Adobe used its Summit conference, held April 20–22 in Las Vegas, to rebrand Experience Cloud as Adobe CX Enterprise and reorganize the entire stack around AI agents. The headline product is the CX Enterprise Coworker, which Adobe describes as a persistent agent that runs continuously, learns from outcomes, and is triggered by signals or schedules rather than one-shot prompts. Adobe also moved more than ten previously previewed agents — covering site optimization, audience creation, journey orchestration, experimentation, and content optimization — into general availability and put 1,770-plus customers on a credit-based pricing model.
The interesting technical move is on interoperability. Adobe added Model Context Protocol endpoints across the platform and shipped reference architectures for Microsoft Copilot, ChatGPT Enterprise, Claude Cowork, and Gemini Enterprise — meaning Adobe's data and skills can be called as tools from inside any of those agent environments. That is a meaningful concession. A year ago, the dominant enterprise pattern was 'pick one agent vendor and standardize on its tools.' MCP, which Anthropic released as an open spec in late 2024, is becoming the connective tissue that lets large software vendors expose capabilities to whatever agent the customer happens to be using.
Strategically, this is Adobe responding to a real threat. As soon as Copilot, ChatGPT, Claude, and Gemini can read your CRM, write your campaigns, and analyze your funnels, the value of a separate marketing-cloud UI starts to erode. Reframing the platform as a set of agents and skills — addressable from inside the buyer's preferred AI environment — is how Adobe stays in the workflow even when the workflow is no longer Adobe's app. Salesforce and Microsoft are pushing similar plays with Agentforce and Copilot Studio.
For learners: the abbreviation worth knowing here is MCP. Model Context Protocol is becoming the de-facto way to give an AI agent access to a tool, a database, or a service. If you work in software, getting hands-on with a small MCP server — a few hours with the spec and a sample repo — is one of the highest-leverage things you can do this year. It is the layer where 'AI agent' stops being a demo and starts being something a business actually uses.