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Adobe and Databricks Debut Agentic Marketing Ecosystem

agentic marketing ecosystem

Adobe and Databricks have introduced a new agentic marketing ecosystem, according to a joint announcement at Adobe Summit 2026 in Las Vegas. The system automates complex customer experience workflows through autonomous AI agents. The partnership centers on the Adobe CX Enterprise Coworker, a tool designed to orchestrate tasks across enterprise data environments. This collaboration provides 20,000 enterprise customers with a governed framework for deploying AI agents that query and act upon operational data in real-time.

The system utilizes Databricks Delta Sharing to enable zero-copy access to first-party data within the Adobe Experience Platform (AEP). This architecture eliminates traditional ETL (Extract, Transform, Load) processes. Marketers can access governed data instantly. The integration leverages the Model Context Protocol (MCP) to facilitate bidirectional communication between the Adobe Marketing Agent and Databricks Genie. This ensures AI tools can call specific data sets and functions across both platforms.

Governance and Data Access in the Agentic Marketing Ecosystem

The agentic marketing ecosystem is built upon the NVIDIA Agent Toolkit and the OpenShell runtime to meet the security needs of regulated industries. These technologies provide governance and oversight for autonomous workflows. This is critical for the Fortune 100 companies that comprise 99% of Adobe's AI user base. Adobe stated that its Experience Platform currently powers more than 1 trillion global experiences annually, highlighting the scale of these agentic workflows.

This move signals a shift from passive AI assistants to active coworkers that execute multi-step marketing strategies with minimal human intervention. A beta version of the Adobe Marketing Agent is scheduled for release on the Databricks Marketplace. This allows developers to test the bidirectional integration. For decision-makers, this represents a move toward zero-copy architectures that reduce data latency and improve the accuracy of AI-driven customer interactions.

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