AI AGENTS

On-demand cost-spike investigator in Slack

An engineer types a time window in Slack and an agent investigates the Cloudflare and Vercel cost driver for that window on demand, returning the top offending route or asset.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSlack command with time windowSlack
  • ActionQuery Cloudflare top URLs + cache status for windowCloudflareCloudflare
  • ActionQuery Vercel usage for the same windowVercelVercel
  • LogicPick larger overage platform and top offender
  • ActionExplain driver and write cache ruleOpenAI
  • OutputReply in Slack thread with fixSlack

What it does

Gives engineers a chat command to root-cause a cost spike on demand. Instead of waiting for a scheduled report, someone asks about a specific window in Slack and gets back the single biggest cost-driving route or asset across Cloudflare and Vercel, with a ready-to-paste cache rule.

When to use it

When you notice a bill anomaly mid-day and want an immediate, conversational investigation scoped to the exact hours in question, without leaving Slack or opening a dashboard.

How it works

  1. 1A Slack slash command or mention triggers the workflow with a time window argument.
  2. 2The agent queries Cloudflare analytics for top URLs and cache status in that window.
  3. 3It queries Vercel usage for the same window to catch function-side cost.
  4. 4A logic step picks whichever platform contributed the larger overage and selects the top offender.
  5. 5An OpenAI step explains the driver and writes a copy-paste cache rule or header fix.
  6. 6The answer is posted back as a threaded Slack reply on the original request.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
  3. 3
    Connect VercelDeploys, runtime logs, analytics.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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