Adobe Summit Updates: Marketo’s Shift to Agentic AI

Adobe is repositioning Marketo from a campaign execution and lead management platform to a more AI-native, agent-driven platform. Below are the key summit updates and what they mean in practice.

1. Marketo + MCP Server

Marketo is introducing a native MCP (Model Context Protocol) Server that enables you to securely connect your instance to enterprise AI platforms such as Claude, Copilot, and other MCP-compatible AI systems.

This means:

• You can build custom AI agents on top of your own Marketo data
• Your data remains within your environment (no external exposure)
• Marketo becomes part of your broader enterprise AI ecosystem
• Cutting down the constant switching between tools

This essentially opens up Marketo for AI innovation without compromising security or governance.

2. Callable Agents

Marketo is moving toward an agent-first model where AI doesn’t just assist, it executes.

These agents can:

• Run within Smart Campaigns as callable steps
• Automate repetitive operational tasks
• Identify data gaps and support normalization of data
• Support campaign execution at scale
• Spotting bad data patterns early, before they impact campaigns
• Bringing CRM and engagement data together to guide next steps

A key use case here is real-time data standardization. Callable agents can run via webhooks inside Smart Campaign flow steps, so when a lead comes in, the agent can normalize fields like job title, company name, and phone number before the record even reaches Salesforce or routing.

Instead of manually building and maintaining everything, teams can rely on agents to handle repeatable work.

3. Agent-First Conversational Experience (AI Marketing Engine)

This is one of the more meaningful shifts in how Marketo will be used day to day.

Marketo is moving toward a conversational, agent-driven experience where much of the manual work MOPs teams handle today can be done through simple prompts.

In practice, this means you can:

• Turn a rough brief into a structured campaign plan
• QA programs against multiple rules automatically
• Clean and normalize data without manual intervention
• Suggesting campaign improvements based on what’s working
• Import and manage leads through conversational inputs
• Customizability of skills through markdown files for Agent First Conversational and Callable Agents

It’s built around real, everyday workflows and removes the need to navigate multiple layers of UI to get things done. This makes Marketo more intuitive, faster to operate, and far less dependent on manual execution. Extensibility and Customizability are key to adapting the agents to customers' organization's needs.

AI Assistant + Refreshed UX

Alongside these changes, Marketo is also improving usability and accessibility:

• AI assistant to help users quickly find answers, troubleshoot, and navigate Marketo
• Refreshed UX that simplifies navigation and reduces friction in everyday tasks

What This Means for Customers

From a Marketing Ops and business perspective, the impact is quite significant:

1. Increased Efficiency: Repetitive tasks like campaign builds, QA, and data cleanup are automated, freeing up team bandwidth.
2. Faster Campaign Execution: Campaigns move from idea to launch much faster with AI handling planning and validation.
3. Improved Data Quality: Continuous cleaning and normalization reduce dependency on periodic data hygiene efforts.
4. Better Scalability: Teams can manage more campaigns and complexity without needing to scale headcount at the same rate.
5. Future-Ready Stack: With MCP and open integrations, Marketo fits more naturally into broader enterprise AI initiatives.