FINANCE

On-Demand Spend Investigation Agent via Discord Command

Triggered by a finance team member's Discord question, an agent queries BigQuery billing data to investigate the spend, breaks down drivers by service and project.

CategoryFinance
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDiscord command with spend questionDiscordDiscord
  • ActionInterpret question and pick billing dimensionsOpenAI
  • ActionRun billing queries in BigQueryGoogle BigQueryBigQuery
  • LogicCheck if results answer the question or need follow-up
  • OutputReply with breakdown in Discord threadDiscordDiscord

What it does

When someone in finance asks a spend question in Discord, this agent interprets the request, runs the right BigQuery queries against your billing export, and replies in the same thread with a breakdown of what drove the cost. It handles open-ended questions like "why did compute jump last Tuesday" or "what did project X cost in May" without anyone writing SQL.

When to use it

Use it to give non-technical finance staff self-serve access to billing data. It removes the bottleneck of asking a data engineer for ad-hoc cost breakdowns and keeps the question, the query, and the answer together in one Discord thread for reference.

How it works

  1. 1A Discord message or slash command in the finance channel triggers the run with the user's question.
  2. 2The agent interprets the question and decides which billing dimensions to pull.
  3. 3It runs one or more BigQuery queries to retrieve the relevant spend, grouped by service, project, and time.
  4. 4A logic step checks whether the results answer the question or need a refined follow-up query.
  5. 5The agent composes a clear breakdown with dollar figures and likely causes.
  6. 6The answer is posted back in the originating Discord thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  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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