AI AGENTS
Batch-research a queue of questions from Airtable into individual memos
On a schedule, pulls every unanswered question from an Airtable queue, researches and writes a sourced memo for each, then writes the memo back and marks the row done.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires for batch run
- ActionPull unanswered questions from AirtableAirtable
- ActionResearch sub-queries for each questionPerplexity
- ActionDraft sourced memo per questionOpenAI
- LogicSkip and flag questions too vague to answer
- OutputWrite memo back to Airtable, mark answeredAirtable
What it does
Drains a backlog of research requests automatically. The agent reads an Airtable queue of questions, processes each one end to end into a sourced memo, writes the result back to the row, and updates its status, so a team can submit questions all week and find answered memos waiting.
When to use it
When research requests pile up faster than an analyst can clear them and they arrive in a structured intake (a form feeding Airtable). Best for steady-volume, medium-depth questions that benefit from batching.
How it works
- 1A schedule triggers the batch run.
- 2The agent queries Airtable for rows with status "unanswered".
- 3For each question it decomposes into sub-queries and runs web research.
- 4An LLM step drafts a sourced memo with a one-line answer, supporting findings, and citations.
- 5A logic step skips and flags any question too vague to answer, rather than guessing.
- 6Each memo is written back to its Airtable row and the status is set to "answered".
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect PerplexitySearch-grounded answers with citations.
- 3Connect OpenAIModels, embeddings, files.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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