Log noise sorted before
it wakes anyone up
A production incident generates thousands of log lines in minutes, and most of them are the same error repeated or noise that looks urgent but is not. We build an agent that reads the stream continuously, groups errors by actual root cause, and pages a human only when something genuinely needs one.
The process today
A single misbehaving service can produce thousands of nearly identical log lines in an hour, and the person on call has to figure out, at 2 a.m., whether that volume means one bug firing repeatedly or ten different problems happening at once. Observability vendors commonly report that on-call engineers spend a large share of incident response time just triaging, that is, deciding what an alert actually means, before they even start fixing anything. Alert fatigue is well documented: teams that page for everything train their own engineers to ignore pages, which is exactly backward from what alerting is supposed to do.
The usual fix, more alerting rules, makes the problem worse. Every new rule is another way to get paged for something that turns out to be benign, and nobody has time to go back and prune rules that stopped being useful months ago. The result is a team that either gets paged constantly for noise or turns down sensitivity so far that a real incident sits unnoticed for longer than it should.
What the agent does
Reads the log stream continuously from your existing observability stack, not a separate copy of it, so there is no lag between an error occurring and the agent seeing it.
Groups errors by root cause, matching stack traces and error patterns that look different on the surface but come from the same underlying bug, instead of treating every slightly different message as a separate incident.
Scores severity based on actual impact, distinguishing a spike in a background job’s retry logs from a spike in your checkout path, so the thing that pages someone at night is the thing that actually needs a person awake.
Writes a plain-language summary with every alert, what broke, how many times, and the likely cause based on the stack trace, so the on-call engineer starts debugging instead of starting by reading raw logs.
Routes to the right person or team automatically, based on which service or repository the error traces back to, instead of a generic page that gets forwarded twice before it reaches someone who can act.
Sends a daily digest of recurring low-severity issues that never rose to paging threshold, so a team lead can see the slow-burning problems that individual alerts would never have surfaced.
What stays with humans
The agent groups, scores and summarizes; it does not decide an incident is resolved. Any pattern it has not seen before, or anything touching data loss, payment processing or a security event, pages a human immediately regardless of how the grouping logic would otherwise score it. Changing what counts as “known noise” and safe to suppress is a decision your team makes and reviews, not one the agent makes on its own.
Guards
A shadow period before go-live where the agent groups and scores in a dashboard without actually paging anyone, so your team can check its judgment against real incidents before it touches the on-call rotation. Every grouping and severity decision is logged with the reasoning behind it, so a human can see why something was or was not escalated. Rate limits prevent alert storms from overwhelming the paging system even if the underlying error truly is firing thousands of times, and a kill switch reverts to your original alerting rules instantly if anything looks wrong.
Price and timeline
| Package | Price | Best for |
|---|---|---|
| Single automation | from $900 | One logging stack, grouping and routing alerts from your existing observability tools |
| Department package | from $2,500 | Log triage plus uptime monitoring, code review and release notes for the same infrastructure |
5 to 10 days, most of it spent learning your known-noise patterns during the shadow period so the agent is not paging for things your team already knows to ignore.
Related
Pairs directly with uptime and error monitoring, since the two usually share the same alerting pipeline, and with database reports when an incident needs a quick data check to confirm impact. Teams running code review often add log triage next to close the loop from a risky merge to what actually broke in production. Part of automation of everything digital and built the way we build AI agents for our own products. We run this kind of triage on our own infrastructure, including the secure Telegram mini-app infrastructure and the factory ERP recovery on self-hosted infrastructure.
Tell us which logging stack and services are in scope 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 log triage agent cost?
A single-stack agent wired into your existing logging and alerting tools starts from $900. A department package covering log triage alongside uptime monitoring, code review and release notes starts from $2,500, depending on log volume and how many services are in scope.
How long does it take to go live?
5 to 10 days: connecting to your log sources, learning what your known noise looks like so it stops paging for it, and a shadow period where the agent groups and scores errors without actually paging anyone, so your team can check its judgment first.
Which tools does it connect to?
Datadog, Sentry, ELK, CloudWatch, Grafana Loki and similar logging stacks, PagerDuty, Opsgenie or Telegram for alerting, your incident tracker, and Claude for reading stack traces and grouping errors by actual cause rather than surface text.
What if the agent misses a real incident?
It is tuned conservatively on known-critical patterns (data loss, payment failures, security events) that always page regardless of grouping, and every suppressed or grouped alert stays visible in a dashboard so a human can audit what got filtered and adjust the rules.
Is our log data safe?
The agent reads only the log sources you connect it to, runs under your own observability platform's permissions, and does not retain raw logs outside the triage session. Sensitive fields (tokens, personal data) are masked before the agent ever sees them.