Rollout best practices

Six rules that keep an Endpoint AI rollout clean. Each one exists because skipping it costs you more later than following it costs you now.

Keep the default profile minimal

Everything in the default profile reaches every enrolled device in the organization after the next sync. That makes it the most expensive place to put anything: a noisy skill there annoys everyone at once, and a connector there widens your audit surface across the whole fleet. Reserve it for the few capabilities genuinely everyone needs, and put everything else in team profiles.

One profile per team shape, not per person

Build profiles around how teams work, not around individuals. A per-person profile sprawl means every joiner, leaver, and role change becomes a manual edit, and nobody can answer "what does sales get?" without reading twenty profiles. Direct assignments exist for genuine exceptions; when you find yourself making the same direct assignment three times, that's a profile telling you it wants to exist. See how access combines on the Teams & people page.

Review before wide assignment

The review queue is the gate between "someone built this" and "fifty devices run this." Package skills submitted via Skilify are not auto-published, even for account owners, and that friction is deliberate: a reviewer who is not the author catches embedded credentials, over-broad tool use, and instructions that only worked on the author's machine. Assign to a pilot team first, and widen only after the pilot shows real use.

Name skills for the job, not the tool

People choose skills by name from a list, so the name must answer "what does this do for me?" — "Summarize support tickets" beats "Zendesk MCP v2". Job-based names also survive replatforming: swap the connector underneath and the skill name stays true. Names that describe plumbing produce duplicate requests for capabilities that already exist, which you will see accumulate as skill requests.

Budget alerts before hard limits

Set LLM budgets with alerts from day one, but resist enforcing hard limits until you have a few weeks of real usage. In week one you do not yet know what normal spend looks like, and a limit that cuts a team off mid-task teaches them the AI is unreliable — exactly the impression a rollout cannot afford. Alerts give you the same visibility without the outage; tighten to enforcement once the baseline is clear.

Watch the audit log during week one

The audit log records every tool call: who called what, with which skill, and whether it worked. In the first week of any wave, skim it daily. Clusters of errors on one connector mean an auth or permissions problem to fix before employees conclude "it doesn't work"; a team with zero calls means enrollment succeeded but adoption didn't, which is a communication problem, not a technical one. The Dashboard charts give you the same signal at a glance, with the live stream one click away.