I Taught an AI to Be Our On-Call Engineer
The article discusses the development of an AI tool named Scooby, designed to assist on-call engineers in troubleshooting alerts. The author shares their experience of teaching the AI to understand their company's specific infrastructure and context. By leveraging historical alert data, Scooby aims to provide more relevant and accurate responses to incidents.
- ▪Scooby is an AI-powered tool created to help on-call engineers manage alerts more efficiently.
- ▪The author initially tried an existing tool but found it lacking in contextual understanding of their infrastructure.
- ▪Scooby was trained using historical alert data to improve its ability to diagnose issues specific to the company's environment.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,408 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Medium |
| Canonical URL | https://medium.com/pipedrive-engineering/scooby-how-i-taught-an-ai-to-be-our-on-call-engineer-163e3b3662ab |
| Publication time | Thu, 21 May 2026 09:38:33 +0000 |
| Retrieval time | 2026-05-21T09:46:10.467Z |
| Last seen | 2026-05-21T09:46:10.467Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | GpqyJNiChKiZ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Scooby: How I Taught an AI to Be Our On-Call EngineerAleksandr Smirnov15 min read·Just now--ListenSharePress enter or click to view image in full sizeThere’s a specific kind of frustration that every on-call engineer knows. It’s 11 pm, an alert fires, and you open five dashboards in a panic. You’re pasting log queries from memory, squinting at a Grafana panel that may or may not be the right one, or trying to find the relevant playbook somewhere in Confluence. You know the answer is somewhere in there.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
Discussion
0 commentsMore from Medium
