Ask the question every administrator has wanted to ask a management console: “what breaks if I change this?” — and get it answered before anything is applied.
For fifteen years, the endpoint management industry has been perfecting the wrong thing.
We have built better consoles. Better filters. Better reports, better exports, better dashboards to read on Monday morning. And all of it rests on an assumption nobody questioned: that somewhere there is a person with the time to go and look.
There isn’t. There hasn’t been for years. Too many tools, too many policies, too much reactive IT — a fleet of any real size generates more questions every morning than any team can ask, so most of them are never asked, and the gap between what your policies say and what your devices actually do widens quietly until something forces you to look.
Applivery Intelligence closes that gap by removing the person from the looking and keeping them exactly where they belong: on the deciding.
Autonomous Endpoint Management, defined
Autonomous Endpoint Management (AEM) is not a console with a chat box on it. It is an operating model with five steps, and Applivery Intelligence runs all five:
- Watch — checks run continuously, on a cadence, unattended.
- Find — drift, exposure, access creep and anomalies, computed rather than guessed at.
- Predict — what a fix would do, simulated before anything touches a Device.
- Act — on one Device or on every Device it applies to, under your role and your approvals.
- Verify — re-checked, logged per Device, and reversible.
Then it starts again, sharper: every finding you accept, dismiss or revert tunes what the loop brings you next.
The loop turns without you, and stops precisely where you told it to. But a loop nobody can reach into is a black box, so before the five steps: the place you reach into it.
Chat — the surface the whole loop runs through
Autonomy does not remove the human. It changes what the human spends the day on — and it needs one place where a person can join the loop at any point in it. That place is Chat, and it is the cornerstone of everything that follows rather than an interface bolted onto the side of it.
Watch how a real afternoon actually goes:
- You ask, because you want to understand something — what did the macOS 14 rollout actually reach? The answer comes back as an artifact: a sortable device table, a policy comparison, a map, a CVE breakdown, an action receipt with its approval trail.
- You pin it. That artifact becomes a live widget on an Insights dashboard and starts refreshing on its own. The question you asked once is now a number that watches itself.
- You put an alert on it, and the alert starts a Workflow. What you asked about is now something the platform does about it.
- Cowork finds the next one before you do — and when a finding needs arguing with, you open a side chat with the specialist that raised it, which still has the evidence in hand.
- You ask the next question, which is usually the one the finding provoked.
Every surface in this post is entered from a conversation and comes back to one. Chat is where work starts, where it is interrogated, and where it is understood after the fact — the dashboards, the runbooks and the findings are where it lives in between.
Two things make it a control surface rather than a chat window. It is grounded: every answer carrying data came from a specialist that had to go and fetch it, and if the Workspace exposes no capabilities the assistant refuses rather than inventing a plausible fleet. And it is actionable: the same conversation that answered the question can take the action, under your permissions, asking first.
It also lives inside the Applivery Dashboard, on the Workspace you already have open, reusing your session — no second console, no second login, nothing to install. A separate AI product is a place you have to remember to visit, which is the reactive habit all over again. A companion in the product you already use is one you actually use.
Watch — the fleet reports back
Insights is the surface most people meet first, and it is usually described as “dashboards”. That undersells it in two directions.
Visibility, at whatever granularity you want. Ask a question, get an answer, press Add to dashboard, and that answer becomes a live widget that re-runs itself from then on. KPI tiles, line, bar and donut charts, tables, maps, cards. One Device, one Segment, one country, or every endpoint you own — the granularity is whatever the question was. Dashboards refresh on a real cron expression with full time-zone support, and the header tells you when the last refresh ran, when the next one is due, and warns you when one is overdue rather than quietly showing you stale numbers.
And actionability, which is the half that matters. A widget is not just a picture. Any widget can carry alert rules — a threshold crossed, a change of more than N %, or the absence of a refresh inside a window — with a notify mode and a cooldown so you are told once rather than sixty times. And a fired alert does not have to end at a notification: it can start a Workflow, which means the chain from number moves to something is done about it closes without a human in the middle.
That is the difference between a dashboard and an operational layer. A dashboard tells you the encryption gap grew. This tells you, opens the remediation, and — if you have set the dial that way — closes it before you have read the message.
Find — the two things nobody has time to look for
Ask any IT team what they know they should check weekly and don’t, and you get the same two answers.
Known vulnerabilities. The Vulnerability Agent matches what has been published against what you actually run: severity, presence in the Known Exploited Vulnerabilities catalogue, exploitation likelihood — set beside the Devices in your fleet on the affected version. Pin it, and “how exposed are we, right now” stops being a research project somebody starts after an incident and becomes a number that refreshes on its own.
Configuration drift. The distance between the Policy you designed and the configuration a Device ended up with is where most real-world exposure lives, and it is invisible from a Policy list. Intelligence attacks it from three sides: checks that catch composition conflicts, Policies that compose to nothing, kiosk hardening gaps and secrets left in an applied configuration; the Policy Agent, which compares Policies and tunes priorities; and the Impact Agent, which tells you what a change would break before you make it.
Add the rest of the catalogue — stale check-ins, obsolete OS versions, over-broad grants, privileged accounts without 2FA, dormant admins, duplicate serials, assets linked to Devices that no longer exist, enrolment and data-usage spikes measured against each Device’s own history — and what you have is not an alert feed. It is the audit you never have time to run, running continuously.
Predict — know the answer before you press the button
This is the part the industry has never had, and the part we would ask you to judge us on.
Every experienced administrator has the same scar: a Policy change that was obviously correct, applied on a Friday, that turned out to hold a shift of kiosks hostage on Saturday morning. The reason is always the same — effective configuration is composed, not written. What lands on a Device is the resolution of everything that targets it, and no list view has ever shown you that resolution before it happened.
Applivery Intelligence answers it as a question, in advance:
If I raise the priority of the Baseline Security policy, which Devices change, and what do they lose?
The Impact Agent answers read-only, on a simulated composition: before and after, per Device, without touching anything. Audience and Segment changes preview the same way — who would be affected, computed against the real population, before the rule is saved. And when a remediation is proposed, the subjects are re-verified at execution time, so what you approved is what runs.
Prediction runs forward as well as sideways. Anomaly checks compare a Device against its own history, so a spike is a change in behaviour rather than a device being busy; obsolete-OS and CVE exposure surface while they are still a maintenance window rather than a breach; and access drift is caught while it is still a permission somebody forgot to remove. The cheapest incident is the one that was a finding three weeks earlier.
Act — surgical, or across the entire fleet
Detection without action is a nicer way to worry. Applivery Intelligence acts at both ends of the scale, with the same guarantees at both ends.
Surgical. Type @ to reference exactly one object — a Device, Policy, MDM User, Segment, Device Audience, Automation Rule or Enrollment Template — and act on precisely that. Lock the laptop David lost yesterday. The reference carries the object’s identity, so nothing has to guess which “MacBook Pro” you meant.
Fleet-wide. The same actions run across a population: a remediation executes against every subject in a finding that survived re-verification, and a for-each node in a Workflow runs a step over every Device in a computed list, with retry and backoff. What makes that safe is not caution, it is structure — the population is computed, never described by a model, an approval gate sits in front of anything irreversible, execution is recorded per Device, and Revert undoes only the steps that actually succeeded.
One Device without opening five screens. Four thousand Devices without writing a script. An audit trail either way.
Verify — receipts, not promises
Every action produces a record: what ran, on which Device, with which parameters, under whose name, with what result. Workflow runs keep the input and output of every node. Cowork re-computes the numbers after execution to confirm the finding actually closed. Applivery’s own audit log carries all of it under the identity of the person who approved it — not a service account, because there isn’t one.
This is the unglamorous half of autonomy, and it is the half that decides whether a regulated organisation can adopt it.
Cowork — the colleague that works your fleet while you are not
Cowork is where all of this stops being something you operate and starts being something that operates for you.
Specialist agents run in the background on a cadence you set, examining the fleet and producing findings: inbox items with the evidence attached, the affected Devices named, and — where the check allows — a remediation already drafted. You acknowledge them, accept them, argue with them in a side chat with the agent that raised them, or approve the fix and watch it run.
Three properties make it a colleague rather than a liability:
- It is supervised by design. Detection is deterministic code Applivery ships, not a model deciding what looks wrong. Remediations are versioned templates, not plans a model improvised. The agent triages and explains; it cannot invent a target or an action.
- It knows when it did not work. Cowork distinguishes “the check ran and found nothing” from “the check could not run”. The green Nothing to review state appears only when there is positive evidence the fleet was actually examined — because monitoring that goes quiet when it breaks is worse than none.
- It gets better at your fleet. Every acceptance, dismissal, threshold change and reversal is feedback: accepted findings stop asking, monitors narrow to the scope and severity you actually care about, and Applivery ships new checks into the same catalogue as new classes of problem appear. The loop you run in month three is not the loop you started with.
The constellation
Nothing here is one model doing everything — that architecture cannot be governed and does not scale. A coordinator — the IT Agent — reads each request and routes it to one or more Agentic Specialists, each with its own brief, its own instructions and its own restricted set of platform capabilities. The coordinator has no access to your Devices at all: every answer carrying data came from a specialist that had to go and fetch it.
| Specialist | Owns |
|---|---|
| Compliance Agent | Posture, audits, who is compliant or drifting, violations |
| Security Agent | Device actions — move, rename, lock, wipe, install — and risk triage |
| Impact Agent | Read-only “what if”: before and after, simulated Policy composition |
| Policy Agent | Policy design, comparison, priority tuning, Segment permissions |
| Insights Agent | Fleet analytics — trends, distributions, charted reports |
| Knowledge Agent | Product and REST API documentation, answered with cited links |
| Enrollment Assistant | Getting devices in — enrolment tokens, Smart Enrollments, and the QR and steps the person holding the device needs |
| Policy Architect | Designs and creates ready-to-apply Policies for Apple, Android, AOSP and Windows |
| Scripts Engineer | Authors, creates and assigns macOS Bash and Windows PowerShell scripts |
| Vulnerability Agent | Known CVEs for an OS or app version, with severity, KEV and exploit likelihood |
You feel that team rather than read about it. Every reply carries a badge naming the specialist that produced it. Ask something that spans domains — “which Devices are non-compliant, and what would fixing the Policy break?” — and the coordinator delegates in parallel and returns one answer. Ask the Policy Architect for a hardened baseline and you get a ready-to-apply Policy for Apple, Android, AOSP or Windows, not a description of one. Ask the Scripts Engineer and you get a Bash or PowerShell script it can also assign. And in Cowork the same idea runs unattended: the Posture Auditor takes hygiene, access, inventory and configuration and may propose a fix; the Threat Analyst takes anomalies and never proposes a write at all.
Autonomy is a dial, not a switch
Here is the commitment that matters more than any capability in this post: you decide how much of this runs without you, one setting at a time.
- Suggest — it tells you what it found, and nothing else happens.
- Approve — it drafts the fix, names the targets and the parameters, and waits for you.
- Auto — it runs the runbook and hands you the receipt.
Set it per capability, per workflow, per monitor. Move it when a class of work has earned your trust, and not a day before. Everything starts on Needs approval — reads included — because the right default for a system that can touch production endpoints is to ask.
And some things never move, whatever the dial says: wipe, lock and anything that deletes are permanently gated behind a confirmation naming the action, the target and the parameters. Underneath all of it, one rule with no exceptions — Intelligence acts as you, under your identity and your Applivery role. If you cannot wipe a Device in the Dashboard, you cannot wipe it here. There is no service account, no elevated mode, and no path by which the assistant can exceed the person operating it.
Guardrails run on every message: prompt-injection filtering, scope filtering, a hard block on answering without data — if the Workspace exposes no capabilities, chat refuses rather than inventing a plausible fleet — and anti-fabrication rules that stop the assistant claiming an action succeeded unless the tool confirmed it in that same turn.
API-first, and now agent-first
Applivery was built API-first, from the first release. Every capability in the platform exists as a documented, permissioned, audited REST endpoint, because we believed a management platform earns its place by how well it plugs into everything else a company runs — not by how much work it can trap inside its own screens.
That decision, made years before anybody said “agentic”, is why this next part was a continuation rather than a pivot.
An AI agent needs exactly what an integrator needed: a complete, permission-aware set of actions, plus a way to discover them at runtime. That is what the Model Context Protocol standardised, and the Applivery MCP server is our implementation of it — 200+ platform actions, discoverable, scoped per agent, every call logged.
Two consequences worth stating plainly:
- Your fleet is available inside the assistant you already use. Connect Applivery to Claude or ChatGPT and operate endpoints from the conversation where the rest of your work already lives — no API key to mint, the same permissions, the same approval gates. The shift is not that a machine can call our API. It is that the person deciding no longer has to come to our console to decide.
- Applivery is a component of your agentic stack, not the end of it. Anything that speaks MCP — your own orchestration, a security agent, an internal copilot — can compose Applivery with your ITSM, your identity provider and your SIEM, agent to agent. And because our documentation and API reference are served over MCP too, an agent can learn how to use Applivery correctly before it tries. Applivery Intelligence consumes that very same catalogue internally: identical capabilities, different assistant.
We are not adding AI to a device manager. We are making a device manager that every AI can operate.
Where this goes: the Autonomous Workplace
Everything above ships. Here is the direction, so you can judge whether it is one you want to be on.
The endpoint is only the first surface. The same loop — watch, find, predict, act, verify — applies to applications, to identity, to the employee’s first day and their last one. We are building toward an Autonomous Workplace: context-aware, policy-driven, AI-assisted, where the corporate device configures itself for the person it is handed to, where an application rollout schedules itself around the fleet’s real state, where a compliance posture holds itself in place between audits instead of being reconstructed before them, and where the IT team’s calendar is finally free of the work that never needed a human in the first place.
The dial moves with your trust. Today most teams will start at Suggest, move the reads to Auto within a week, and leave the destructive end where it belongs. In a year, the routine two thirds of endpoint operations will run on Auto in organisations that would not have permitted any of it today — not because the technology got braver, but because every action left a receipt, and receipts are how trust is built.
That is the bet: the winner in endpoint management will not be whoever ships the best console. It will be whoever makes the fleet trustworthy enough to run itself.
Plans, availability and rollout
Applivery Intelligence is generally available, licensed per seat. The organization Owner buys seats at a tier and assigns them. The rollout begins in September 2026 and continues progressively through the fourth quarter, so every organisation arrives with the configuration and the conversation it needs rather than all at once.
| FREE | PRO | MAX | ULTRA | |
|---|---|---|---|---|
| Price | – | €29 /seat/mo | €99 /seat/mo | €199 /seat/mo |
| Chat | ✅ | ✅ | ✅ | ✅ |
| Insights, Workflows, Cowork | – | – | ✅ | ✅ |
| Usage per period | A limited taster | Baseline capacity | 5× Pro | 10× Pro |
Prices exclude VAT. Max and Ultra have the same features — the difference is headroom, not capability. Every tier drives the same models: tiers differ by which surfaces you reach and how much you use them, never by giving one customer a smarter assistant than another. It runs on your existing Applivery Workspace, on a platform certified to ISO 27001 and built GDPR-ready in Europe.
Get started
Open your Applivery Dashboard and press the ✨ button — Getting started walks an Owner through the first seats in about five minutes.
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