◆ Elasticsearch

Build custom agents for data processing

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts
See the prompt↓All Elasticsearch tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskBuild a custom agent that monitors the 'web_server_logs' index for HTTP 5xx errors. When the…+
Build a custom agent that monitors the 'web_server_logs' index for HTTP 5xx errors. When the rate exceeds 10 errors per minute, send an alert to the 'devops_alerts' Slack channel with the error count and a link to the relevant logs.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore deploying this 5xx error agent to production, let's make it more intelligent. Can you…+
Before deploying this 5xx error agent to production, let's make it more intelligent. Can you add a feature to detect if the 5xx errors are coming from a specific endpoint or user agent, and include that detail in the Slack alert? This would help the on-call team diagnose issues faster.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe anomaly detection agent I built for our 'transaction_data' index is flagging minor…+
Our custom anomaly detection agent is generating too many false positives, and the on-call team is starting to ignore its alerts.
The anomaly detection agent I built for our 'transaction_data' index is flagging minor fluctuations as critical, leading to a flood of false alerts. The on-call team is complaining and starting to ignore actual incidents. I'm afraid of missing a real problem, but I can't tell if the threshold is too sensitive, or if the baseline model needs retraining. What's the most likely reason for these false positives, and what's the best next step to recalibrate the agent without silencing real issues?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternMy custom data processing agents often perform well in development, but then struggle when…+
I keep building custom agents that work in testing but struggle with real-world noise and complexity.
My custom data processing agents often perform well in development, but then struggle when deployed to production, overwhelmed by unexpected data variations or edge cases. This leads to constant tweaking and re-deployments, eroding my team's trust in my solutions. What habit should I change in my agent development process to build more resilient and adaptable agents that handle real-world data gracefully from the start?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Elasticsearch's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.