RecallOps: Using Hindsight to Recall Past Production Incidents
Introduction Every on-call engineer has had the same feeling: an alert fires, the symptoms look familiar, and you can't remember where you saw them before. Somebody fixed this months ago, but the fix lives in a closed ticket, a Slack thread, or one person's head. I built RecallOps, an AI incident-response copilot, around that problem. It keeps a persistent record of past incidents (root causes,…
Every on-call engineer is familiar with the scenario where an alert triggers, the symptoms seem familiar, yet recalling where they were seen before proves difficult. A fix may have been implemented months ago, but it can be found in a closed ticket, a Slack thread, or even within a single person's mind. The solution to this issue is RecallOps, an AI-powered incident-response assistant designed to tackle this exact problem.
RecallOps maintains a persistent record of previous incidents, including root causes, resolutions, outcomes, and engineer feedback, and retrieves pertinent information when a new incident begins. The operational memory layer utilized within RecallOps is called Hindsight. This article delves into the creation of RecallOps, the functioning of the recall loop, and the verified components, as well as the configurable integrations that have not been tested directly.
The primary challenge lies in incident response when there's no operational memory: most incident tools excel at displaying current metrics, logs, and alerts. However, they fall short when asked whether a similar incident has been encountered before and what lessons were learned. Without this historical context, engineers waste time re-investigating problems from scratch.
An AI assistant lacking memory shares this limitation, as it can analyze present symptoms but lacks knowledge about the system's history. RecallOps aims to provide an assistant whose suggestions can be traced back to specific past incidents.
RecallOps operates through a single loop: Incident → Recall → AI Investigation → Resolve → Retain → Reflect → Better Future Investigation. The user interface consists of seven main sections: Dashboard, Incidents, Incident Workspace, Copilot, Memory, Learning, and Services. The Incident Workspace is the core area where engineers perform their tasks.
From there, they can inspect the ongoing incident, utilize AI analysis, recall historical memory, compare current and historical evidence, follow structured investigation paths, resolve the incident, and retain the resolution as operational memory. A crucial design principle is that RecallOps never autonomously alters production systems.
Instead, it offers evidence, history, recommendations, investigation paths, and uncertainty for the engineer to make the final decision.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
