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Use case

AI Workflows for Customer Onboarding Documents

Every new enterprise customer gets an onboarding guide that is 80% boilerplate and 20% specifics — their integrations, their SSO setup, their environment names. Producing that 20% by hand means each kickoff eats a CS engineer's afternoon, and the boilerplate 80% drifts out of date separately in every copy.

Templates don't fix it: the copies still fork, the product still changes under them, and nobody owns reconciling either.

How it works with an agent fleet

A Fleet workflow fans out over per-customer config files and renders each guide from the single current source of truth — then a CS lead approves before each guide is shared.

Step Kind What it does
guide Document Runs once per file in customers/*.md. “Write the onboarding guide for this customer file: their integrations, SSO provider, and environments, against current product docs.” Reads docs/product/**/*.md, customers/*.md.
accuracy Review “Check the guide against current product docs. Flag stale steps or features that changed.”
cs-approval Approval gate Pauses the run for a human sign-off; records the approver, timestamp, and note.
share Publish Shares the approved document at a link.

You build this once on the dashboard canvas; your self-hosted Fleet worker pulls the definition and runs it against your repository.

When the product changes, re-run: incremental rebuild regenerates only what the change touches, and every customer's guide is consistent with the docs again — no per-copy reconciliation.

The fleet pattern

Fan-out over customer files → per-customer guide from shared product corpus → accuracy review → CS approval → published share link. One source of truth, N rendered guides, zero forks.

Guardrails that matter here

  • Each guide passes its own approval gate — a person confirms each customer-facing document before its link exists
  • The accuracy review compares against current product docs, catching the 'guide says the old settings path' failure mode
  • Published links serve the approved revision; an updated guide is a new approval, not a silent edit

Who this is for

CS and solutions teams onboarding customers with per-customer configuration, where guides are valuable enough to personalize and numerous enough that hand-maintenance fails.

Frequently asked questions

Where does per-customer data live?

As files in your repo — one markdown file per customer with their integrations, providers, and environment details. The fan-out runs the draft step once per customer file. Sensitive values stay out; the guide references names, not secrets.

What happens when the product changes?

Re-run the workflow. Step fingerprinting skips unchanged work, so only guides affected by the docs change regenerate — each through its own review and approval again.

Run your first agent fleet

One binary. Five minutes. See every agent, coordinate every handoff, and keep a full audit trail of what your fleet did.