands at noon.
Finance asks why the bill jumped
Ops pulls the cost breakdown by agent and deployment. The new prompt template added 40% more tokens per call. The answer is in the dashboard, not a two-day investigation.
What makes this work.
The same six capabilities power every team-specific use case above.
Not sure which chair is you?
Start free. The first trace lands in under five minutes. You can decide what shape your team is once you can see what your agents are actually doing.