CHATBOTS
Slack dictionary agent with warehouse-first, Confluence-fallback grounding
An agentic concierge answers data questions by first checking BigQuery catalog metadata, then falling back to the Confluence data-glossary space.
How it runs
The automated pipeline, trigger to output.
- TriggerSlack mention with a natural-language data questionSlack
- LogicAgent decides: schema metadata, business concept, or both
- ActionQuery BigQuery catalog for matching tables/columnsBigQuery
- ActionSearch Confluence glossary space when coverage is thinConfluence
- OutputReply in Slack with synthesized, source-cited answerSlack
What it does
An agent-driven concierge that answers free-form data questions by reasoning across two sources of truth. It checks the BigQuery catalog first for authoritative column metadata, and if the question is conceptual rather than schema-level, it searches the Confluence data-glossary space — always citing where the answer came from.
When to use it
When questions range from 'what's the type of this column' to 'what's our definition of an active customer', and the answers live partly in the warehouse and partly in human-written glossary docs. Use it when single-source lookups aren't enough and you need an agent to decide where to look.
How it works
- 1A Slack mention poses a data question in natural language.
- 2The agent decides whether it needs schema metadata, a business concept, or both.
- 3It queries the BigQuery catalog for any matching tables and columns.
- 4If coverage is thin, it searches the Confluence glossary space for the concept.
- 5The agent synthesizes a grounded answer, declining to invent anything unsupported by either source.
- 6It replies in the Slack thread with the answer and an explicit citation of the source used.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect ConfluenceSpaces, pages, blueprints.
- 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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