Treasure Data Launches Treasure Code for Agentic CDP Operations

As customer data platforms become increasingly complex and central to modern marketing operations, organizations are seeking new ways to automate workflows and reduce operational overhead. Treasure Data’s launch of Treasure Code introduces an agentic AI driven approach designed to transform how enterprises manage customer data operations within the CDP ecosystem.

Treasure Data announced the general availability of Treasure Code, an AI native command line interface that enables technical teams and AI agents to operate the Treasure Data Intelligent Customer Data Platform entirely as code. The company says the release brings DevOps principles and automation practices into customer data management, allowing organizations to streamline workflows and scale operations more efficiently.

The new interface is designed to address growing operational complexity as brands manage hundreds of millions of customer profiles and process trillions of data points across marketing systems. According to Treasure Data, traditional manual workflows slow innovation and increase risk, particularly as organizations attempt to integrate artificial intelligence into data operations.

“We’ve never seen such organic adoption from an AI-native product at Treasure Data, with over a quarter of our customers embracing Treasure Code in a matter of days,” said Rafa Flores, Chief Product Officer at Treasure Data. “Our customers deal with incredible complexity and scale operating a CDP, often with upwards of hundreds of millions of profiles and trillions of data points processed. Treasure Code reduces the operational burden of data operations, enabling brands to manage a CDP with fewer resources and free teams up for strategic, high-impact work.”

Treasure Code is built for data engineers, platform teams, marketing operations professionals, and advanced CDP users who require programmatic control across customer data workflows and AI systems. The platform introduces an AI assisted command environment that allows users to automate data processes, configure customer journeys, and manage CDP infrastructure through natural language inputs supported by Claude Code.

By combining AI driven iteration with human verification, the system enables teams to rapidly build and modify workflows while maintaining governance and operational oversight. Users can execute complex tasks using conversational instructions rather than traditional SQL queries or command line syntax, reducing technical barriers and accelerating deployment cycles.

The platform also introduces code grade governance features, allowing CDP configurations to be version controlled, peer reviewed, and rolled back instantly when necessary. Treasure Data says this approach consolidates fragmented operational tools into a unified command layer that automates development to production workflows in seconds.

Early adoption has been rapid, with approximately one quarter of Treasure Data’s customer base already using Treasure Code for technical operations shortly after release.

“With Treasure Code, it feels like we’ve added a legion of data engineers to the team. I can describe what I want in plain language and get production-ready SQL, segments, and workflows in minutes.” said Tomohiko Sugiura, Executive Vice President, Dentsu Digital.

“Treasure Code is a transformative feature that fundamentally changes how we implement and operate our CDP. Historically, constructing data pipelines and workflows required highly specialized technical skills. With Treasure Code, however, we can now execute these tasks rapidly and easily using natural language. This allows even non-experts to maintain high-quality implementations, and we are eager to actively leverage this capability to accelerate our business.” said Hideo Ota, Senior Vice President & Executive Officer, NTT Integration.

By introducing agentic automation into customer data operations, Treasure Code reflects a broader industry shift toward AI native infrastructure that simplifies enterprise data management while enabling faster experimentation, improved governance, and scalable marketing intelligence.