Act Overview

HiveMQ Act lets you build AI agents without application code. Agents connect directly to your MQTT brokers, databases, and APIs, analyze the data they observe, and take automated actions.

You describe what an agent does, and the platform runs it, keeps it alive, and manages your fleet. When an action needs approval, the platform routes it to a person.

What You Can Build

  • Monitor industrial sensors: Subscribe to MQTT topics, detect anomalies, and publish alerts.

  • Automate threshold responses: Evaluate conditions with rules or a large language model (LLM), and act when limits are exceeded.

  • Add human review to quality control: Query a database for defect data, then route critical findings for human review before the agent acts.

  • Connect to your ERP or SCADA system: Call REST APIs, write results back, and create maintenance tickets.

To build your first agent end-to-end, see Getting Started.

How an Agent Works

Every agent runs the same four-stage loop on each cycle: Sense → Reason → Actuate → Reflect.

Stage What Happens

Sense

Collect data from MQTT topics, databases, and APIs.

Reason

Analyze the data with an LLM or deterministic rules, plan actions, and apply governance checks.

Actuate

Execute the planned actions: publish MQTT messages, write to a database, call an API, or send email.

Reflect

Learn from the cycle’s outcomes, save memory for the next cycle, and capture metrics.

An agent runs on a trigger (an interval, a cron schedule, or an incoming MQTT message) and loops continuously until you stop it.

Agents Are Configuration, Not Code

An agent is described entirely by configuration: a data-defined agent (DDA). There is no code to write and no build step. The platform reads the configuration and runs the agent.

An agent can reason in two ways. You can use one mode or both in the same agent:

Mode When to use

LLM-powered

Pattern analysis, anomaly detection, and natural-language reasoning.

Deterministic

Fixed threshold rules, with no AI cost or latency.

Use deterministic rules for straightforward checks. Call an LLM only for an analysis that needs it.

Networks and Orchestrators

Networks organize agents, and orchestrators run them:

  • A network groups everything for one site, line, or environment, for example berlin-factory or munich-staging. Tools and resources, such as databases, APIs, and Model Context Protocol (MCP) servers, that you register on a network are available to every agent on the network. Therefore, you can configure a shared connection once rather than per agent. See Create a Network.

  • An orchestrator is the software you run on your own infrastructure to host agents alongside your brokers. Every orchestrator belongs to one network and runs your agents as Docker containers. See Deploy an Orchestrator.

The Control Plane is the agent-management layer. You use it to create networks, store agent templates, deploy agents, and monitor them. The Control Plane is built into the HiveMQ Platform, not a separate product. To access the Control Plane, open the Act tab.

Two Brokers: Your Data and the Agent Bus

Each agent maintains two separate connections. This separation keeps business traffic isolated from coordination traffic:

Connection What it carries Configured by

Data connections

Business data (sensor readings, database results, API responses)

Your agent configuration

Agent bus

Inter-agent messages and human feedback requests

The orchestrator, automatically

Because the two are independent, a problem with your data broker does not disrupt agent-to-agent communication, and vice versa.

How Deployments Reach Your Infrastructure

You never push files to your servers. Instead, the orchestrator periodically checks in with the Control Plane to ask (or pull) what it should run. Then it starts or stops containers to match.

This pull model has two effects:

  • All connections flow outbound from your infrastructure. Orchestrators and agents never open listening ports, so they work behind firewalls and Network Address Translation (NAT).

  • You deploy, update, and remove agents from the Act tab. The orchestrator applies each change on its next check-in.

For the full deploy flow and agent lifecycle states, see Deploy an Agent. To watch deployed agents run, see Monitor Agent Health.