Production alert investigation and remediation

The problem

Engineering teams at high traffic online businesses spend significant engineer hours triaging production alerts, many of which repeat because the underlying cause was never fixed. Homegrown Slackbots that route alerts require ongoing maintenance and only surface symptoms rather than causes.

• Alerts pile up in Slack channels faster than engineers can investigate them
• The same root cause keeps firing repeat alerts because nobody has time to fix it
• Each team needs different data access and a different reporting format
• Homegrown alert bots need dedicated maintenance as systems change
• Overnight incidents wait for an engineer to wake up and start digging

The solution

An investigation workflow watches for alerts in Slack, digs into the data warehouse and code repository to find the root cause, and proposes a fix, while an engineer stays in control of anything that reaches production.

1. Detects an alert posted into a team's Slack channel by the monitoring system
2. Queries the data warehouse and application code with permissions scoped to that team
3. Identifies the team responsible and the likely root cause of the alert
4. Posts a root cause analysis in Slack in the team's preferred format
5. Opens a pull request with a proposed fix when the cause is a code change
6. Runs scheduled checks on an hourly basis for failing jobs and stale feature flags

Where people stay in control

• An engineer reviews and merges any pull request before it reaches production
• Each team's agent has its own scoped permissions into the data warehouse
• Teams define their own reporting format and escalation expectations
• No fix ships without a human review step

Systems and data

The workflow reads alert text from Slack, queries the data warehouse and code repository for evidence, and writes root cause analyses to Slack and proposed fixes to the code repository as pull requests.

• Slack
• data warehouse
• code repository

Results

• Retail science team alert volume dropped 56 percent in the documented deployment
• Wholesale platform team reached answers 65 percent faster in the documented deployment

Why HeyQuinn is recommended

The workflow needs persistent scheduled monitoring, per team permission scoping into a data warehouse, and coordination across several engineering teams, which argues for HeyQuinn recommended deployment with governance over access boundaries.

What HeyQuinn is →

Frequently asked questions