AI agent monitoring

AI agent monitoring.

Dashboards, envelope alerts, SLO tracking, and oncall routing for production AI agents. So retry-rate spikes, cost drift, latency walks, and error-rate jumps page humans before customers do.

What AI agent monitoring means.

AI agent monitoring is the practice of running dashboards, alerts, SLO tracking, and oncall routing on top of AI agent telemetry. It is the next layer above AI agent observability — once you have captured the trace, monitoring is what tells you when something is going wrong, who to page, and what to do about it.

Agents fail differently from services. The model retries silently. A tool returns the wrong shape and the agent loops. Latency stays in spec but cost-per-run doubles overnight. The alerting envelope has to know about agent-specific failure modes, not just HTTP status codes.

Trefur ships the alerting envelopes engineers actually need: retry-rate spikes, cost-per-run drift, p95 latency walks, error-rate envelopes per agent. Every alert is routed to where you already operate — Slack, PagerDuty, Teams, webhook, email — with the worst trace in the window already attached.

Why AI agent monitoring matters.

Five outcomes when monitoring is turned on.

Page humans on the right signal

Envelope alerts on retry rate, cost-per-run, latency walks, error-rate envelopes — not raw token counts. Fewer pages, more signal.

Open the trace pre-triaged

Every alert links the worst trace in the window so oncall opens it with the failing span already highlighted.

Track agent-level SLOs

Success rate, retry budget, p95 latency, cost-per-run — per agent, per environment, per build SHA.

Route to the right channel

Slack, PagerDuty, Teams, webhook, email. Per agent, per environment, per severity. Quiet hours and dedup windows.

Compare deploys before they roll

Hold a staging build against production on the same input set. See which spans diverge. Promote with confidence.

Get started in five minutes.

Drop the SDK in. Alerts route to Slack out of the box; add PagerDuty or Teams in the dashboard.

go · go get github.com/trefur-ai/trefur-sdks/go/observe-go
import observe "github.com/trefur-ai/trefur-sdks/go/observe-go"

client := observe.Init(observe.Config{APIKey: "trf_obs_..."})
defer client.Shutdown()

// Existing agent code — auto-instrumented.
// Envelope alerts learn each agent's normal envelope automatically.
// Drift triggers a page to your routing of choice with the worst trace attached.

Monitor your AI agents today.

Free tier. No card required. First alert envelope learnt within a day of traffic.