Enterprise AI teams don’t lack ideas—they lack time. Between standing up infrastructure, wiring data sources, enforcing security, and proving compliance, building production-ready AI agents often takes longer than the business will tolerate. Qubika is aiming to close that gap.

The company has announced the public launch of QBricks, a Built on Databricks solution designed to streamline the entire lifecycle of enterprise AI agents, from development and evaluation to deployment and ongoing observability.

Already in use across multiple Qubika client environments, QBricks now enters the market as a centralized accelerator built for scale, compliance, and real-world enterprise constraints.

From Experimentation to Production—Faster

QBricks is positioned as a response to one of the biggest friction points in AI adoption: the amount of undifferentiated work required just to get started. Teams often spend weeks—or months—setting up infrastructure, building connectors, configuring monitoring, and hardening security before an agent ever reaches production.

QBricks abstracts much of that groundwork.

By running natively on the Databricks Data Intelligence Platform, the accelerator allows development teams to focus on agent logic and business outcomes, rather than plumbing. The result, according to Qubika, is dramatically reduced time to value for intelligent agent initiatives.

Built for Enterprise Reality, Not Demos

Unlike many low-code or no-code agent builders that prioritize ease over governance, QBricks is explicitly designed for regulated, enterprise-grade environments.

Key benefits include:

• Secure and compliant by default, adhering to SOC 2, GDPR, and ISO 27001 standards

• Enterprise-grade data privacy, with all data encrypted, access-controlled, and fully contained within the customer’s cloud or preferred infrastructure

• Native Databricks integration, supporting deployment across any major enterprise cloud setup

• No vendor lock-in, with agents delivered as reusable, standalone code that can run on mainstream agent orchestrators outside of Qubika

That last point is especially notable in a market increasingly wary of proprietary AI platforms that are easy to start—but hard to leave.

A Full Agent Lifecycle, in One Place

QBricks isn’t just an infrastructure shortcut. It provides a production-ready agent ecosystem with tooling designed to support long-term operation, not just initial deployment.

Core capabilities include:

• A library of pre-built agents and workflows

• A visual agent workflow builder

• An evaluation framework to test and compare agent performance

• End-to-end observability dashboards for monitoring behavior, performance, and reliability

The platform also includes a curated library of agent templates covering common enterprise use cases such as retrieval-augmented generation (RAG) systems, translation workflows, and API-driven automations—patterns many teams are already building from scratch.

Why Databricks Matters Here

QBricks is built using Databricks Lakebase, Vector Search, and GraphFrames, tying agent behavior directly to the same data platforms enterprises already rely on for analytics and machine learning.

That alignment reflects a growing trend in enterprise AI: agents are no longer standalone tools—they’re becoming extensions of the data platform itself.

Databricks, which now serves more than 20,000 organizations, has been positioning its platform as the foundation for analytics, AI applications, and agent-based systems. QBricks effectively layers enterprise-ready agent acceleration on top of that foundation.

Differentiation in a Crowded Agent Market

The AI agent ecosystem is rapidly filling with tools promising faster builds and easier workflows. According to Sebastian Diaz, SVP of Data & AI at Qubika, QBricks’ differentiation comes down to data-native design and portability.

“The key differentiator of QBricks compared to other low-code/no-code AI workflow builders is that it has native data integration with data platforms and external data sources,” Diaz said. “We ensure that the agents developed are fully portable and our clients can continue to manage, deploy, and evolve them completely independently.”

That emphasis on portability and independence directly addresses a growing enterprise concern: how to scale AI initiatives without surrendering architectural control.

The Bigger Signal for Enterprise AI

QBricks’ launch reflects a broader shift in enterprise AI adoption. As organizations move beyond pilots, they’re demanding platforms that deliver:

• Governance and compliance out of the box

• Deep integration with existing data estates

• Observability and evaluation at scale

• Freedom from vendor lock-in

In that context, accelerators like QBricks are becoming less about speed alone—and more about making AI operationally sustainable.

For enterprises building intelligent agents that must live inside complex, regulated environments, that sustainability may matter more than novelty.

Get in touch with our MarTech Experts.