AI & RAG
Post-Deploy Metric Sanity Check Against Baselines
After a GitHub deployment, the bot checks the service's key Datadog metrics against pre-deploy baselines, consults the runbook for what 'healthy' looks like.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub deployment completesGitHub
- ActionPull post-deploy golden-signal metrics and pre-deploy baseline from DatadogDatadog
- ActionLook up the service's documented healthy ranges in ConfluenceConfluence
- LogicDecide go or no-go by comparing post-deploy values to baseline and runbook bounds
- ActionWrite the go/no-go verdict and recommended action (OpenAI)OpenAI
- OutputPost the post-deploy sanity verdict with metric deltas and citations to SlackSlack
What it does
When a deployment completes, this workflow runs an automatic post-deploy health check. It compares the service's golden-signal metrics in Datadog against their pre-deploy baselines, reads the Confluence runbook to understand documented healthy ranges, and posts a clear go/no-go verdict to Slack. Instead of staring at dashboards after every release, the team gets a cited summary: which metrics moved, by how much, and whether that's within expected post-deploy variance.
When to use it
Use it as a deployment gate or a confidence check on continuous delivery, especially for services where regressions show up as latency or error-rate creep rather than hard crashes.
How it works
- 1A GitHub deployment-completed event triggers the flow with the service and release info.
- 2Datadog returns the service's golden-signal metrics for a short post-deploy window plus the pre-deploy baseline.
- 3Confluence is searched for the service runbook's documented healthy ranges.
- 4A logic step decides go or no-go by comparing post-deploy values against baseline and runbook bounds.
- 5OpenAI writes a verdict summarizing the comparison and the recommended action.
- 6The sanity-check verdict is posted to Slack with per-metric deltas and runbook citations.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect DatadogMetrics, traces, log search.
- 3Connect ConfluenceSpaces, pages, blueprints.
- 4Connect OpenAIModels, embeddings, files.
- 5Connect SlackChannels, DMs, threads, mentions.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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