AI AGENTS

Grounded Questionnaire Drafting via Policy MCP Server

Triggered by webhook from your questionnaire portal, this agent answers each question by calling a custom MCP server that exposes your live policy and control catalog.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPortal posts questions to webhookHTTP webhook
  • ActionExtract questions and requested format
  • ActionFetch matching controls from policy MCP serverCustom MCP server
  • LogicReject answers without a live control mapping
  • ActionDraft answers citing control IDs and evidence
  • OutputReturn completed responses to portal via webhookHTTP webhook

What it does

Drafts questionnaire answers that are tied to live controls rather than static documents. Instead of searching files, the agent calls a custom MCP server wired to your GRC system's control catalog, so every answer references the current control owner, status, and evidence. This keeps answers from drifting out of date.

When to use it

Use when your controls change often and document-based answers go stale, or when auditors require every questionnaire response to trace to a tracked control ID. Ideal for teams already running a control management system behind an MCP server.

How it works

  1. 1Your questionnaire portal posts new questions to an HTTP webhook, triggering the run.
  2. 2The agent extracts each question and its requested format.
  3. 3It calls the custom MCP server to fetch matching controls and their current state.
  4. 4A check rejects any answer that lacks a live, in-effect control mapping.
  5. 5The agent drafts each answer citing the control ID and evidence reference.
  6. 6The completed response set is returned to the portal via webhook callback.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect Custom MCP serverConnect any MCP-compatible tool you own.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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