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The Logic Engine is a visual rule builder, and it’s a generic capability with three jobs:
  • Automatic tag membership — decide which devices or users carry a tag
  • Dynamic columns — compute a new column value on the Devices grid (see Dynamic columns and AI Transform)
  • Custom Vitals (Protect only) — your own compliance checks, which flag an issue on every device that fails (see Custom Vitals) New in 3.3
All three are built the same way: you assemble a flow on a canvas rather than writing a query.

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How a flow works

Logic Engine canvas for the APAC tag — a Device source node feeding two Condition nodes (Tags NOT Contains End-User, then Longitude NOT Greater than 60), ending in an Apply Tag: APAC node, with a Draft/Published toggle A flow reads top to bottom:
  1. A source node — the Device or User record being evaluated
  2. One or more Condition nodes — each tests a property against one or more values and branches True / False
  3. Optional Transform nodes that derive a new value along the way
  4. An output node — Apply Tag for a tag, Output for a column, or Flag Issue for a Custom Vital
Each flow has a Draft and a Published state, so a half-built rule never goes live until you click Publish.

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Nodes

Add a node from the palette on the left of the canvas, or click + under a node’s True or False output.

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Operators

A Condition can use Equals, Contains, Starts with, Ends with or Is empty, and number properties add Greater than. Turn on NOT to invert the test. You can enter more than one value, and the condition matches if any of them match.
The Logic Engine is not the query language. The query bar finds devices on the grid, and has wildcards for partial matches. The Logic Engine builds the rules behind tags, columns and Custom Vitals, with operators such as Greater than and branching logic the query bar doesn’t have.

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Build and test a flow

Right-click the canvas for the editing tools:
  • Debug — pick a device or user and the flow highlights the path it takes, so you can see which output it reaches before you publish
  • Auto Layout and Fit View — tidy the flow and bring it all into view
  • Insert Node, Copy / Paste and Undo / Redo
  • Add Note and Show Notes — leave an explanation on the canvas for whoever edits the flow next
  • Show Minimap and Show Node IDs — find your way around a large flow
Always run Debug against a device or user you expect to match, and one you don’t, before you click Publish.

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AI Composer

New in 3.3 Composer is available wherever you use the Logic Engine: tags, dynamic columns and Custom Vitals. Describe the logic you need in plain English and Composer builds the flow for you, from a single condition to multi-branch rules with transformations. It’s the quickest way to get a working flow, and you don’t need to know which node or operator to use.
1

Open Composer

Open the Logic Engine for a tag, column or Custom Vital and make sure the editor is on Draft. Composer opens in the panel on the right; toggle it with the Composer icon in the top bar.
2

Describe what you want

For example: “Build a flow that tags devices as ‘Encrypted’ if Jamf or Intune reports encryption enabled.”
3

Review and refine

Composer adds the nodes to the canvas. Check them, ask Composer for changes or edit the nodes by hand, and test with Debug before you click Publish.
Composer only works on a draft, and only one person can edit a flow at a time. Depending on your plan, requests may use AI credits; the panel shows your usage.

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Live re-evaluation

A published flow keeps itself up to date. When a device’s or user’s details change in a source system, it joins or leaves the tag, column value or Custom Vital result automatically. There’s nothing to refresh by hand.
Device rules and user rules are evaluated separately. A tag’s count in Settings → Tags includes inactive devices, so it can be higher than the number you see on the Devices grid with its default Active filter.

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Dynamic columns and AI Transform

The Logic Engine’s other job is building dynamic columns on the Devices grid. Open the columns panel (Toggle columns, or ⇧ C) and click Open Column Editor. Columns under the Logic Engine heading are computed by a flow rather than read from an integration; click + Add Column to create one. The flow is the same shape as a tag flow, but it ends in an Output node instead of Apply Tag:
  1. A source node (the device record) supplies fields
  2. Condition and Transform nodes derive a value
  3. An Output node writes the result into the column
For example, a Risk column can send devices through a few conditions to Outputs of Critical, High, Medium or Low, and a Device Type column can sort devices into types from their OS and hardware details.

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The AI Transform node

An AI Transform node sends the device fields you reference to an AI model with a natural-language prompt, and writes the model’s answer into the column. Use it for fuzzy classification — judgements that are hard to express as a chain of conditions. A simple example is an AI Device type column. Its flow is Device → AI Transform → Output: AI Device type column flow — a Device source node feeding an AI Transform node whose prompt classifies Workstation/Server/Unknown from the OsName and Model fields, ending in an Output node named aiTransform1 Its AI Transform node uses a prompt like:
The source node supplies OsName and Model, the prompt references them, and the result — Workstation, Server, or Unknown — populates the column.

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When to use AI Transform vs a Condition flow

Prefer plain Condition nodes whenever the logic can be expressed as field comparisons — they’re faster, free, and fully predictable. Reach for AI Transform only when conditions would be brittle.

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Best-practice prompt patterns

Tell the model exactly which values it may return. The AI Device type column returns only Workstation, Server, or Unknown — never free prose. A bounded output keeps the column filterable and safe to build saved views, Vitals, and Actions on.
“If you are unable to tell within a high degree of accuracy, output Unknown.” Without this the model guesses, and a confident-but-wrong value is worse than an honest gap. You can then filter the column for Unknown to measure how much you’re relying on the model.
Pass the smallest set of fields that answers the question (here, OsName + Model). Extra fields add noise and processing cost without improving the judgement.
Use the flow’s Debug option to run it against a real device and check the output. Keep the column in Draft until the results look right, then Publish.
Sort or filter on the new column and confirm the distribution is sane — a large Unknown share usually means the prompt needs more context or a sharper instruction.
Unlike Reporting — where the AI sees only the schema — an AI Transform sends the actual values of the fields you reference to the model. Reference only fields you’re comfortable processing this way, and don’t pipe sensitive identifiers into the prompt.

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What you can build with it

AI Transform is good at turning messy, open-ended input — model strings, hostnames, job titles, OS builds — into a clean, filterable value that drives tags, Vitals, Actions, and saved views. Each example below follows the same safe pattern: reference a few fields, constrain the output, and give an Unknown escape hatch. Spot unsupported operating systems. A condition can match the OS name, but it can’t know which builds are past vendor support — that’s world knowledge a rule can’t hold.
Feed the column into an Unsupported-OS tag and a Tracked View to drive the upgrade project. Sense-check the model’s verdict against your own end-of-life calendar before relying on it for compliance. Decode your hostname naming convention. Most estates encode site, role, or build year in device names — but every org’s scheme differs, so a fixed pattern needs hand-coding for each one. Describe the scheme in plain English instead:
Now you can tag and report by site without touching your naming convention or your CMDB. Flag hardware due for refresh. Knowing that a “ProBook 640 G2” is years past its prime is world knowledge. AI Transform reads the make and model and tiers it:
Sort the column and you have a refresh-budget candidate list in one click. Tag VIP devices from the user’s role. The same role can have many job titles, such as “CRO”, “Chief Revenue Officer”, “VP Sales” and “Head of Revenue”. Rather than maintain a list, let the model read the intent:
Tag the VIP results, then filter Vitals and Tracked Views to that tag so you can keep a closer eye on those devices. Canonicalise messy vendor names. "Dell Inc.", "Dell Incorporated", and "DELL" are three values for one vendor. Collapse them for clean grouping and reporting:
The common thread: each of these would be brittle or impossible as a fixed rule, because the input is unbounded free text or the judgement needs outside knowledge. That’s the sweet spot for AI Transform — and because every output is a constrained value, the column stays as filterable and dependable as any raw field.

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Cross-references

Tags

Tag design and strategy

Custom Vitals

Build your own compliance checks

Query Language

Grid search syntax — separate from the Logic Engine