DATA OPS
Agent-authored migration impact review from schema drift to Confluence
A CEO-driven agent analyzes the staging-vs-prod schema diff, reasons about downstream impact and rollback risk, drafts a human-readable migration impact review.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled run
- ActionRead staging and prod schemasPostgres
- LogicAgent reasons over diff for impact and rollback risk
- ActionPublish impact review to ConfluenceConfluence
- OutputOpen GitHub sign-off issue linking the pageGitHub
What it does
This agent-driven workflow goes beyond a raw diff. After detecting drift between staging and production, an agent reviews each changed object, infers the likely application impact (dropped columns still referenced, new NOT NULL without a default, index changes affecting query plans), assesses rollback risk, and writes a structured migration impact review. It publishes that review to Confluence and files a GitHub issue linking to it for sign-off.
When to use it
Use it for higher-stakes migrations where a bare diff isn't enough and you want a written, reasoned assessment before approval — without a senior engineer hand-writing the doc every time.
How it works
- 1A scheduled or manual run kicks off the review.
- 2Pull staging and production schema catalogs from Postgres.
- 3The agent diffs them and reasons over each change for impact and rollback risk.
- 4The agent drafts a structured migration impact review document.
- 5Publish the document to a Confluence space.
- 6Open a GitHub issue linking the Confluence page and tagging owners for sign-off.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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