Multi-agent

Every Cube deployment ships with a single agent by default — see the Overview for the standard configuration. For more advanced setups, you can configure multiple agents within the same deployment, each with its own model, accessible views, rules, and certified queries. Start with one agent, and read When to use multiple agents before adding more.

agents/config.yml is the source of truth for spaces and agents: declaring them there and reconciling is the only supported way to create either. Agents can’t be created in the UI, and their settings are read-only there. Cube links every record it creates to the entry it came from, and matches them by that entry’s name.

When to use multiple agents

Add an agent only when users need it to behave differently in a way one agent can’t vary per user. Split by audience, not by subject area. Multiple agents fit when they answer questions about the same data for audiences that need a different voice or model — for example, an agent for store managers that answers sales questions in plain business terms, and an agent for the analytics team, on a different model, that names measures and shows SQL. Rules apply to everyone using a space and can’t branch on who is asking, so one agent can’t speak two ways. Give each of these agents its own space for its tone rules, and attach the rules and certified queries they both need to each space. Don’t split one data model into subject-area agents — a finance agent and a marketing agent for the same users. Someone who asks about both has to pick the right agent for every question, and so does any client calling Cube for them: an MCP client chooses the agent per request from its name, and uses the default agent when it names none. Use one agent instead:

Scope data with access policies. The agent runs with the permissions of the user it operates under, so access policies decide what it can see and return.
Add subject-area knowledge as agent_requested rules, which the agent pulls in only when a question needs them.

Subject-area agents in separate spaces make sense only when their audiences don’t overlap and their rules, certified queries, and memories must stay apart.

Don’t rely on an agent for security. accessible_views, rules, and the choice of agent guide what the agent does; they don’t restrict data. A rule such as “always filter this view by region” is an instruction the agent can miss — enforce it with an access policy.

To benchmark each agent, run an eval once per agent, choosing it under Agent.

Architecture

A multi-agent setup introduces one new concept on top of the single-agent model: spaces.

A space belongs to the account and is used by one or more deployments — see Space scope.
A space is an isolated context. It owns its rules, certified queries, and memories — they are not shared across spaces.
Each agent belongs to exactly one space and inherits everything that space owns. Multiple agents can live in the same space and share the same rules, certified queries, and memories.

This means you choose a space’s boundary based on what context should be shared. Two agents serving the same team usually live in one space; agents that need different rules — a different tone for a different audience, or context that must stay apart — live in different spaces. In the single-agent setup, there is an implicit auto space that holds all rules, certified queries, and memories — you don’t need to think about it. In a multi-agent setup, you define spaces explicitly and attach rules and certified queries to specific spaces.

What changes from the single-agent setup

The agents/ file structure is the same. What’s different is how agents/config.yml is shaped:

Agents are defined as an array. Each agent gets a unique name and an optional description, in addition to the standard agent properties (llm, runtime, accessible_views, memory_mode, etc.).
Spaces are introduced. A spaces array defines the contexts agents operate in. Each space gets a unique name.
Rules and certified queries attach to spaces. Use the space property in the frontmatter of each rule or certified query Markdown file to attach it to a specific space.

You cannot mix flat root-level properties (the single-agent style) with spaces or agents arrays in the same file. Use one style per file.

Agents

Replace the flat root-level agent properties with an agents array:

# agents/config.yml
agents:
  - name: sales-assistant                   # Required
    description: "AI assistant for sales analytics"
    space: sales-analytics                  # Required: reference to a space
    llm: claude_4_6_sonnet
    accessible_views:
      - orders_view
      - customers_view
    memory_mode: user

  - name: marketing-analyst                 # Required
    description: "AI assistant for marketing analytics"
    space: marketing-analytics              # Required
    llm: gpt_5

The properties available on each agent are the same as in the single-agent setup, plus:

Property	Type	Required	Description
`name`	string	Yes	Unique identifier for the agent.
`description`	string	No	Human-readable description.
`space`	string	Yes	Name of the space this agent belongs to.

Spaces

A space is the context an agent operates in. Spaces own the rules, certified queries, and memories that the agents inside them share. Define spaces alongside agents:

# agents/config.yml
spaces:
  - name: sales-analytics                   # Required
    description: "Space for sales team analytics and reporting"

  - name: marketing-analytics               # Required
    description: "Space for marketing team analytics"

Property	Type	Required	Description
`name`	string	Yes	Unique identifier for the space.
`description`	string	No	Human-readable description.

Each agent must reference exactly one space via its space property. Multiple agents can share the same space and inherit its rules, certified queries, and memories.

Reconciliation

A space or agent declared in agents/config.yml needs a matching record in Cube before users can chat with it. Cube creates those records from your config — that step is reconciliation:

Declare the space and the agent in YAML

Add the spaces: and agents: entries to agents/config.yml. In dev mode the pending list reflects your branch, so you can reconcile before merging.

Open the pending configurations

Cube compares the config with the records already available to that deployment, matching each entry by name. Entries with no record yet are listed under Pending Configurations on the Agents page and on Agents → Spaces, once you pick the deployment. The Semantic Model IDE also shows a Reconcile agent configs button with the pending count that links there.

Create them

Choose the space scope and press Create All. Spaces are created first, then each agent is linked to the space its space property names.

Reconciliation only creates missing spaces and agents, so you need it only when an entry has no record available to the deployment yet: after adding a name to the config, and on each deployment you reconcile for the first time — an agent belongs to one deployment, as does a space created per deployment, while a global space counts as created everywhere. Agent behavior — llm, description, accessible_views, memory_mode, rules, certified queries — is read from the agents/ directory of the deployment’s data model and takes effect without reconciling. The implicit auto space and agent of the single-agent setup are never listed as pending. An agent’s space is the exception: the link is made when the agent is created, so changing it in YAML doesn’t move an existing agent. The agent’s page flags the mismatch between the space its config names and the space it is linked to. If the newly named space has no record yet, it appears under Pending Configurations, and Create All creates it and moves the agent onto it in the same action. If that space is already available to the deployment, nothing about the agent is pending and Create All won’t move it — delete the agent so its config reads as pending again, then Create All recreates it in the space the config names. The recreated agent is a new agent, so chats from before the delete don’t carry over to it.

Changing an entry’s name reads as a new entry: reconciling creates a new space or agent, and the record created from the old name stays behind with everything tied to it — an agent’s chats, a space’s memories — flagged (misconfigured) because its config no longer exists. Delete it from the Agents or Spaces page once you no longer need it.

Space scope

Spaces live at the account level, so one space can be used by agents in more than one deployment. When a space is created, you choose its scope:

Global — one space that agents in every deployment can use.
Per deployment — the space is available to a single deployment only.

You pick the scope when you reconcile: the Create All panel’s Create Spaces control offers Global and Per deployment. You can change the scope of an existing space later on its page, under Agents → Spaces. Spaces created per deployment are named after the deployment, for example Product (production) and Product (staging). Only the displayed name changes — the space stays linked to the spaces: entry it was created from, so the YAML entry keeps matching. Choose per-deployment scope when the same spaces: entry is declared in several deployments — typically development, staging, and production fed from branches of one data model — and you don’t want them sharing what the space stores.

Creating the spaces again does not split a space that was created as global: it already counts as created for every deployment, so Create All creates nothing new. To split one, re-scope it to a deployment on its space page, then run Create All for the other deployments.

Spaces created before this option existed have no scope stored. Cube infers the deployment they belong to from the agents linked to them, and the Spaces list shows them as Not scoped. Set the scope explicitly on the space page to make it definite.

Scope affects storage, not configuration

Scope never changes where configuration comes from. Agents always belong to a single deployment, and the spaces: and agents: entries, rules, and certified queries that shape an agent are read from the agents/ directory of the data model of that deployment, on that deployment’s branch. A global space does not merge the configuration of the deployments that use it. What a global space shares is storage: the space itself and the data held against it — memories in particular. Two deployments using the same global space read and write the same memories, while each of them still applies its own agents/ configuration.

Attaching rules and certified queries to a space

In the single-agent setup, rules and certified queries belong to the implicit auto space. In a multi-agent setup, you must attach each rule and certified query to a specific space using the space property in the Markdown frontmatter:

<!-- agents/rules/fiscal-year.md -->
---
space: sales-analytics
type: always
---
Always use fiscal year starting April 1st when analyzing dates.

<!-- agents/certified_queries/quarterly-revenue.md -->
---
space: sales-analytics
description: "Apply when the user asks about quarterly revenue"
user_request: "What is the revenue by quarter?"
---
SELECT
  DATE_TRUNC('quarter', order_date) AS quarter,
  SUM(amount) AS revenue
FROM orders
WHERE status != 'cancelled'
GROUP BY 1
ORDER BY 1

You can also organize rules and certified queries into space-named subdirectories. Files placed under agents/rules/<space-name>/ or agents/certified_queries/<space-name>/ are attached to that space automatically — no space frontmatter required.

Complete example

# agents/config.yml
spaces:
  - name: sales-analytics
    description: "Space for sales team analytics and reporting"

  - name: marketing-analytics
    description: "Space for marketing team analytics"

agents:
  - name: sales-assistant
    description: "AI assistant for sales analytics and reporting"
    space: sales-analytics
    llm: claude_4_6_sonnet
    accessible_views:
      - orders_view
      - customers_view
      - products_view
    memory_mode: user

  - name: marketing-analyst
    description: "AI assistant for marketing analytics"
    space: marketing-analytics
    llm: gpt_5
    memory_mode: user

Connect to data

Users & Permissions

SSO & Identity Providers

Deployment

Monitoring

AI

Account

When to use multiple agents

Architecture

What changes from the single-agent setup

Agents

Spaces

Reconciliation

Space scope

Scope affects storage, not configuration

Attaching rules and certified queries to a space

Complete example

​When to use multiple agents

​Architecture

​What changes from the single-agent setup

​Agents

​Spaces

​Reconciliation

​Space scope

​Scope affects storage, not configuration

​Attaching rules and certified queries to a space

​Complete example

When to use multiple agents

Architecture

What changes from the single-agent setup

Agents

Spaces

Reconciliation

Space scope

Scope affects storage, not configuration

Attaching rules and certified queries to a space

Complete example