Google wants you to be a citizen data scientist

For more than a decade, enterprise teams bought into the promise of business intelligence platforms delivering “decision-making at the speed of thought.” But most discovered the opposite: slow-moving data pipelines, dashboards that gathered dust, and analysts stuck in time-consuming prep work.

Now, Google Cloud thinks it has the fix. It’s investing heavily in AI agents to finally close the gap between data insights and real-world decisions. These tools are designed to work behind the scenes, letting non-technical users ask questions and get real answers fast. It’s a shift that could redefine data jobs across industries, pushing analysts toward more strategic roles as AI takes on the grunt work.

At the Cloud Next Tokyo conference, Google unveiled a wave of specialized AI agents under its agentic AI initiative on the Google Cloud Platform (GCP), designed to streamline data engineering, automate scientific workflows, and empower developers and business users to analyze data using plain English prompts.

Richard Seroter, senior director and chief evangelist at Google Cloud, says the company envisions a future where AI agents are deeply embedded within enterprise systems, assisting with data analysis while leaving strategic decision-making to people. These agents, he explains, are designed to be “a powerful and empowering layer of a company’s enterprise platform, with humans in the loop.”

Among the six new agentic capabilities across GCP is a BigQuery AI Query Engine, which infuses generative AI directly into SQL to simplify query creation; a Gemini CLI GitHub Actions agent that integrates seamlessly into developer workflows; and a Data Science Agent that can clean, analyze, and visualize data—all within a notebook environment.

Seroter points to a common challenge among enterprise customers: the difficulty of integrating data accumulated over decades. As organizations build around long-standing systems of record, their data often ends up fragmented, unstructured, or trapped behind various APIs, making it harder to access and utilize effectively.

But the real productivity gains, he says, come when agentic AI is put in the hands of the person closest to the problem, whether that’s a marketing manager, customer support lead, or UX designer. “These people can now prototype and even deploy a solution faster than a multi-month [or year] development cycle.”

The company also announced the regional availability of Gemini 2.5 Flash for in-region machine learning processing in Japan, with expanded support in Australia, India, Singapore, South Korea, Canada, and the U.K. In addition, the new Looker MCP Server joins the Model Context Protocol (MCP) toolbox to streamline database orchestration.

Is the Age of the Citizen Data Scientist Here?

Google Cloud’s new Data Engineering Agent for BigQuery uses natural language to automate the entire pipeline creation process, from ingesting a CSV to cleansing columns and joining tables. It launches alongside a Spanner Migration Agent (currently in preview), an AI-powered service designed to simplify migrations from legacy systems like MySQL.

Likewise, the Data Science Agent can transform BigQuery notebooks into AI-powered labs. Users can ask it to analyze customer churn, and it will autonomously run exploratory data analysis, generate features, build models, and interpret the results.

For nontechnical users, a Conversational Analytics Agent comes equipped with a Code Interpreter. Powered by Gemini’s reasoning capabilities and developed with Google DeepMind, the interpreter simplifies querying to simple English questions. Users can ask, “What were my top-selling products last month?” or “What were my sales last quarter?” and receive detailed responses—complete with Python code and visualizations—without writing a single line of SQL.

Gemini CLI GitHub Actions also brings agentic AI directly into the developer workflow. Engineers can mention “@gemini-cli” in any issue or pull requests to delegate tasks. The upgraded CLI can write tests, implement suggestions, brainstorm alternative solutions, or fix well-defined bugs on demand.

Ryan J. Salva, a senior product director at Google Cloud, says developers appreciate having both targeted code review agents and more flexible, general-purpose ones. He expects more niche agents to emerge as the ecosystem grows. “Some tasks are better solved by an agent with access to unique problem-solving capabilities, specialized data, or models,” he tells Fast Company.

Since the launch of Gemini Code Assist for GitHub in February 2025, Salva says several major tech companies have seen significant improvements. Capgemini reported a notable increase in coding speed (up by 31%), Turing saw higher technical productivity and quicker delivery of quality output (30%), and Quantiphi experienced a substantial boost in overall developer efficiency (30%).

What makes this launch more than just a feature drop is the backend infrastructure powering it. Google Cloud has upgraded BigQuery and AlloyDB with autonomous vector indexing, hybrid semantic and keyword search, and adaptive filtering. The technologies are built on the same tech stack that supports Google Search and YouTube Ads. 

“We have built an intelligent system that understands how to efficiently prepare, index, and serve vector data at a petabyte scale. It’s the same systems-level thinking we use to index the web, now applied to corporate data,” says Yasmeen Ahmad, managing director of StratOps and outbound product management at Google Cloud. “Serving the right YouTube ad in milliseconds is a massive, real-time vector search problem. We’ve taken that same low-latency, high-throughput infrastructure and built it into AlloyDB.”

Under the hood, the Model Context Protocol (MCP) ensures that agents can securely interact with tools like GitHub, Looker, and custom APIs.

The Agentic AI Arms Race: Google vs. Everyone

With this launch, Google is making a bold play in the enterprise agentic AI race, aiming to leapfrog Snowflake, Databricks, and Microsoft by moving beyond code-generating assistants to full-fledged agents that can replace entire layers of routine data work.

“Microsoft has a commanding lead in enterprise integration with its Copilot ecosystem, Databricks and Snowflake are strong in data-centric pipelines, but Google’s strength lies in end-to-end orchestration and model tooling, not just data prep,” says Arnie Bellini, the managing partner at Bellini Capital. He believes the differentiator will be whether Google can translate its developer-first tools into scalable, secure enterprise systems. “That’s where Microsoft currently holds the edge,” he adds.

While rivals focus on lakehouse unification and governed agentic orchestration, Google claims its edge is how deeply its agents are embedded in its core cloud infrastructure.

“We’re not locking developers into a specific IDE or data lake. Because we know developers work with many tools and services in the course of a day, we aspire to build tools that are both open and extensible,” says Salva. He believes the company is empowering developers to build and evolve their entire engineering systems using natural language.

“We’re building collaboratively through open source projects like Gemini CLI and Agent2Agent,” he adds. “By working in the open, we not only increase transparency, but also invite developers to help shape the future of dev tools.”

Ahmad adds that Google’s aim is to transform the entire data toolchain into an intelligent, conversational interface, not to merely augment existing tools.

Ahmad explains that, unlike other tools that primarily convert plain-language questions into SQL, Google’s new Code Interpreter is designed for more advanced analytical tasks, such as forecasting and running what-if scenarios. “Crucially, this isn’t happening in a vacuum,” she says. “The future of data isn’t about moving all your data to one central mega-lake. It’s about a logically unified, but physically distributed, data ecosystem.”

As part of this push, the company also announced new Oracle Cloud Infrastructure (OCI) locations in Japan, enabling in-region support for mission-critical applications in Tokyo, with Osaka support expected by early 2026. Ahmad notes that for many enterprises, particularly in regulated sectors like finance and healthcare, data residency is a legal and operational requirement. “The new Oracle locations in Japan are a direct response to this.”

But Craig Le Clair, vice president and principal analyst at Forrester Research, says Google still lags behind other platforms in comprehensive data support—critical for broad enterprise adoption.

“They also lack a strong action component to be able to build real business patterns that would provide ROI,” he adds, noting that many current AI agent deployments resemble basic assistants rather than fully autonomous systems. “At this point, these efforts seem focused on search-oriented use cases, which again lack a strong action component and are unlikely to drive significant business value,” he says.

Agentic AI Has Promise, But It Lacks Magic

While agentic AI excites enterprises, many experts remain skeptical of its maturity. Although some organizations report double-digit efficiency gains, impact varies widely based on use case and implementation.

“Most agent deployments are single agents handling specific task requests. The ROI heavily depends on good integration, training, and aligning the agents with actual workflows,” says Jim Hare, vice president & analyst at Gartner. “So while measurable gains are real, it’s not automatic as it requires the right groundwork to unlock the value.”

He notes that true enterprise-grade maturity—with robust scalability, compliance, and security—is only beginning to emerge. Most current offerings are either developer-heavy tool kits requiring customization or narrowly scoped point solutions.

“The big players like AWS, OpenAI, Microsoft, Google, Anthropic are actively building out platforms and ecosystems but broad deployment in production settings remains limited and use-case specific,” says Hare.

Data governance also remains a concern. As agents gain more autonomy, enterprises will need stronger safeguards to prevent hallucinations or unintended actions. To address these risks, Google includes features like Workload Identity Federation and command allowlisting.

“Our tools have native logging and monitoring to give you a clear audit trail of an agent’s actions and reasoning,” says Seroter. “We are also focused on building a secure-by-design environment with strong identity and access management. This allows enterprises to give agents ‘just enough’ permission to do their job without becoming a security risk.”

Experts caution that we’re still early in the maturity curve. As Bellini puts it, “think of it like a modern air traffic control system. It’s not about building the best plane, but coordinating the entire flight path, securing it, and landing safely at scale.”

A Redefinition of Digital Labor

The agentic AI era signals a deeper shift in digital labor. As agents evolve from generating insights to initiating action, Google’s message is clear: AI agents aren’t novelties—they’re infrastructure. Hype aside, one thing is certain: the future of data work won’t be entirely human.

“In our view, humans are the strategic orchestrators and architects of a fleet of highly specialized agents,” says Seroter. “The goal is to give organizations the confidence to move forward, knowing that they can trust the systems they’re building and have a clear ‘human-in-the-loop’ process for validation and oversight.”

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