AI & RAG

Agent-run end-to-end completion of a security questionnaire

An autonomous agent works a full questionnaire from a Google Drive upload: it retrieves policy evidence across Confluence and Drive, drafts every answer with citations.

CategoryAI & RAG
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew workbook in intake Drive folderGoogle DriveGoogle Drive
  • ActionAgent clusters questions by topicOpenAI
  • ActionRetrieve evidence from Confluence and DriveConfluenceConfluence
  • LogicMark unsupported questions as gaps
  • OutputDeliver completed workbook to Drafts folderGoogle DriveGoogle Drive

What it does

Hands an entire questionnaire to an agent that plans its own work: clustering related questions, deciding what evidence each needs, pulling from both Confluence policies and supporting Drive documents, and assembling a fully drafted, cited response workbook. Questions with no supporting evidence are explicitly marked as gaps rather than fabricated.

When to use it

Use it for large, messy, or non-standard questionnaires where rigid row-by-row retrieval underperforms and you want judgment about how to group and source answers. Good fit when answers must draw from more than one knowledge source.

How it works

  1. 1A new workbook in the intake Drive folder triggers the agent.
  2. 2The agent parses and clusters questions by topic.
  3. 3For each cluster it retrieves from the Confluence policy space and relevant Google Drive evidence files.
  4. 4It drafts grounded answers with inline citations, marking unsupported items as gaps.
  5. 5It self-reviews for unsupported claims before finalizing.
  6. 6The completed, cited workbook is written back to a "Drafts" Drive folder for human approval.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Google DriveDocs, sheets, slides, files.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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