Use case

AI Workflows for Board Reporting

Name: Fleet
Author: Fleet

A board update is high-stakes recurring knowledge work: the same document every month, assembled from sources that change constantly — metrics exports, OKR docs, hiring plans, runway models. Someone senior spends a day collecting numbers and someone more senior spends an evening checking them, because a wrong revenue figure in a board deck is not a typo, it is a credibility problem.

The failure mode of using a chatbot for this is well known: the draft reads fluently and gets one number wrong, and nobody catches it because nobody is assigned to catch it. The problem is not drafting. It is governance — making review and sign-off a structural step instead of a favor.

How it works with an agent fleet

Fleet's workflow engine runs the report as a governed pipeline: a draft step reads your actual source documents (the corpus), an independent review step checks every claim against those same sources, and an approval gate pauses the run until a named human signs off on the exact document.

# .fleet/config.yaml
genflows:
  - name: board-report
    schedule: "0 9 1 * *"   # 9am on the 1st, monthly
    steps:
      - {name: draft, prompt: "Write the monthly board update. Every number must come from the corpus.", corpus: ["metrics/**/*.md", "okrs/*.md"], kind: report, out: board-update.md}
      - {name: fact-check, prompt: "Check every claim in the draft against the corpus. Flag anything unsupported.", depends_on: [draft], kind: review, out: review.md}
      - {name: ceo-signoff, depends_on: [draft, fact-check], kind: approval, out: decision.md}
      - {name: send, depends_on: [ceo-signoff, draft], kind: publish, out: published.md}

If the reviewer flags an unsupported claim — "draft says revenue grew 40%, the source says 25%" — the run parks at the gate and the approver sees the flagged claims next to the document itself. Approving resumes the run; rejecting stops it. The decision, the approver, and the timestamp are recorded with the artifact.

The fleet pattern

Draft → independent fact-check review → human approval gate → publish. The reviewer is a separate step with its own prompt, not the author grading its own work. The approval email deep-links to a page showing the exact document and the reviewer's flags, so sign-off means someone actually read it.

Guardrails that matter here

The approval gate pauses the run (`awaiting_approval`) until a human decides — there is no path to publish that skips the gate
Every decision is recorded with verdict, approver, and timestamp; artifacts are content-addressed and versioned, so you can prove which document was approved
Document steps run tool-free (`claude -p` with all tools disabled), so the draft is a document, not an agent session with side effects
Runs execute on your own Fleet worker — source documents never leave your infrastructure

Who this is for

Founders and executives who produce a recurring investor or board update from sources that live in (or can be committed to) a repository. The value scales with how expensive a wrong number is.

Frequently asked questions

What stops the AI from inventing a number?

Two structural layers. The draft step's corpus is the only context it gets, and a separate review step checks each claim against those sources and flags anything unsupported. A flagged run parks at the approval gate with the flags displayed — it cannot proceed to publish until a human looks at exactly what was questioned.

Is the workflow engine available today?

The visual workflow builder is live in the Fleet dashboard now, and the execution engine is rolling out with the next binary release. Fleet is in open beta — the governed document pipeline (draft, review, approval, notify, publish steps) is the default-on capability.

Related use cases

AI Workflows for Weekly Status Reports →AI Workflows for Compliance Evidence →AI Workflows for Recurring Data Reports →

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