Marketers have spent the last decade stitching together audiences, dashboards, and media plans across disconnected tools. LiveRamp thinks AI agents can finally do that stitching automatically.

LiveRamp (NYSE: RAMP) has introduced a new suite of agentic AI capabilities designed to transform how brands plan, execute, measure, and optimize campaigns. The update includes agent-powered access to the LiveRamp platform, enabling specialized AI agents to autonomously collaborate with ecosystem partners inside a governed environment.

In short: what marketers currently do manually—building audiences, running experiments, measuring cross-media performance—AI agents can now handle at machine speed.

From Workflow Chaos to Governed Automation

LiveRamp’s pitch is straightforward. Marketing execution is still too manual and fragmented. Even in sophisticated enterprises, audience creation, measurement, and optimization often involve multiple teams, exports, and reconciled reports.

“We're making it possible for AI agents to do what marketers have been doing manually — build audiences, measure cross-media performance, and optimize spend — but faster and within the governed environment our customers already trust,” said Matt Karasick, Chief Product Officer at LiveRamp.

The company says customers can activate these capabilities in alignment with their internal AI policies and the usage rules of the underlying AI providers—an increasingly important consideration as enterprises formalize AI governance frameworks.

Agents Go Live: Newton Research and SemantIQ

The first wave of live agent integrations includes:

• Newton Research, which enables marketers to query LiveRamp’s Cross-Media Intelligence platform using natural language to unlock instant measurement insights.

• SemantIQ, allowing health and life sciences marketers to build and activate healthcare provider audiences directly from the LiveRamp Clean Room.

These agents operate within LiveRamp’s identity and data collaboration framework, meaning marketers don’t have to move sensitive data outside controlled environments to benefit from AI automation.

John Hoctor, CEO and co-founder of Newton Research, framed the collaboration as a way to translate analytics into “quantifiable performance increases” through specialized intelligent agents.

LiveRamp says additional agent partnerships across audience planning, segmentation, optimization, and measurement categories are in development.

Identity as the Agent’s Foundation

The real differentiator in LiveRamp’s agentic strategy may not be the agents themselves—but the identity infrastructure underneath them.

LiveRamp has long positioned its identity graph as the connective tissue across media channels. Now, that foundation becomes the operating system for AI agents.

Among the new capabilities:

• Enhanced AI-powered lookalike modeling leveraging first-, second-, and third-party data

• The ability to apply a single, identity-powered control group across channels for consistent performance measurement

• Faster experimentation management with scalable, agent-assisted execution

This unified control group approach addresses a long-standing marketer pain point: inconsistent measurement across platforms. If AI agents can standardize experimentation frameworks across surfaces, cross-channel incrementality becomes easier to measure—and defend in budget conversations.

Lookalike Modeling, Minus the Friction

Lookalike audiences are hardly new. But marketers often struggle with data fragmentation, workflow complexity, and activation hurdles.

Ananda Chakravarty, Research VP for Retail Insights at IDC, noted that friction typically stems from three areas: data access, workflow integration, and connectivity.

LiveRamp’s update attempts to address all three—sourcing from multiple data tiers, adapting to marketer workflows, and ensuring seamless activation across partners.

For retail media networks and large advertisers, that could streamline precision targeting without adding operational burden.

Austin Leonard of DG Media Network and Thomas Atkins of MGM Resorts International both highlighted the role of identity and data security in building higher-quality AI models—underscoring how privacy-safe collaboration is becoming foundational rather than optional.

The Bigger Trend: Agentic Marketing Platforms

LiveRamp’s move aligns with a broader industry shift toward “agentic” systems—AI tools that don’t just generate insights but take action autonomously.

Instead of dashboards requiring human interpretation, agentic systems can:

• Build and refine audience segments

• Launch and adjust experiments

• Reallocate media budgets

• Generate performance summaries in natural language

The promise is exponential performance gains through automation. The risk, of course, lies in governance and transparency—especially when agents operate across premium data environments.

LiveRamp is betting that its clean room architecture and identity controls make it a safer home for AI-driven execution than fragmented adtech stacks.

Competitive Implications

As retail media networks, DSPs, and data clean rooms integrate AI layers into their platforms, the competition is shifting from raw data access to intelligent orchestration.

If agents can operate across LiveRamp’s partner ecosystem with governed permissions, the platform could evolve from a data collaboration hub into an autonomous marketing command center.

For enterprise marketers, the value proposition is clear: fewer manual handoffs, faster optimization cycles, and consistent cross-channel measurement—without compromising privacy compliance.

Whether agents fully replace traditional workflows remains to be seen. But the trajectory is clear.

Marketing is moving from assisted intelligence to autonomous execution. LiveRamp just handed the keys to the agents.

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