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Opening the chat

From the Agents list, click on any agent to open its chat interface. The chat is where you interact with your agent in real time — ask questions, explore results, and follow up with more questions.

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Asking questions

Type your question in plain language at the bottom of the screen and press Enter (or click Send). Good questions to start with:
  • “How many sessions did we have last week, broken down by channel?”
  • “Which campaigns had a ROAS above 3 in the past 30 days?”
  • “Show me the top 10 highest-spend ad groups from last month.”
You don’t need to know SQL or the exact table names — the agent figures that out from the context you’ve given it and the data it can access.
If the agent returns unexpected results, try being more specific. For example, instead of “last week”, try “the 7 days ending Sunday 23 March 2025”. Date precision often leads to more accurate queries.

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Reading results

Depending on your question and the agent’s configuration, results can appear in several formats:

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Tables

Data is presented in a scrollable table with sortable columns. Column headers show the field names from your BigQuery data — if these aren’t clear, add descriptions to your agent context.

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Charts

When you ask for a visualisation (or when the agent decides a chart would be more useful), results are rendered as an interactive chart directly in the chat. You can ask the agent to change chart types:
“Can you show that as a line chart instead?”

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Markdown text

For summaries, insights, and explanations, the agent responds in formatted text with bullet points, bold highlights, and recommendations.

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Following up

Every response is a starting point. You can ask follow-up questions that build on the previous result:
“Great — now filter that to campaigns where spend was over €1,000.”
“Can you add a column showing the change vs. the previous period?”
“What’s the average CPA across all those campaigns?”
The agent maintains context within a chat session, so you don’t need to repeat yourself.

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Saving insights

If the agent returns something useful — a metric definition, a schema correction, a calculation rule — you can save it as a learning. Learnings are stored and made available to the agent in future sessions, gradually improving its accuracy over time. To save a learning, click the Save as learning button that appears when you hover over any agent response. Learn more about prompt management and learnings →

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Chat history

Every chat session is stored permanently and accessible from the chat history tab. If the chat was created by a scheduled job, it’s also linked to that job’s run history.

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Tips for better results

Agents use CURRENT_DATE as a reference point. Phrases like “last month”, “this quarter”, and “last 7 days” are interpreted relative to today. If you mean a specific date range, say so explicitly.
If you’re asking about a specific campaign, use the exact name or ID as it appears in BigQuery. You can ask the agent first: “What campaigns are currently active?” to get the exact names.
Complex multi-part questions sometimes produce one part well and miss others. Break big analyses into steps for more reliable results.
If the agent misunderstands something, correct it in plain language: “Actually, the revenue column is in euros, not cents.” Then save that correction as a learning so it doesn’t happen again.