Data agents are here: Choose your path to getting started

Every data team has a backlog that never ends. More dashboards to build, more pipelines to fix, more ad-hoc requests stacking up while strategic work waits. Gartner predicts that by 2027, augmented analytics will become autonomous, managing 20% of business processes. That future is closer than most teams realize, and data agents are how it happens.

What data agents actually do

Data agents don't just answer questions. They take action. A data scientist can hand an agent a single prompt and walk away while it builds a classification model, forecasts next month's web traffic, writes the code, runs the analysis, evaluates the results, and generates the visualizations. At the far end of the spectrum, a multi-agent workflow can produce a complete credit memo with no human input beyond the initial setup.

What separates these systems from standard AI models is their architecture: a reasoning engine that breaks problems into steps, a business context layer that keeps outputs grounded in your actual data, and an orchestration engine that selects the right tools for each step. That combination is what makes autonomous, multi-step work possible.

The technology works. The question is how quickly your team can start using it. Google Cloud offers two paths: pre-built agents you can deploy immediately, or a complete blueprint for building your own.

Pre-built agents you can use now

If your team needs help now, not next quarter, Google Cloud offers fully managed data agents within BigQuery Studio. Each one is built for a specific role on your team and requires no development work.

• Business users and analysts can ask the Conversational Analytics Agent questions in plain language and get answers, charts, and data explorations that reflect your team’s actual business context.

• Data engineers can use the Data Engineering Agent to build pipelines from natural language descriptions, migrate legacy code into modern formats, and troubleshoot broken jobs by analyzing logs automatically.

• Data scientists get an AI partner with the Data Science Agent, built directly into Colab Notebooks. It handles data wrangling, generates exploratory visualizations, and suggests Python or SQL code for model development and evaluation.

Build your own from the ground up

For teams with unique requirements, Google Cloud provides a complete blueprint for custom development. 

• Context: This step involves creating a "business context layer" to bridge raw data with the agent's reasoning engine. By using tools like Dataplex and Looker’s semantic layer to define metadata and relationships, developers ensure agents deliver accurate, grounded responses rather than hallucinations.

• Setup: This phase focuses on configuration using the Agent Development Kit (ADK) for core structure and the Model Context Protocol (MCP) to securely connect agents to external tools and databases without custom integrations.

• Deploy: The guide recommends Vertex AI Agent Engine as a fully managed runtime for handling security and auto-scaling, though Cloud Run is offered as an alternative for teams requiring granular container control.

• Publish: Developers are encouraged to register agents in the Gemini Enterprise Agent Gallery, transforming isolated projects into discoverable, governed assets accessible to the wider organization.

• Analyze: Finally, the chapter emphasizes observability. Using the BigQuery agent analytics plugin and Vertex AI evaluation services, teams can track costs, usage, and performance to continuously refine the agent's quality.

Get started

That backlog isn't going to clear itself, but your team doesn't have to clear it alone either. The Data Agents Guidebook walks through both paths, from deploying a pre-built agent this week to building a custom system from scratch.