Agents Overview

Preview

DEFINE AGENT is a preview feature. It ships in broker 2.22. The syntax and behaviour may still change.

What an agent is

Your broker already moves sensor data, drives equipment, and stores history. Two LoT pieces add a language model to that world. A TYPE LLM connector sends one prompt and returns text. Actions use it for a classification, a summary, or a short conversation. A DEFINE AGENT is a named assistant that runs on that connector. It can read topics, publish, run connector events, and hand work to other agents — only where you grant it. The connector holds the provider, the model, the key, and the price. Every agent that names it with USES LLM CONNECTOR picks up a change to that connector. An agent is its own LoT entity. It is deployed with -addAgent, and it does not appear in the connector list.

Like a phone line and the people who answer it. The TYPE LLM connector is the line to the model. Each DEFINE AGENT is a person you hire, with a job description and a set of keys. CALL LLM dials the line directly. CALL AGENT asks a specific person to do the job.

When to use which

Prompt in, text out — a TYPE LLM connector and CALL LLM inside an Action
An assistant that reads or changes the broker — DEFINE AGENT, then ADD TOOL for each thing it may touch
People and apps chatting over MQTT — WITH INTERACTION_TOPIC on the agent
Hands-off work — CALL AGENT from an Action on a topic or a timer
Several specialists — one LLM connector, several agents, and WITH AGENTS for the peers a supervisor may call

Both need a Growth or Enterprise license, the same row as LLM / Agent on Licensing. On Community, -addAgent is refused. An agent does not consume a connector slot.

Agents and the LLM connector

An agent never talks to a provider directly. USES LLM CONNECTOR names a TYPE LLM connector, and that connector holds the provider (ollama, openai, anthropic, or compatible), the model, the key, and prices. The connector itself never calls tools. The agent adds the job description, the grants, the budgets, and the topics people and Actions use to reach it.

	`TYPE LLM` connector	`DEFINE AGENT`
Deployed with	`-addConnector`	`-addAgent`
Listed by	`-listConnectors`	`-listAgents`
Called from an Action	`CALL LLM`	`CALL AGENT`
Calls tools	No	Only what `ADD TOOL` grants
Counts as a connector	Yes	No

Agents and connectors share one namespace, so an agent can’t take the name of a connector. Deploy order doesn’t matter: an agent whose connector is missing waits in error and comes online when the connector starts.

Agent syntax

DEFINE AGENT, its properties, ADD TOOL, delegation, topics, and CALL AGENT.

Agent examples

A chat agent, a reporting agent, and a supervisor with specialists.

Best practices

Give every agent its own service user

DEFAULT_USER "root" turns every grant into whatever root can do. Create a user that can do the job and nothing wider, and name that user.

Keep keys on the LLM connector

WITH API_KEY GET SECRET "OPENAI_API_KEY". A literal key is copied into every project export.

Grant the narrowest topics that do the job

write_mqtt on reports/line/# with WITH LEVEL "write". A grant of # lets the agent publish anywhere its user can.

Say LEVEL write on anything the agent must do today

A writing grant without LEVEL is refused. Approvals are not available yet.

Name peers in WITH AGENTS

An undeclared peer is not offered to the model.

Watch the stream while you tune grants

Subscribe to $SYS/Coreflux/Agents/<agent>/task/+/stream. Every refusal is a tool_result with an outcome.

Guardrail text steers the model. ADD TOOL and RBAC are the limit. Anything the agent must never do stays outside its grants.

Limits

Limit	Default	When you hit it
`MAX_IN_FLIGHT`	4 per agent	New tasks are `rejected` with `agent_busy`
`MAX_ITERATIONS`	10 per run	Further tool calls are refused with `budget_exhausted`
`MAX_TOKENS`	50 000 per tree	Further tool calls are refused, then the task fails with `budget_exhausted`
`TASK_TIMEOUT`	600 seconds per tree	`deadline_exceeded`
`MAX_CHAIN_DEPTH`	4 hops	Delegation is refused with `hop_limit`
Working memory	256 sessions, 256 KiB, 40 messages	Oldest sessions and turns are dropped

Task work runs on the agent’s own pool, so a slow model does not stall other connectors.

Coming from TYPE AGENT

DEFINE ROUTE … WITH TYPE AGENT and ADD AGENT_CONFIG are gone. -addConnector refuses the block. A connector file that still holds one is skipped at load and the log names the replacement. Nothing is migrated for you. Rewrite each one by hand.

Old `AGENT_CONFIG` setting	Where it goes
`PROVIDER`, `BASE_URL`, `MODEL`, `API_KEY`, `TEMPERATURE`, `MAX_TOKENS`	A `TYPE LLM` connector. `MAX_TOKENS` there caps one completion
`SYSTEM_PROMPT`, `MAX_ITERATIONS`, `DEFAULT_USER`, `INTERACTION_TOPIC`, `DESCRIPTION`	The `DEFINE AGENT`
`BROKER_TOOLS`, `ENABLED_COMMANDS`	`ADD TOOL` grants. Nothing is granted by default
`ACTIONS`	`ADD TOOL "execute_actions"` with `WITH TARGET` for each action and `WITH LEVEL "write"`
`MCP_ROUTES`	No direct replacement. MCP tools are not offered to the model. Fire an event on the MCP connector with `ADD TOOL "execute_routes"` (`WITH TARGET "<mcpConnector>.<event>"`, `WITH LEVEL "write"`) and read that event’s `DESTINATION_TOPIC` with `read_mqtt`
`MODELS`, `VECTOR_STORE`, `HISTORY_MODE`	Dropped. Working memory is per session (`WITH MEMORY`)
`MAX_HISTORY_*`	`WITH MAX_HISTORY_TOKENS` on the agent caps the session history it keeps
`INTERACTION_MODE`, `TOOL_EXECUTION_MODE`, `CONFIRM_TOPIC`, `AGENT_MODE`	Dropped. Confirmation in the broker is a later release. The HUB still asks before a write when the assistant runs in the HUB
`TRACE`, `TRACE_TOPIC`, `DEBUG`, `ARTIFACT_FORMAT`, `TOOL_MODE`	Dropped. Every task streams on `…/task/<id>/stream`

Clients that published to {InteractionTopic}/{agent}/{session}/input and read artifacts or stream move to {INTERACTION_TOPIC}/{session}/ask, …/response, and $SYS/Coreflux/Agents/<agent>/task/<session>/stream. CALL AGENT "<name>.execute" keeps its call shape. The old $SYS/Coreflux/Agents/<name>/Capabilities and …/Metrics topics are not published. Read the card, the status topic, and the task stream.

DEFINE CONNECTOR PlannerLlm WITH TYPE LLM
    ADD LLM_CONFIG
        WITH PROVIDER "anthropic"
        WITH MODEL "claude-sonnet-4-0"
        WITH API_KEY GET SECRET "ANTHROPIC_API_KEY"

DEFINE AGENT Planner
    USES LLM CONNECTOR PlannerLlm
    WITH DEFAULT_USER "agent_svc"
    WITH INTERACTION_TOPIC "factory/agent/planner"
    ADD TOOL "read_lot"

Not available yet

-addAgent refuses these, with a message that names what is missing, so a setting is never silently ignored.

WITH LEVEL "write_confirm" and any writing grant that omits LEVEL
AGENT_MODE "insight" as a default, and TOOL_EXECUTION_MODE "confirm". Per-request "mode": "insight" works today
WITH SKILLS
Shared or long-term memory (MEMORY "shared:…", WITH LONG_TERM_MEMORY)
Collaboration modes other than delegate
EXTERNAL_ACCESS "true"
WITH OUTPUT_MODEL
ADD TOOL "run_lot_once"

Next steps

Creating Agents in Coreflux

Paste a site guide, a scheduled report, alert triage, or a shift handover and try them over MQTT.

AI Connectors

Providers, LLM_CONFIG, and CALL LLM.

Quick Start

Coreflux MQTT Broker

LoT Language

Coreflux HUB

VS Code - LoT Notebooks

Coreflux Portal

Using AI with Coreflux

Product Updates

What an agent is

When to use which

Agents and the LLM connector

Agent syntax

Agent examples

Best practices

Limits

Coming from TYPE AGENT

Not available yet

Next steps

Creating Agents in Coreflux

AI Connectors

​What an agent is

​When to use which

​Agents and the LLM connector

Agent syntax

Agent examples

​Best practices

​Limits

​Coming from TYPE AGENT

​Not available yet

​Next steps

Creating Agents in Coreflux

AI Connectors

What an agent is

When to use which

Agents and the LLM connector

Best practices

Limits

Coming from TYPE AGENT

Not available yet

Next steps