AI AGENTS
Slack Ask to Cited Answer Thread
Lets anyone ask a research question by mentioning the bot in Slack; the agent researches it live and replies in-thread with a concise answer plus numbered citations…
How it runs
The automated pipeline, trigger to output.
- TriggerUser mentions the research bot in SlackSlack
- ActionLive neural search for sourcesExa
- LogicEnough quality sources to answer?
- ActionSynthesize cited answer with confidence ratingOpenAI
- OutputReply in the Slack threadSlack
What it does
Puts a sourced research assistant inside Slack. Mention the bot with a question and it runs a live multi-source search, synthesizes a tight answer, and replies in the same thread with numbered citations and an overall confidence rating, so the whole team sees both the answer and where it came from.
When to use it
Use it for the steady stream of "does anyone know..." questions in a team channel. It replaces guesswork and stale tribal knowledge with a fast, cited answer everyone can audit, without leaving Slack.
How it works
- 1A Slack app-mention trigger captures the question.
- 2The agent runs a neural search to pull candidate sources.
- 3A logic step checks whether enough quality sources were found; if not it replies asking to narrow the question.
- 4An LLM synthesizes a concise answer with inline numbered citations and an overall confidence rating.
- 5The bot posts the answer back in the original Slack thread.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect ExaNeural search across the web.
- 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 this workflow in your colony.
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