How does agentic decisioning work in marketing?

Summary

• Agentic decisioning uses AI agents to decide the next best action for each customer. Databricks describes it as turning customer signals, context, and business strategy into the next best action, offer, or experience.
• Marketers set the goal and the guardrails. Databricks describes marketer-defined limits on eligibility, discount depth, contact frequency, consent, brand standards, and the points where human approval is required.
• Databricks grounds agentic marketing in three kinds of context: customer context, business context, and decision context, which records what a customer already received and how they responded.
• Each action is measured against what would have happened without it, and the result feeds back into the next decision.
• Databricks announced CustomerLake, its agentic customer data platform, in Private Preview on June 16, 2026. Contact your Databricks account team about availability.

How does agentic decisioning work in marketing?

Agentic decisioning uses AI agents to decide the next best action for each customer. The Databricks CustomerLake product page describes it as "agentic decisioning that transforms customer signals, context and business strategy into the next best action, offer or experience." The CDP Institute defines next best action as "the treatment that a business believes will produce the most desirable result for an individual customer; typically based on a combination of rules and predictive analytics." Databricks says Campaign Agents help marketers "move from static, one-off campaigns to always-on engagement."

What happens in the decision loop?

Databricks describes the CustomerLake campaign workflow this way:

1. The marketer defines a goal, such as increasing revenue, growing loyalty enrollment, or reactivating lapsed customers.
2. Campaign Agents use governed context in Databricks to help identify the right audience and to incorporate eligibility or inventory constraints.
3. They recommend the next-best offer or channel.
4. They activate the campaign across destinations.
5. They optimize or suppress based on performance signals.

The agents can draw on customer attributes, behavioral signals, predictive models, eligibility rules, and operational context in Databricks. Databricks calls the result infinity campaigns: continuous, agent-driven engagement loops.
Databricks uses an example scenario of an airline customer whose flight is delayed. By processing and understanding the customer's signals, the Campaign Agent can determine that the best action is complimentary lounge access.

What context does an agent need to decide well?

Databricks defines agentic marketing as using AI agents grounded in trusted customer, business, and decision context to recommend the next best action for each customer, within guardrails that marketers set. It describes the three kinds of context this way:

Context	What it covers
Customer context	Identity, behavior (historical and in-the-moment), preferences, consent status, and lifecycle stage
Business context	Growth objectives, margin implications, brand rules, inventory, and channel constraints
Decision context	The offers and actions a customer has already received, how they responded, and why earlier decisions were made

A Databricks blog post on the agentic CDP describes a similar idea as Golden Context, which adds what the business is trying to accomplish right now and what has already been tried with this customer and how they responded.

How does CustomerLake support each stage?

Stage	What Databricks says CustomerLake does
Ingest	Ingests and transforms batch and real-time customer, campaign, and engagement data with native connectors to marketing tools, advertising platforms, and databases
Unify	Profile Agents prepare data, identify quality issues, and support third-party data enrichment to unify disconnected records. Agentic Identity Resolution combines deterministic, probabilistic, and agentic matching, with LLM and human review workflows for edge cases
Decide	Campaign Agents recommend the next-best offer or channel. Teams can use ML models or deploy custom models through Model Serving with real-time queries directly from the customer profile
Serve	A real-time serving layer for customer attributes personalizes content in real time
Activate	Native managed reverse ETL connectors sync audiences and signals from Databricks to martech, adtech, and customer experience tools
Govern	Governance through Databricks and Unity Catalog gives centralized control over access, lineage, and auditability across customer data, audiences, and AI assets

Where do humans stay in control?

Databricks says humans still define the strategy, goals, and guardrails. In its agentic marketing guidance, the recommended message, offer, timing, and channel "all operate within marketer-defined limits on eligibility, discount depth, contact frequency, consent, brand standards, and the points where human approval is required."
A Databricks blog post on the agentic CDP says that an agentic CDP integrated with the governance layer means every agent action runs under the same data entitlements and security boundaries as every other operation in the enterprise. Marketers can explore customer context and build audiences in natural language through Genie, which reduces the wait for custom data pulls.

How is the loop measured?

Each action is measured against what would have happened without it, and that result feeds back into the context used for the next decision. Databricks ties this to incrementality, which separates the outcomes marketing activity was associated with from the outcomes that changed because the brand acted.

What is the status of CustomerLake?

Databricks announced CustomerLake on June 16, 2026 and said it was available in Private Preview. The launch blog tells interested teams to contact their Databricks account team. The capabilities above are Databricks' description of a Private Preview product, so confirm current availability with your account team before you plan around them.

FAQs

Do marketers need SQL to use CustomerLake?

Databricks says no. CustomerLake gives marketers an agent-first workspace powered by Genie, so they can automate campaigns, ask questions, surface insights, and build audiences in natural language.

Does CustomerLake move or copy customer data?

Databricks says no. CustomerLake is embedded directly in Databricks, so CDP capabilities run where your customer data already lives. Through Lakehouse Federation, teams can access trusted customer data where it resides, including cloud object storage and operational databases.

Does CustomerLake replace my existing martech tools?

Not necessarily, according to Databricks. CustomerLake is open and composable, and designed to work with the marketing, advertising, analytics, and customer experience tools teams already use.