Creating Agents in Coreflux

Turn Your Broker Into a Teammate — and Your Automations Into Thinkers

Your devices, integrations, and databases already run on Coreflux — whether you monitor a factory, a solar site, a building portfolio, or a mobile fleet. AI agents live in LoT on that same broker: no separate AI platform, no copy-paste into another chat app. Talk with live data and automate with judgment. Teams ask in plain language and get answers from what the broker sees right now. Your Actions already react to MQTT and timers; with agents they can also reason on a schedule or when an event fires — a status digest, a plain-language take on an alarm, a handover brief — all through CALL AGENT in LoT.

What you gain	Example (any vertical)
Ask the system, don’t only chart it	”Which zones are offline?” · “What’s output at site B?” · “Any inverters under target?”
Smarter automations, not more scripts	A timer or topic wakes an agent; LoT publishes the result — no babysitting a workflow
Reports while you sleep	Hourly or daily digest on MQTT for supervisors, facility managers, or NOC staff
Specialists, not one overloaded chatbot	A guide for people, a reporter on a schedule, an analyst when alarms fire — each with its own job

You define agents once in LoT (alongside your Actions and Routes). Below are ready-to-use use cases you can paste and deploy — pick one to prove value in minutes, or combine several on the same broker. Full options — multi-agent teams, MCP tools, safety rails — are in AI Routes.

Like a supervisor on the floor and a analyst on the line. People get answers when they ask; your automations get judgment when something changes — all on the broker you already run.

Ready-to-Use Use Cases (Copy & Deploy)

Each tab is a full recipe: agent Route, LoT Action where needed, and MQTT topics to try. Adapt names to your vertical — examples in the table.

Use case	Who benefits	How it runs
Talk with your data	Operators, support, NOC	Ask on `agents/site/ask`, read `agents/site/reply`
Automatic report	Supervisors, managers	Scheduled summary on `reports/operations/hourly`
Alert triage	On-call, maintenance	Each alert gets a short AI explanation
Shift handover	Shift leads, crews	Plain-language brief before the next rotation

Some examples of use-cases for your industry:

Vertical	Typical data topics	Automatic report might cover
Manufacturing	`production/`, `lines/`, `quality/`	Throughput, downtime, batch issues
Solar & energy	`solar/`, `inverters/`, `grid/`	Generation, underperformance, curtailment
Smart buildings	`buildings/`, `hvac/`, `occupancy/`	Comfort, energy, fault patterns
Fleet & logistics	`fleet/`, `vehicles/`, `routes/`	Utilisation, delays, asset health

Before you start

A running broker — Installation and Getting Started if you are new
A model provider — OpenAI, Anthropic, Mistral, or local Ollama on your network. Every recipe in the tabs below uses OpenAI; swap PROVIDER, MODEL, and the secret name if you prefer another cloud provider or edge inference (see Picking a Provider). For these examples, store an OpenAI API key once on the broker:

KEEP SECRET "OPENAI_KEY" WITH "sk-..."

The HUB AI Assistant (Coreflux icon in the dock) is a separate built-in helper for drafting LoT and dashboards. This guide is about your agents — the ones you own in LoT.

Talk with your data
Automatic report
Alert triage
Shift handover

Outcome: Anyone with MQTT access can ask questions; answers use live broker data (topics, Routes, recent payloads).Paste into the LoT Editor (Routes, then Actions) or your notebook:

DEFINE ROUTE SiteGuide WITH TYPE AGENT
    ADD AGENT_CONFIG
        WITH PROVIDER "openai"
        WITH MODEL "gpt-5.5"
        WITH API_KEY GET SECRET "OPENAI_KEY"
        WITH BROKER_TOOLS "true"
        WITH INTERACTION_MODE "interactive"
        WITH INTERACTION_TOPIC "agents/site/interact"
        WITH SYSTEM_PROMPT "You are a site operations guide. Use broker tools to answer from live MQTT data for this deployment. Be concise."

DEFINE ACTION AskSiteGuide
ON TOPIC "agents/site/ask" DO
    CALL AGENT "SiteGuide.execute"
        WITH (task = PAYLOAD)
        RETURN AS {reply}

    PUBLISH TOPIC "agents/site/reply" WITH {reply}

Deploy and confirm

In HUB Routes, check SiteGuide (AGENT). In Actions, check AskSiteGuide. Both should show healthy.

Ask a real question

In MQTT Explorer or HUB Data Viewer, publish to agents/site/ask and subscribe to agents/site/reply. Try a question that fits your data, for example:

Which sensors have been updated in the last few minutes?

What is the latest data on telemetry/zone1/status?

(Rename telemetry/ to production/, solar/, buildings/, or your own tree.)

Read the answer on reply

The reply topic carries the agent’s answer — grounded in what your broker actually sees.

You just talked with your data. No extra AI server — the agent runs where your MQTT already lives.

Outcome: LoT automation with AI — on a schedule, an Action calls the agent and publishes an operations summary to MQTT. No chat window.

DEFINE ROUTE OperationsReporter WITH TYPE AGENT
    ADD AGENT_CONFIG
        WITH PROVIDER "openai"
        WITH MODEL "gpt-5.4-mini"
        WITH API_KEY GET SECRET "OPENAI_KEY"
        WITH BROKER_TOOLS "true"
        WITH INTERACTION_MODE "autonomous"
        WITH SYSTEM_PROMPT "You write operations reports from live MQTT. Use broker tools on telemetry/ and related topics. Bullet points: current status, anomalies, items needing follow-up. Plain language for managers. Adapt wording to what the data represents (production, energy, facilities, fleet, etc.)."

DEFINE ACTION HourlyOperationsReport
ON EVERY 1 HOUR DO
    CALL AGENT "OperationsReporter.execute"
        WITH (task = "Hourly operations report: summarise key signals from telemetry/ topics, flag anything off-norm or stale, note follow-ups before the next review period.", use_history = "false")
        RETURN AS {report}

    PUBLISH TOPIC "reports/operations/hourly" WITH {report}

Deploy Route and Action

Add OperationsReporter and HourlyOperationsReport in the LoT Editor. No public chat line — only the schedule triggers this agent.

Subscribe to the report

Subscribe to reports/operations/hourly in MQTT Explorer, a dashboard, or the HUB Data Viewer.

See the first report

Publish sample payloads under your data tree (telemetry/, solar/site1/power, buildings/tower/hvac, etc.), then wait for the next run — or temporarily use ON EVERY 30 SECONDS and change it back when done.

Scheduled insight on autopilot — the report speaks your operation’s language, not broker diagnostics.

Outcome: When something hits your alerts topic, the broker adds a plain-language triage note — what it might mean and what to check first.Works for any alert source: equipment faults, threshold breaches, connectivity, security events.

DEFINE ROUTE AlertAnalyst WITH TYPE AGENT
    ADD AGENT_CONFIG
        WITH PROVIDER "openai"
        WITH MODEL "gpt-5.4-mini"
        WITH API_KEY GET SECRET "OPENAI_KEY"
        WITH BROKER_TOOLS "true"
        WITH INTERACTION_MODE "autonomous"
        WITH SYSTEM_PROMPT "You triage operational alerts from MQTT. In 3 short sentences: what happened, likely cause, recommended first check. Be direct."

DEFINE ACTION TriageAlert
ON TOPIC "alerts/#" DO
    CALL AGENT "AlertAnalyst.execute"
        WITH (task = "Alert on topic " + TOPIC + ": " + PAYLOAD, use_history = "false")
        RETURN AS {triage}
    PUBLISH TOPIC "agents/site/alerts/analysis" WITH {triage}

Wire your alerts

Ensure your existing logic still publishes under alerts/ — for example alerts/inverter/fault, alerts/building/leak, or alerts/line2/quality.

Deploy triage

Add AlertAnalyst and TriageAlert. Subscribe to agents/site/alerts/analysis.

Trigger once

Publish a test alert to alerts/test/manual with any message. Read the analysis on the triage topic.

On-call gets context with the alarm, not only a raw payload.

Outcome: Before each rotation, a handover brief lands on MQTT — what changed, what looks odd, what the incoming team should watch. Common in manufacturing, utilities, and 24/7 operations centers.

DEFINE ROUTE HandoverBrief WITH TYPE AGENT
    ADD AGENT_CONFIG
        WITH PROVIDER "openai"
        WITH MODEL "gpt-5.5"
        WITH API_KEY GET SECRET "OPENAI_KEY"
        WITH BROKER_TOOLS "true"
        WITH INTERACTION_MODE "autonomous"
        WITH SYSTEM_PROMPT "You write handover notes for operations teams. Use broker tools. Bullet points: status, anomalies, open items. Max 8 bullets."

DEFINE ACTION ShiftHandover
ON EVERY 8 HOURS DO
    CALL AGENT "HandoverBrief.execute"
        WITH (task = "Prepare handover for the next shift: current operational status, active alarms, and anything unusual in the last 8 hours.", use_history = "false")
        RETURN AS {brief}
    PUBLISH TOPIC "agents/site/shift/handover" WITH {brief}

Deploy

Add HandoverBrief and ShiftHandover. Subscribe to agents/site/shift/handover.

Test faster

Temporarily use ON EVERY 2 MINUTES, read one brief, then restore ON EVERY 8 HOURS for production.

Crew change becomes a story, not a scramble through dashboards.

Starter pack: SiteGuide + HourlyOperationsReport + AlertAnalyst — ask your deployment, get scheduled insight, enrich every alarm. Rename topics to production/, solar/, or buildings/ when you copy the prompts.

When You’re Ready to Grow

You already have the building blocks: chat (ask/reply topics), schedules (ON EVERY), and event-driven (ON TOPIC) automations with CALL AGENT. Next level is tuning models per job, adding MCP Routes for Slack or databases, and naming agents by role so LoT stays readable. For multi-agent patterns (operator, specialist, watchdog, reader), see Multi-Agent Deployments.

Stay Safe While You Experiment

Try read-only first

Add WITH AGENT_MODE "insight" on a Route so it can explore but not publish or trigger downstream equipment. Switch to full mode when you trust the prompt.

Keep API keys off the clipboard in LoT files

Always use GET SECRET "OPENAI_KEY" — never paste keys into Route definitions that get saved or shared.

Match the agent to the audience

Chat for people; timed Actions for machines. Do not put a public chat line on agents that should only run in the background.

What to Explore Next

AI Routes (full reference)

Providers, MCP tools, multi-agent teams, and deployment settings when you outgrow this tutorial.

Using AI with Coreflux

Use MCP in your editor to write LoT faster — separate from agents running on the broker.

Quick Start

Developing and Managing Coreflux

Coreflux MQTT Broker

LoT Language

Product Updates

Turn Your Broker Into a Teammate — and Your Automations Into Thinkers

Ready-to-Use Use Cases (Copy & Deploy)

Before you start

When You’re Ready to Grow

Stay Safe While You Experiment

What to Explore Next

AI Routes (full reference)

Using AI with Coreflux

​Turn Your Broker Into a Teammate — and Your Automations Into Thinkers

​Ready-to-Use Use Cases (Copy & Deploy)

​Before you start

​When You’re Ready to Grow

​Stay Safe While You Experiment

​What to Explore Next

AI Routes (full reference)

Using AI with Coreflux

Turn Your Broker Into a Teammate — and Your Automations Into Thinkers

Ready-to-Use Use Cases (Copy & Deploy)

Before you start

When You’re Ready to Grow

Stay Safe While You Experiment

What to Explore Next