AI in CX Enterprise
This guide covers the AI capabilities available across Adobe CX Enterprise applications: generative AI and AI Assistant for product knowledge and operational insights, Agent Orchestrator and Experience Platform Agents for automating jobs, CX Enterprise Coworker for a fully conversational, agent-first experience, and MCP for connecting your own AI tools to CX Enterprise data.
About AI in CX Enterprise
Start here for a primer on where and how AI is used across CX Enterprise:
- Generative AI describes which CX Enterprise applications support generative AI and AI Assistant, and how they compare.
- Agentic AI explains how Experience Platform Agents work in both existing CX Enterprise applications and AI-first applications, and lists the agents available in each.
- Agentic AI monitoring covers the dashboards that track agent adoption, usage, feedback, and AI Credit consumption.
- Agent jobs and AI credit consumption explains how AI Credits are consumed by agent jobs, with estimated consumption rates by agent and job type.
AI Assistant
AI Assistant is a conversational, generative AI tool available in Adobe Experience Platform-based applications. Use it to gain product knowledge, troubleshoot problems, find operational insights, and access Experience Platform Agents, all through natural language prompts in a full-screen or rail view interface.
Read the AI Assistant UI guide to learn how to navigate the interface, and the prompt library for example prompts by agent.
Agent Orchestrator and Experience Platform agents
Agent Orchestrator is the agentic layer that powers Experience Platform Agents. When you ask AI Assistant a question, Agent Orchestrator plans the work, calls on the specialized agents needed to answer it, and returns a unified response, all with human oversight.
The following Experience Platform Agents are documented in this guide:
For the full list of agents, the applications each supports, and eligibility requirements, see Agentic AI in CX Enterprise.
CX Enterprise Coworker
CX Enterprise Coworker is an agent-first evolution of AI Assistant. Instead of asking one question at a time, you describe a goal in natural language, and Coworker plans the work, executes it across your Adobe and connected systems, validates the results, and returns the finished work for your approval. Coworker includes:
- Coworker Chat: A conversational interface for exploring your data, validating audiences and journeys, and completing multi-step tasks across CX Enterprise applications.
- Coworker Campaigns: An AI-native application that consolidates campaign briefing, audience building, content generation, journey design, and proofing into a single conversational experience.
Eligible customers are gradually being transitioned from AI Assistant and Experience Platform Agents to Coworker Chat. Read CX Enterprise Coworker Trial to learn about trial eligibility, AI Credit usage, and how to get access.
To see Coworker Chat in action, walk through Coworker Chat in Playground, or read real-world use cases such as Validate AA to CJA migration data and Analyze conversion drop-off.
MCP
Adobe CX Coworker Gateway is the unified Model Context Protocol (MCP) endpoint for CX Enterprise. It gives MCP-compatible clients, such as Claude, ChatGPT, and Cursor, a single governed connection to the product tools your organization is entitled to use, including Real-Time CDP, Experience Platform, Journey Optimizer, Customer Journey Analytics, and Adobe Analytics.
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Access requirements
Your Adobe Admin must grant the appropriate permissions before you can use AI Assistant and Experience Platform Agents. Requirements vary by application; see Access in the Agent Orchestrator guide for details.
Privacy and security
AI Assistant and Experience Platform Agents are built with privacy, security, and governance at the forefront, including sandbox-specific data isolation and honoring your existing access control policies. For full details, read Privacy, security, and governance in AI Assistant.
Best practices
To get the most value from your AI Assistant or Coworker experience, follow these best practices:
- Be specific in your prompts to obtain targeted and relevant insights.
- Verify responses by reviewing the source citations and reasoning explanations provided.
- Use context setting to make sure the most relevant data sources are used for your questions.
- Provide feedback to help improve performance and accuracy over time.
- Combine insights from multiple agents for a more comprehensive analysis.
Legal considerations
AI Assistant currently supports responses in English only, and language models may occasionally make mistakes. Always verify the information provided, and use the reasoning steps included in each response to understand how it was generated. For full details, read the legal disclaimer.