Downtime caught before
a customer has to report it
Most teams find out about an outage from a customer complaint, not from their own monitoring, because the monitoring either stayed quiet or fired so often that nobody was watching when it mattered. We build an agent that watches uptime, error rate and latency continuously and alerts with a likely cause already attached.
The process today
A service degrading slowly, rising latency, a creeping error rate, often does not look like an outage until it already is one, because generic monitoring thresholds set once at launch stop reflecting what normal actually looks like for that service six months later. Industry incident reports commonly cite detection time, not fix time, as the largest chunk of total outage duration: teams that eventually fix an issue in minutes often took much longer to even notice it was happening, especially outside business hours.
The opposite failure is just as common. A team sets thresholds tight enough to catch everything and ends up paged for every traffic spike, every slow third-party API call, every deploy’s momentary blip, until the alerts get muted or ignored entirely. Either way, the first real signal a team gets is often a support ticket or a tweet, which means the business already absorbed the damage before anyone on the inside knew.
What the agent does
Watches uptime, error rate and latency continuously across every site and API you connect, not on a fixed polling interval that misses what happens between checks.
Builds a real baseline per service, learning what normal traffic, normal error rate and normal latency actually look like for that specific service at that time of day, instead of applying one generic threshold everywhere.
Attaches a likely cause to every alert, correlating the spike with recent deploys, infrastructure changes or known third-party outages, so the first message a human sees already points at where to look.
Correlates automatically with your deploy history, flagging when an error spike started within minutes of a release going out, which is the single most common root cause and the easiest one to miss under pressure.
Drafts status page updates for customer-facing outages, written in plain language and held for a human to approve and publish rather than posted automatically.
Sends a weekly reliability summary with trend lines, so slow degradation that never crossed an alert threshold still shows up before it becomes an incident.
What stays with humans
The agent detects and explains, it does not decide an incident is over or communicate with customers unsupervised. Any public status page update or customer-facing communication is drafted by the agent and published only after a human approves it. Infrastructure changes to actually fix the root cause, scaling, rollback, failover, stay with your engineers; the agent’s job ends at a clear alert with a likely cause attached.
Guards
A tuning period where the agent watches and would-have-alerted without actually paging, so your team can compare its judgment against real traffic before thresholds go live. Every alert logs the exact metric, baseline and deploy correlation behind it, so nothing is a guess dressed up as certainty. Rate limits prevent alert storms during a genuine widespread outage from flooding the paging system, and a kill switch reverts to your previous monitoring setup instantly.
Price and timeline
| Package | Price | Best for |
|---|---|---|
| Single automation | from $700 | One stack, watching uptime, error rate and latency with alerts tuned to real baselines |
| Department package | from $2,500 | Uptime monitoring plus log triage, code review and release notes for the same infrastructure |
4 to 8 days, most of it spent establishing a real traffic baseline per service so alerts are tuned before they ever reach a human.
Related
Shares its alerting pipeline with log triage and alerting, and feeds database reports when an incident needs a quick data check to confirm customer impact. Teams running API integration glue often add uptime monitoring to watch the integrations themselves. Part of automation of everything digital and built the way we build AI agents for our own products. We run this on our own live systems, including our own marketplace ProBay with an AI agent team and the secure Telegram mini-app infrastructure.
Tell us which services and regions you want watched and we will send back a fixed price and a plan for the first week: get in touch.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does an AI uptime monitoring agent cost?
A single-stack agent watching your existing services starts from $700. A department package covering uptime monitoring alongside log triage, code review and release notes starts from $2,500, depending on how many services and regions are in scope.
How long does it take to go live?
4 to 8 days: connecting to your services and existing monitoring tools, establishing a normal-traffic baseline for each one, and a short tuning period before alert thresholds are trusted enough to page someone directly.
Which tools does it connect to?
UptimeRobot, Pingdom, Datadog, Grafana and similar monitoring platforms, your CI/CD pipeline to correlate incidents with recent deploys, PagerDuty, Opsgenie or Telegram for alerting, and Claude for turning a metric spike into a plain-language likely cause.
What if the agent pages for a false alarm?
Thresholds are tuned against your own service's real traffic patterns, not a generic default, and every alert names the metric and baseline it tripped so a human can dismiss a false positive in seconds and feed that back into tuning.
Is our infrastructure data safe?
The agent only reads the metrics and logs from the services you connect it to, under your own monitoring platform's permissions, and does not retain data outside the monitoring session. It has no write access to your infrastructure beyond sending alerts.