Use case · AI Studio
Operations & DevOps
The operation that knows where it will break before the peak: monitoring, database check and narrowing down in minutes instead of hours.
01 · The brief
The two hours every operation knows.
Every business with a seasonal peak knows the same two hours: a metric tips over, reports pile up, and before anyone can repair anything, the question has to be answered first: did it come from the last release, are errors piling up, has a system become slow, or is it not our fault at all?
Answering that question costs experienced people hours. In peak season it costs revenue.
The brief: an operations agent team that continuously reads the state of application, servers and database, narrows down and quantifies anomalies and files the result as a ticket where the team already works. In our own software operations it has been running on the production system since March 2026.
02 · What the team does
What the team does.
Monitors, unattended
Server and application state, error logs, response times per endpoint, deployment history and usage data come together in one weekly evaluation. The decisive part is not the list of errors but the flag on every error for which nobody has yet created a ticket.
Watches the database too
A second evaluation checks the query statistics: which queries have become slow, where an index is missing, which tables are bloated or have not been cleaned up for too long. That is the class of problem nobody notices in summer and that tips the balance in peak season.
Narrows down, in minutes instead of hours
When something looks off, the agent checks in parallel what a person would have to check one after another: recent deployments, instance state, error rates, response times per endpoint, affected devices and OS versions. The result is not an alarm but a narrowed search space with evidence.
Quantifies instead of alarming
The agent does not report “error found” but what a finding weighs: how many users are affected, whether it is urgent or can wait. That is the difference between a notification and a decision paper.
Hands over to your people
Whatever comes out lands as a ticket in your ticket system, in your template, assigned and linked. No additional tool, no second inbox.
Brings in the neighbours
If a question crosses a team boundary, such as “what did this outage cost in revenue?”, the coordinator brings in the neighbouring team’s agents instead of someone calling a meeting.
03 · How it works
How the agents work.
Connected to hosting, log service, error monitoring, database and ticket system. All access read-only, except for writing tickets.
Step 01
Collect
Logs, error monitoring and usage data are pulled into one evaluation, weekly and immediately when needed.
Step 02
Narrow down
Anomalies are checked against releases, instances, endpoints and devices until the search space is small and substantiated.
Step 03
Quantify
Affected users, urgency and options are in the ticket, in order of suitability.
Step 04
Hand over
Assessment, prioritisation and implementation rest with the team. The agent delivers in minutes; the pace after that is set by your release rhythm.
Set this process up in your company? Request a conversation →
04 · From practice
Three cases from operations.
The error that only occurred on some days and vanished in the average.
An interface that delivers replies to users showed a markedly elevated error rate on individual days and zero on most. In the weekly average nothing was visible; there were no complaints. The agent found the pattern in the logs, linked it to a matching report from error monitoring and quantified what it meant. The cause lay deeper than its first assumption: an upstream service delivered the same message several times within a few milliseconds, creating parallel sessions with different access keys, the second of which was rejected. The fix has been shipped.
An attack, detected before it got expensive.
Automated third-party access to a public endpoint, rising to ten times the normal value in three days. The agent showed from the data that these were bots and not growth: countries outside the target group, fully reported as “organic”, and above all no conversion. It assessed the damage soberly and delivered three countermeasures in order of suitability, with the finished configuration lines for the recommended one at gateway level, without a new deployment being needed. Four days from ticket to done.
The case where the right answer was: don’t develop.
After the roll-out of a new app version, errors during checkout piled up. The obvious reflex: stop the roll-out. Instead the agent broke the affected users down by device and OS version and found that the large majority were running a pre-release version of the phone operating system that does not work with the platform provider’s payment library. No bug in the company’s own code. The recommendation was accordingly not “stop” but roll out fully. This case ended without a single line of development work. A team under time pressure would have stopped the roll-out and searched its own code for days.
05 · The outcome
What changes.
The question “what is causing this?” is answered before anyone starts repairing. In our own operations the weekly report has run since May 2026 without a gap and without human involvement.
Because the evaluation takes an hour instead of a week, it pays to do it before anyone develops. And because agent teams err as employees err, usually in the interpretation of numbers, a separate instance cross-checks every result. Whatever stands out is recorded as a fixed rule.
- Weekly report from logs, error monitoring and usage data, with no staff involved.
- Database state in view before peak season makes it visible.
- A narrowed search space with evidence instead of an alarm.
- Tickets in your system, decisions with your team.
06 · Who decides what
Who decides what.
find the anomaly → narrow down the cause → quantify the effect → create ticket with evidence and proposal → assess and prioritise → implement → approve → test → measure the effect
The highlighted steps rest with people, set through the granted permissions. The operations agent does not deploy (release and configuration variables of production are locked), does not write production code (its access to the code repository is at the lowest writing level: tickets and comments), does not prioritise and does not decide on money or staff.
Every agent action is logged with a timestamp. Every machine-generated ticket carries a header line naming the person who triggered it; the tool enforces this, a ticket without this attribution cannot be created.
07 · Systems and access levels
Systems and access levels.
Our own operations set-up as an example. In your company, your corresponding system would take each place. The right-hand column is the whole lever, and it can be dialled back just as easily.
| System | Used for | Access level |
|---|---|---|
| Application hosting | Application and instance state, deployments | read only · releases and configuration explicitly locked |
| Log service | Request logs, response times, error rates | read only |
| Error monitoring | New errors, frequency, affected users | read only |
| Database | Query statistics, indexes, table state | read only |
| Code and ticket system | History, tickets, deployments | read · write tickets and comments · no code |
| Session recording | Drop-offs, devices, OS versions | read only |
| Chat / messenger | Reports, warnings, summaries | read · send only in approved channels, otherwise draft for approval |
08 · Limits
Limits, and what changes over time.
An agent is only as good as its data sources. It recognises an error in the data, but not readily its absence: if a source silently stops delivering, a report appears that looks normal and is nevertheless empty. Whoever introduces monitoring has to monitor the monitoring. That is why a completeness check is part of it from day one.
The agent does not set the pace. It delivers its part in minutes. Until that becomes a shipped change, the time that assessment, approval and release rhythm need goes by. The gain is at the start of the chain, not at the end.
The first weeks are the most expensive. At the beginning the team knows neither the quirks of the data nor the unspoken conventions of the operation: which spikes are normal, which systems are maintained when, which finding concerns whom. Every correction is recorded as a rule and applies from then on. Whoever evaluates after two weeks sees the learning curve, not the result.
Frequently asked
Frequently asked questions about this use case.
Does this replace our monitoring?
No. Your monitoring stays where it is. The agent team reads the existing sources together, connects them and answers the question dashboards are not built for: what is causing it and what it weighs.
Can the agent intervene itself?
Only as far as you set the access level. In our own operations it may read, write tickets and comments and report in approved channels. Deployments, configuration and production code are locked.
Which operations is this suited to?
Software and platform operations with their own production system, a ticket system and at least one log or error service. The better history and responsibilities are documented in writing, the faster the team becomes useful.
More use cases
Related use cases.
End-of-life mails to tickets
The email everyone receives and nobody handles: vendor end-of-life notices become verified tickets in your own ticket system, with deadline, priority and owner.
Marketing
Performance marketing end to end, analysis, creation and rollout in one system, with a human at the gate.
Energy management
Spot load peaks, understand consumption, control costs, before they show up on the bill.
Does this sound familiar?
In a first conversation we look at your processes together and sketch out where an agent team would make the biggest difference. Free of charge and without obligation.