agent hive

TICKET MANAGEMENT

Diagnose Sensor Anomalies and Draft a Work Order with an Ops Agent

An agent reads each incoming anomaly, pulls the sensor's recent history and asset metadata from Airtable, reasons about the likely root cause.

CategoryTicket Management
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerAnomaly received via webhookHTTP webhook
  • ActionPull sensor history and asset metadata from AirtableAirtableAirtable
  • LogicAgent infers root cause and assigns priorityOpenAI
  • ActionWrite drafted work order back to AirtableAirtableAirtable
  • OutputSend diagnosis and work order to engineer in SlackSlack

What it does

Instead of dumping a raw threshold breach on a technician, this workflow has an agent investigate first. It correlates the alert with the asset's history and produces a ready-to-action work order with a probable cause and recommended fix.

When to use it

Use it when your team wastes time triaging cryptic sensor codes by hand. The agent turns a numeric anomaly into a human-readable diagnosis and a draft work order, so engineers arrive on site knowing what to check.

How it works

  1. 1An HTTP webhook receives the anomaly with sensor ID, reading, and location.
  2. 2An action pulls the sensor's recent readings and asset metadata from Airtable to give the agent context.
  3. 3The agent reasons over the data — comparing against normal ranges, recent faults, and maintenance history — to infer a likely root cause and priority.
  4. 4An action writes the drafted work order back to Airtable with the diagnosis, priority, and suggested parts.
  5. 5The output posts the plain-English summary and work order link to the assigned engineer in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect AirtableBases, tables, views, automations.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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.

Run this workflow in your colony.

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