Solution · AI Operations
Monitor AI in production: the function that watches your agents live — latency, cost, failures and drift — and alerts you before the customer
An agent in production doesn't fail with a red error: it fails in silence — slows down, spikes the cost per token, starts returning garbage when a tool changes — and without observability you don't find out until someone complains. Monitoring AI isn't a pretty dashboard nobody looks at; it's the function that instruments, watches and alerts: traces of every call, live metrics, alerts when something goes off-script and whose phone rings at 3am.
The problem
An agent in production breaks in silence, and without observability the first alert is a customer complaint
- The agent slows down or starts returning odd answers because an API changed or the model updated, and nobody sees it: there are no traces or live metrics, just the raw log nobody opens until there's an incident.
- The cost per token spikes on a Tuesday with nobody noticing until the provider's bill arrives at month-end, multiplied, with no idea which flow set it off.
- When something really fails, there's no on-call and no runbook: it's discovered by an angry customer, hunted by hand for which step broke, and it takes hours to understand what happened because no trace was left.
- The only signal the system is unwell is complaints, which arrive late and only from the tip; slow drift — quality dropping a little each week — goes unseen by everyone until it's already a big problem.
Cost of staying the same
An agent in production without observability isn't an operated system, it's a black box that bills and sometimes fails. The cost isn't just the one incident: it's the time to find it blind once it's already blown up, the token bill spiking with nobody watching, and the drift eating quality so slowly that by the time you react you've lost trust — and customers. When the only metric is the complaint, every failure is paid by someone outside before a panel inside, and scaling to more agents on a base you can't see multiplies the surface of what can break without warning.
The solution
We instrument and operate your agents' observability — traces, live metrics, alerts, on-call and an incident runbook — so you stop finding out about failures from a customer and start seeing them coming on a dashboard
- 1We instrument each agent: end-to-end traces of every call — what came in, which model and tools were used, what came out and how long and how much it took — so you stop having a black box and start seeing what happens inside at each step.
- 2We set up the metrics that matter, live: latency, cost per case and per token, error rate, usage and failure of each tool, cases going off-script; not a decorative dashboard, but the numbers that say whether the system is healthy right now.
- 3We put alerts and on-call in place: thresholds that fire a warning when latency, cost or failures go out of range — even if nobody has complained — with whose phone rings and a runbook of what to do, so the incident is caught in minutes and not in the month-end bill.
- 4We watch drift and close the loop with incidents: we detect the slow quality decline and the failure patterns, leave a post-mortem of each incident and feed the improvements. All measured: p95 latency, cost per case, error rate, time to detect and time to resolve.
What changes
What you stop losing
The failure stops being discovered by a customer: traces and alerts surface it live, when latency, cost or errors go out of range, not when someone complains.
Mechanism
The cost per token stops being a month-end surprise: it's watched per case continuously, so the spike shows on the day it happens and you know which flow set it off.
Mechanism
The incident is caught in minutes, not hours: with traces, on-call and a runbook, you know which step broke without rebuilding it blind.
Mechanism
What we measure: p95 latency, cost per case, error rate, time to detect and time to resolve an incident.
What we measure
Spec sheet
- Work it removes
- having your AI agents run blind in production — no traces, no live metrics, no alerts or on-call — so the first sign something's wrong is a customer complaint or the end-of-month token bill
- Typical setup
- 2–4 weeks
- Input
- the real calls of your agents in production, their latency, their cost, their tool failures and their slow drift, today buried in a raw log nobody opens until there's an incident
- Output
- every agent instrumented with live traces and metrics, alerts that fire before the complaint, on-call with an incident runbook and drift watched, so you see it coming on a dashboard instead of discovering it in a customer
- Works with
- OpenAIAnthropicAzure OpenAIAmazon BedrockGoogle Vertex AI
- Can connect to
- Your agents and their production logsYour observability stack (LangSmith, Langfuse, Datadog, Grafana)Your alert channel and your on-call
- What we measure
- p95 latencycost per caseerror ratetime to detect and time to resolve
- Good fit for
- companies with one or more AI agents in production running today without live monitoring that need to operate them — see them, alert on them and respond to incidents — before scaling
- Not a fit for
- anyone still in a prototype with no real traffic to watch, or looking to measure answer quality (rubric and evals) rather than the operational health of the system — that's another, complementary function
Frequently asked questions
They're cousins, not twins, and they complement each other. Evaluating quality measures whether the answer is good — against a rubric: accuracy, tone, policies. Monitoring measures whether the system is healthy live — latency, cost, tool failures, incidents — and alerts you when it goes out of range. An agent can give correct answers and still be expensive, slow or about to fall over; and it can be fast and cheap while returning garbage. You need both: quality scores the what, observability watches how it's running.
With the one you already have. We instrument on top of your agents and their logs and lean on standard observability tools — LangSmith, Langfuse, Datadog, Grafana and the like — whatever your model provider. We don't ask you to rewrite the agent or migrate platforms: we build the trace, metrics and alert layer on top of what already runs in production.
That's agreed before anything is switched on. We define the on-call — who gets the alert, on which channel and with what runbook — depending on how you want to operate: we run it as an external function, your team runs it with the runbook we leave, or a hybrid. What we won't leave you with is an alert that fires and rings for nobody: without on-call, monitoring is just one more dashboard nobody looks at.
Yes, because the silent failure doesn't wait until you have a hundred. With one agent you're already exposed: a cost spike, a tool that changes and breaks the flow, a drift that lowers quality quietly. Observability is exactly what lets you scale with a net: going from one to several agents on a base you can see and alerts that fire, instead of multiplying black boxes. The sooner you build it, the fewer incidents you pay for with customers.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.