---
title: "CustomerLake enters Beta: Leading enterprises going live with the Agentic CDP"
description: "Databricks announced CustomerLake’s Campaign Agents moving to Beta and Profile Agents entering Private Preview, expanding access to the Agentic CDP. The platform embeds customer data platform capabilities directly into Databricks, allowing marketers to build audiences using governed data without recreating context in separate tools. Early adopters including HP, Getnet by Santander, AB InBev, and Wine.com reported significant improvements: HP achieved audience building in hours versus months and a potential 40% reduction in total cost of ownership. Campaign Agents uses Databricks’ Genie AI to enable natural language audience creation, while Profile Agents unifies scattered customer data into Customer 360 profiles using deterministic, probabilistic, and LLM-based matching. Philadelphia Union cut audience turnaround time by 90%, and Databricks’ own Data + AI Summit campaign using CustomerLake delivered a 10% conversion lift."
---

# CustomerLake enters Beta: Leading enterprises going live with the Agentic CDP

[Databricks](https://yomu.fyi/company/databricks) · Tasso Argyros, Katy Yuan, Cindy Jiang, Phil Catterall · Oct 20, 2026

**Type:** Announcement

## Summary

Databricks announced CustomerLake’s Campaign Agents moving to Beta and Profile Agents entering Private Preview, expanding access to the Agentic CDP. The platform embeds customer data platform capabilities directly into Databricks, allowing marketers to build audiences using governed data without recreating context in separate tools. Early adopters including HP, Getnet by Santander, AB InBev, and Wine.com reported significant improvements: HP achieved audience building in hours versus months and a potential 40% reduction in total cost of ownership. Campaign Agents uses Databricks’ Genie AI to enable natural language audience creation, while Profile Agents unifies scattered customer data into Customer 360 profiles using deterministic, probabilistic, and LLM-based matching. Philadelphia Union cut audience turnaround time by 90%, and Databricks’ own Data + AI Summit campaign using CustomerLake delivered a 10% conversion lift.

## Context

The rise of AI in marketing demands faster, more personalized customer engagement. Traditional CDPs are slow and costly. Databricks CustomerLake embeds CDP into the existing data platform, using AI agents for audience building and profile unification.

## Approach / What changed

CustomerLake is an Agentic CDP embedded in Databricks. It provides Campaign Agents for natural language audience building via Genie AI, and Profile Agents for unified customer profiles using deterministic, probabilistic, and LLM-based matching. It works with existing Databricks data and governance.

## Takeaways

- HP built audiences in 2 hours with CustomerLake compared to 2-3 months with a traditional CDP, with potential 40% TCO reduction.
- Campaign Agents enables marketers to create audiences in natural language and sync directly to engagement platforms like Braze without engineering support.
- Profile Agents unifies customer data using deterministic, probabilistic, and LLM-based matching with stateful incremental processing and manual override.

## FAQ

### How does CustomerLake reduce CDP total cost of ownership?

CustomerLake works directly with existing Databricks schemas, governance, and data models, eliminating the need to recreate customer context in another platform. HP reported a potential 40% reduction in CDP total cost of ownership.

### How do Campaign Agents work?

Powered by Genie, Campaign Agents let marketers build audiences using natural language based on trusted customer data already in Databricks. Audiences can then be synced directly to engagement platforms like Braze.

### What is Profile Agents?

Profile Agents unify scattered customer information from dozens of sources into Customer 360 profiles directly in Databricks. It uses source normalization, agentic identity resolution (deterministic, probabilistic, LLM-based), and stateful incremental processing.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Customer Data](https://yomu.fyi/topic/customer-data), [Databricks](https://yomu.fyi/topic/databricks), [Marketing](https://yomu.fyi/topic/marketing)

- Source: [Databricks](https://www.databricks.com/blog/customerlake-beta)
- Source URL: https://www.databricks.com/blog/customerlake-beta
- Ingested by Yomu: 2026-10-20T15:00:14.898Z

[Read original post](https://www.databricks.com/blog/customerlake-beta)