AI AGENTS

Route Low-Confidence Security Answers to the Right SME

After answers are drafted, this agent classifies each question by domain, routes anything below a confidence threshold to the owning subject-matter expert via Slack DM.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled scan for needs-review answers
  • ActionClassify question by security domain
  • LogicBranch on confidence threshold
  • ActionDM mapped SME with draft and gapSlack
  • ActionCapture and normalize SME reply
  • OutputWrite final answer to Airtable answer bankAirtableAirtable

What it does

Closes the gap on questions the autofill agent cannot answer with confidence. Each unresolved question is classified into a domain (infosec, legal, infra, privacy), assigned to the SME who owns that domain, and sent to them with the draft and missing context. Their reply is captured back into an Airtable answer bank so the next questionnaire reuses it.

When to use it

Use when your first-pass autofill leaves a tail of hard questions that bounce around channels and never get logged. This makes routing deterministic and turns each human answer into durable knowledge.

How it works

  1. 1A scheduled run scans for newly drafted answers marked needs-review.
  2. 2The agent classifies each question into a security domain.
  3. 3A branch checks confidence: high-confidence answers skip straight to the answer bank.
  4. 4Low-confidence items are DM'd to the mapped SME in Slack with the draft and gap.
  5. 5The SME's reply is captured and normalized into a clean answer.
  6. 6The final answer is written to the Airtable answer bank with its domain tag.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect AirtableBases, tables, views, automations.
  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.

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