AI AGENTS

Decompose an exec question into a sourced decision memo (Notion)

Takes a vague leadership question in chat, breaks it into focused sub-queries, researches each across the web.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOperator asks an exec question in chat
  • LogicDecompose into 3-6 focused sub-queries
  • ActionNeural web search per sub-queryExa
  • ActionSynthesize findings + draft recommendationOpenAI
  • LogicVerify every claim has a citation; flag unsourced
  • OutputPublish cited decision memo to NotionNotionNotion

What it does

Turns a one-line executive prompt like "Should we expand into the German market next year?" into a structured, source-backed decision memo. The agent decomposes the question, runs targeted research per sub-question, and assembles a memo with a recommendation, supporting evidence, and inline citations in Notion.

When to use it

When a founder or operator asks a broad strategic question and you want a defensible first-draft answer in minutes instead of a multi-day analyst cycle. Best for market, competitive, and go/no-go questions where the value is in fast, sourced synthesis.

How it works

  1. 1An operator submits the question in chat.
  2. 2The agent decomposes it into 3-6 answerable sub-queries (market size, competition, regulation, cost, timing).
  3. 3For each sub-query it runs a neural search to gather high-signal sources.
  4. 4An LLM step synthesizes findings per sub-query and drafts a recommendation with confidence and risks.
  5. 5A logic step checks every claim carries a source; unsourced claims are flagged, not dropped.
  6. 6The finished memo is published as a new Notion page with citations and an executive summary up top.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ExaNeural search across the web.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  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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