Source URL: https://cloud.google.com/blog/products/data-analytics/ai-based-forecasting-and-analytics-in-bigquery-via-mcp-and-adk/
Source: Cloud Blog
Title: Forecasts and data insights come to BigQuery’s MCP and Agent Development Kit tools
Feedly Summary: For AI agents to be really useful, they need to be able to securely interact with enterprise data. In July, we introduced a toolset to help AI agents interact with and analyze business data in BigQuery through natural language, and with just a few lines of code. Today, we’re taking the next step, with “Ask data insights” for Conversational Analytics and the “BigQuery Forecast” for time-series predictions, going beyond fetching metadata and executing queries to full-scale data analysis and predictions. Both tools are available today in the MCP Toolbox as well as Agent Development Kit’s built-in toolset.
Let’s dive into what you can do with these new tools.ask_data_insights: Converse with BigQueryWith the ask_data_insights tool, you can now answer complex questions of your structured data in BigQuery using plain English.Built on top of the powerful Conversational Analytics API, ask_data_insights enables an agent to utilize the API to offload the task of understanding the user’s question, pulling in relevant context, formulating and executing the queries, and summarizing the answer in plain English. Along the way, the ask_data_insights tool shows its work, returning a detailed step-by-step log of its process, so you have full transparency into how it arrived at the answer.Predict the future with BigQuery ForecastInformation without insights is just noise. The ability to predict future trends, whether sales, user traffic, or inventory needs, is critical for any business. BigQuery Forecast simplifies time-series forecasting using BigQuery ML’s AI.FORECAST function based on the built-in TimesFM model.With BigQuery Forecast, the agent can run the forecasting job directly within BigQuery, without you having to set up machine learning infrastructure. Point the tool at your data, specify what you want to predict and a time horizon, and the agent will make its predictions using TimesFM.New tools in action: Building a Google Analytics Data AgentLet’s explore how to build a simple agent to answer questions about Google Analytics 360 data using ask_data_insights and BigQuery Forecast. For this demo,The data is stored in BigQuery tables. Users of this agent only require read access to these tables, which are available under the BigQuery public dataset. bigquery-public-data.google_analytics_sample.We will use ADK to build this agent and “adk web" to test it.We are using one tool from the ADK’s built-in tools and one from the MCP toolbox. You can choose to use either option depending on your agent architecture and needs.This diagram shows the architecture of this simple agent:
And here is the agent code:
code_block
Using the agent code above, let’s turn to the ADK’s developer UI, i.e., adk web, to test the agent and see it in action.First, let’s use the tools to understand our data…
Agent using the insights tool to summarize the data
Then, let’s see if the agent can answer a business question.
The Conversational Analytics API backend is equipped with deeper thinking, and is able to bring out rich insights.
As you can see above, the Conversational Analytics API is equipped with the ability to perform deep thinking, so it can provide rich insights into our question.Now, let’s see if the agent can predict the future.
Short answer, yes, yes it can, with a 95% confidence level. With these tools, the power of the TimesFM model is finally available to business users, regardless of their technical skill level.Bring analysis and forecasts to your dataThese new BigQuery capabilities will help developers reimagine how they build data-driven applications and agents. Together, we believe the combination of AI-powered Conversational Analytics and powerful, built-in forecasting capabilities will make performing sophisticated data analysis easier than ever.Learn more about the ask_data_insights and BigQuery Forecast tools in the MCP Toolbox for databases and the core Agent Development Kit.
AI Summary and Description: Yes
Summary: The text discusses new tools released for AI agents to enhance their capability to interact with enterprise data securely. The introduction of “Ask data insights” for Conversational Analytics and “BigQuery Forecast” for time-series predictions marks a significant step forward in enabling AI agents to conduct complex data analyses and predictions in an accessible manner.
Detailed Description:
The provided text focuses on the advancements in AI agents used for interacting with enterprise data through Google’s BigQuery platform. This innovation integrates advanced analytics capabilities into AI, aiming to simplify the user experience and expand accessibility for businesses. Key points include:
– **Tool Introductions**:
– **Ask Data Insights**: An AI tool designed to allow users to pose complex queries in natural language. It leverages the Conversational Analytics API to:
– Understand user questions.
– Pull relevant data for analysis.
– Execute appropriate queries.
– Provide detailed logs of the process for transparency.
– **BigQuery Forecast**: A time-series predictive analytics tool integrated into BigQuery, which allows users to:
– Conduct forecasting tasks with minimal setup.
– Utilize the TimesFM model for predictions.
– Apply time-series forecasting directly within BigQuery, making predictions about future trends in sales, user traffic, etc.
– **Operational Workflow**:
– Developers can use the Agent Development Kit (ADK) and MCP toolbox to build and test data agents.
– The example illustrates building a Google Analytics data agent that retrieves data, performs analyses, and answers business queries seamlessly.
– **Impact on Business Users**:
– The accessibility of sophisticated analytics tools provides a significant advantage to businesses, allowing users with varying technical skills to gain insights without complex setups.
– The goal is to facilitate a more intuitive interaction with data, enabling better decision-making through enhanced predictive capabilities.
– **Future Directions**:
– The combination of AI-powered insights and time-series forecasting is expected to empower developers to create more advanced data-driven applications.
This text is crucial for security and compliance professionals as it highlights the secure interaction of AI systems with sensitive enterprise data, offering insights into how organizations can leverage AI for improved data analysis while maintaining transparency and security in data handling practices.