The power of Google Agentspace, and the Supermetrics Agent that will live there, relies on a structured, governed data foundation. This architecture, built around BigQuery and the Looker semantic layer, is what allows agents to move beyond generic responses and make reasonable decisions based on your organization's specific context. Supermetrics plays a critical role in ensuring all your marketing data is available and ready for this advanced ecosystem.
Video tutorial: BigQuery, Looker, and Agentspace use case
Key learnings and action steps
| Feature/Concept | What it does | Your action for preparation |
| BigQuery (data storage) | Acts as the central location for your structured data from all channels and CRM properties. | Standardize data flow: Use Supermetrics to reliably move all your marketing data into BigQuery, ensuring it is ready for analysis and activation. |
| Looker semantic layer (context) | This layer sits atop BigQuery and defines exactly what your metrics (e.g., "sales") and dimensions are within your organization's context. | Define metrics: Ensure your definitions for core metrics (like a "customer" or "revenue") are correctly configured in your Looker model, as this informs the agents. |
| Agentspace (interaction) | Becomes the central interaction vector for anyone working with the data, allowing them to ask complex questions of reasonable complexity. | Leverage the ecosystem: Understand that any data flowing from Supermetrics to Looker/BigQuery is instantly available and contextually rich for Agentspace workflows. |
| A2A framework | The Agent-to-Agent framework allows custom agents built by your developers to interact with out-of-the-box agents. | Developer engagement: Encourage your developers to explore the Agent Development Kit (ADK) to build custom agents tailored to your organization's specific needs. |