Configure the orchestrator, subagents, tools, and model overrides that power your agent.
The Agent Builder tab is the core of your agent configuration. It shows a visual canvas of how your agent is structured — the orchestrator at the top, connected to one or more subagents below.Click any node on the canvas to open its settings panel on the right.
Each node represents a component of your agent. The orchestrator receives every user message first, decides what needs to happen, then delegates tasks to its subagents. Results flow back up to the orchestrator, which assembles the final response.
The system prompt tells the orchestrator who it is, what it does, and how it should behave. Write this in plain language — the model interprets it at the start of every conversation.A good system prompt includes:
The agent’s role and purpose
Which markets, brands, or data domains it covers
Key metrics and how they’re defined
Any formatting preferences (e.g. “always include a summary row”, “use tables for comparisons”)
Escalation rules (e.g. “if ROAS drops below 2, flag it as a concern”)
Example:
You are a paid search analyst for the Dutch and Belgian markets. Your job is to analyse Google Ads performance using the BigQuery connector. Always present campaign data with dimensions by market, campaign, and time period. Define ROAS as revenue divided by ad spend. Highlight campaigns where CPA exceeds the target set in the campaign name.
The more specific your system prompt, the less the agent needs to guess. Every business rule you define here is one less thing you need to repeat in your questions.
By default, the orchestrator uses the model selected in the General tab. To use a different model for this component specifically, select one from the Model override dropdown.The orchestrator handles coordination and reasoning, so a capable model like Claude Sonnet works well here.
Subagents are specialists that the orchestrator calls to perform specific tasks. The subagents available to you depend on your agent type.
Database Query Agent
What it does: Generates and runs SQL queries against your BigQuery connector.When to use it: Any time your agent needs to retrieve data from your warehouse.
Data Analysis Agent
What it does: Interprets query results — computing summaries, identifying trends, formatting charts.When to use it: When you want the agent to reason about the data, not just return raw rows.
Like the orchestrator, each subagent has its own system prompt. For most agents, you can leave this blank — the subagent’s built-in instructions are already optimised. Override only if you have specific requirements, such as:
Restricting which tables the Database Query Agent should query
Telling the Data Analysis Agent to always format numbers in a specific currency
The same context options — organisation context and global context — are available per subagent. Enable them to give the subagent the same background knowledge as the orchestrator.
Tools extend what a subagent can produce. Each subagent has a set of tools you can enable or disable. Enabled tools appear as options the agent can use when answering a question.
Database Query Agent — available tools (Multi-Agent Swarm only)
Tool
What it does
Data sheet
Returns query results as a formatted sheet directly from the query agent
Hand off to Data Analysis
Passes results to the Data Analysis Agent for further interpretation
Data evaluator
Validates query results before returning them
Skills
Allows the agent to use saved learnings from previous sessions
Save learning
Saves observations about the data schema or query patterns
In the Data Analysis Agent type, the Database Query Agent’s tools are fixed — it always runs metadata lookup, data exploration, and SQL queries internally. Tool selection only applies to the orchestrator in this mode.
