AI AGENTS

On-Demand Cited Research Brief from Web + Notion

Triggered by a chat question, an agent searches the live web and your Notion workspace, then writes a structured brief where every claim carries an inline source link.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerTeam member asks a research question in chat
  • ActionSearch live web sources with ExaExa
  • ActionSearch internal docs in NotionNotionNotion
  • ActionSynthesize brief with inline citations (OpenAI)OpenAI
  • LogicVerify every claim maps to a real source; drop the rest
  • OutputReturn cited brief to the chat thread

What it does

Turns a one-line question into a fully cited research brief. The agent pulls fresh sources from the web via Exa, cross-references what your team already knows in Notion, and synthesizes a short brief in which every factual statement is footnoted with the URL or page it came from. No uncited assertions are allowed through.

When to use it

When an operator, analyst, or founder needs a fast, trustworthy answer to a research question ("What's the current state of X regulation?") and wants to see exactly where each fact originated rather than trusting an unsourced summary.

How it works

  1. 1A team member asks a research question in the agent chat.
  2. 2The agent runs an Exa neural search to gather the most relevant recent web sources.
  3. 3In parallel it searches the Notion workspace for any internal docs on the topic.
  4. 4An OpenAI synthesis step drafts the brief, attaching a citation to every claim and discarding anything it can't source.
  5. 5A logic step verifies each claim maps to a real source link; unsupported lines are dropped or flagged.
  6. 6The finished cited brief is returned in the chat thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ExaNeural search across the web.
  2. 2
    Connect NotionPages, databases, comments.
  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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