AI Agents Now Build 80% of Corporate Databases

AI Agents Are Quietly Revolutionizing Data Infrastructure

A quiet yet profound transformation is underway in the corporate technology world. Recent data shared by cloud computing and data analytics company Databricks reveals that artificial intelligence (AI) agents now handle approximately 80% of corporate database creation processes. This figure is interpreted as the clearest indicator of a rapid shift from a human-centric approach in software development and data engineering toward an AI-assisted and, ultimately, AI-agent-centric model.

Databricks' Lakebase platform is positioned at the very heart of this revolution. The platform enables developers and data teams to use AI agents to build complex data pipelines, design, and manage database schemas. This 'agent-centric' application development paradigm largely automates the traditional line-by-line coding process, allowing data infrastructures to be built faster, more reliably, and with greater scalability.

AI Agents and Data Lifecycle Management

The effective integration of AI agents is transforming not just coding processes but the entire data lifecycle. Next-generation database platforms like Couchbase 8.0 are also optimized for high-performance AI applications. These platforms, working in integration with Large Language Models (LLMs), can manage all stages of data—from collection and processing to storage and querying. This integration helps customers establish reliable AI agent systems while simultaneously reducing system latency, paving the way for real-time applications.

The role of AI agents in database construction is concentrated in the following key areas:

• Schema Design and Optimization: Analyzing business requirements to automatically propose and create the most suitable database schema.
• Data Pipeline (Pipeline) Automation: Designing and implementing end-to-end data workflows that ingest, transform, and load data with minimal manual intervention.
• Performance Tuning: Continuously monitoring and optimizing database queries and resource allocation for peak efficiency.
• Governance and Security: Applying and enforcing data governance policies and security protocols automatically during the database creation and management phases.

This shift signifies more than just automation; it represents a fundamental change in how data assets are conceptualized and built. Data engineers are increasingly becoming orchestrators and validators of AI-driven processes rather than manual coders. The focus is shifting from writing syntax to defining intent, business logic, and governance frameworks that guide the AI agents. This evolution promises to accelerate digital transformation initiatives, reduce time-to-insight, and free up human expertise for higher-level strategic tasks in data architecture and innovation.