DEVOPS
AI Root-Cause Agent for Cache Regressions with Rollback MR
When cache hit ratio regresses, an agent investigates across Cloudflare analytics, Datadog metrics, and recent GitLab history to write a root-cause narrative and open a targeted…
How it runs
The automated pipeline, trigger to output.
- TriggerCache regression alert received
- ActionPull per-rule cache stats (Cloudflare)Cloudflare
- ActionCorrelate request/latency series (Datadog)Datadog
- LogicAgent reasons over GitLab commit timeline for best-fit causeGitLab
- ActionOpen scoped rollback MR for offending ruleGitLab
- OutputPost root-cause narrative + MR to SlackSlack
What it does
This is the agent-driven version of the sentinel. On a cache hit-ratio regression, an investigative agent gathers evidence from multiple systems — Cloudflare's per-rule cache stats, Datadog's request and latency series, and the GitLab commit timeline — then reasons about which change most plausibly caused the drop. It writes a human-readable root-cause analysis and opens a rollback MR scoped to just the offending rule, not a blanket revert.
When to use it
Use it when regressions aren't always traceable to the single newest commit — overlapping config edits, gradual TTL drift, or interaction effects — and you want a reasoned diagnosis rather than a mechanical revert of HEAD.
How it works
- 1A regression alert (schedule or upstream monitor) triggers the agent.
- 2The agent pulls per-rule cache stats from Cloudflare.
- 3It correlates with Datadog request/latency series over the same window.
- 4It walks recent GitLab commits to find the change that best explains the drop.
- 5The agent opens a scoped rollback MR reverting only the offending rule.
- 6It posts the root-cause narrative and MR link to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
- 2Connect DatadogMetrics, traces, log search.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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