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Put intelligence to work,
under your control

Turn a decision your team already trusts into something that happens on its own, inside limits your team sets. Works with any broker, including one that is not ours.

You cannot scale a response that waits on a person

It is still manual
The intelligence is right and the response is a person reading an alert and opening a ticket, which costs time the line does not have.
Automation nobody trusts
Generic AI tools reason over data with no plant context and no limits, so operations will not let them near anything that matters.
Knowledge that walks out
The people who know how a process really behaves are retiring, and most of what they know was never written down.

Act turns those numbers into work that gets done

Agent runtime frameworkPreview

The engine that runs agents inside your network on Kubernetes, Docker or standalone, outbound-only and certificate-authenticated.

So you can

Have agents reason on grounded operational context rather than hand-fed schemas, and run where the process runs, including air-gapped sites.

Agent studioPreview

Author what an agent senses, reasons over and does, in plain language rather than code, and validate it in a sandbox first.

So you can

Let domain experts build agents without a data science team, so expert judgment gets captured before it retires and runs at every site.

Agent template marketplaceRoadmap

Start from a proven pattern for a known operational job rather than from a blank page.

So you can

Start from a pattern that already works, so time to value is measured per use case rather than per project.

Agent orchestration & governancePreview

Policy gates, approval steps, autonomy limits and role permissions, authored by your team and enforced by the runtime on every cycle.

So you can

Keep behaviour inspectable and bounded, so automated action clears the risk review instead of stalling in it.

Operational safety & oversightPreview

People in the loop wherever you place them, and a recorded, replayable history of every decision and the data behind it.

So you can

Make every decision attributable and reversible, so autonomy can be extended incrementally as trust is earned.

Autonomy is a dial, not a switch

Most industrial AI stalls because the only choice on offer is trust it or do not. Every agent has a setting, you choose it per use case, and you can turn it back down at any point.

Describe

Tells you what is happening. Reads only.

Diagnose

Offers a cause, using the context around the signal.

Prescribe

Proposes the action and waits for a person.

Automate

Carries it out on its own, inside the bounds your team set.

You decide what an agent is allowed to do

Scope is set by your team, not by us. Agents work on the systems and decisions you nominate, and everything outside that boundary stays outside it.

Nothing runs unattended until it has earned the lane, and every action it takes is recorded, replayable and reversible.

What this changes

Problems caught before they stop a line

Conditions that used to surface after a breakdown surface while there is still time to act.

Quality corrected in the moment

Issues get addressed on the line instead of appearing in a batch review days later.

Expertise that stays

What your best operators know becomes something every site can run, not something that leaves with them.

Faster response when a person steps in

The agent already knows what happened and why, so the handover starts at the answer rather than the question.

See it in the platform

Screenshot of the HiveMQ Platform Act workspace, showing the AI agent lifecycle pipeline and fleet-wide performance metrics.

You have seen the whole path. It starts with one line.

Step 1

Connect

Move operational data reliably across OT, IT, edge and cloud.

Revisit Connect →Step 2

Contextualize

Give every signal shared meaning and a governed structure.

Revisit Contextualize →Step 3

Analyze

Turn trusted data into operational intelligence.

Revisit Analyze →
You are here

Act

Put that intelligence to work in governed workflows.

Frequently asked questions

What happens when an agent is wrong?

Out-of-lane actions never execute without a person approving them. Every cycle is recorded and replayable, so a wrong recommendation is something you review rather than something you discover later.

What leaves my network?

Agents run inside your environment. Connections are outbound-only and certificate-authenticated, so nothing inbound is exposed.

Who approves an action?

You do, by role. Approval gates and autonomy limits are authored by your team and enforced by the runtime.

Can I run this without your broker?

Yes, and with sources that are not MQTT. It is better with the rest of the platform underneath, because the agent reasons on data that has already been modeled and checked, but it does not require it.

Start with one operational improvement. Scale the results.

Start with the outcome that matters most. Prove value on the data you already have, then scale the pattern.