Adobe bets on agentic AI to rewrite SaaS for customer experience

Consumer engagement has been fundamentally changing with the advent of AI agents, forcing a rethink by software-as-a-service (SaaS) companies, and creativity platform provider Adobe is responding by shifting its approach to what it calls ‘Customer Experience Orchestration (CXO).’

Announced today at Adobe Summit, the new Adobe CX Enterprise suite is a pivot to a future defined by agents rather than by software alone, where SaaS companies claim an advantage based on their deep domain expertise and troves of first and third-party data.

The platform brings together customizable and out-of-the-box AI agents, Model Context Protocol (MCP) endpoints, and new intelligence systems built on Adobe’s orchestration engine.

“SaaS is changing, and we are re-architecting so that we can participate in the reimagination, the redefinition of SaaS,” said Adobe VP Sundeep Parsa.

Agents executing with guidance from a ‘coach’

Adobe CX Enterprise builds on the company’s Adobe Experience Platform (AEP) Agent Orchestrator, which brought AI agents directly into Adobe apps. Released in 2025, AEP now  powers more 1 trillion experiences annually, according to the company.

AEP remains the “anchor” for Adobe CX Enterprise, which now gives customers the ability to create agent skills (reusable instructions), as well as providing specialized and customizable agents. These can be incorporated into any AI tech stack, including Anthropic’s Claude, OpenAI’s ChatGPT, Google’s Gemini, Microsoft Copilot, Nvidia’s NemoClaw, and others. Developers also have access to Model Context Protocol (MCP) servers and other infrastructure required to build customized use cases.

“We’re going to make sure our applications are not trapped inside our UI layer, that they become composable services available through MCP tool calls or the A2A layer,” Parsa explained. “Customers can tap into what they have and bring that into their own unique processes, be their own UI.”

He emphasized the importance of customer choice. Many enterprises are still grappling with the ‘build or buy’ question; some will prefer to create their own bespoke user interface (UI) layer, while others will have no interest in doing so.

With CX Enterprise, enterprises can use pre-loaded agent skills to build custom workflows, or can launch agents pre-built for specific tasks like workflow optimization (coordinating tasks or automating handoffs) and brand governance (enforcing policies, managing permissions, tracking asset rights). And, a new Adobe CX Enterprise Coworker, to be available in the coming months, will act on specified goals and orchestrate other agents to perform multi-step actions.

For instance, if a marketing team is looking to increase loyalty subscriptions by 3% in the next quarter, the CX Enterprise Coworker will work with other agents to identify relevant audience segments, surface performance insights, create a plan, and develop email copy or visual assets, Parsa noted. Once all this is approved by a human, the Coworker will then help execute the campaign and monitor results.

Whereas previously agents would build an audience, then “go to sleep,” Adobe’s new CX Enterprise Coworker is “always on,” has persistent memory, and can run workflows across weeks, or even full financial quarters if required, Parsa explained. He likened the CX Enterprise Coworker to an American football quarterback, the player who directs the activities on the field, guided by a coach on the sidelines. Coworker’s coach is a marketer or a brand specialist.

“We’re doubling down on this framing of customer experience orchestration,” Parsa says.

Moving to one-on-one personalization

Along with these agentic tools, Adobe is introducing two new intelligence systems: Adobe Brand Intelligence and Adobe Engagement Intelligence.

Brand Intelligence is built on a fine-tuned large language model (LLM) with vision-language capabilities that learns from “qualitative and nuanced inputs” like annotations, feedback cycles, or rejected assets.

“Brand intelligence is going after a much harder problem than ‘a brand kit,’ which is a codification of a CSS style guide,” Parsa explained. The LLM can begin to understand brand sentiment, informed by “data engagement signals and the actual enterprise assets.”

Adobe Engagement Intelligence helps teams decide next best offers, messages, or other actions for targeted customers. This is based on their lifetime interactions, rather than click-throughs or conversions, according to Parsa.

Whereas previously, less was more, “in this world, more is better,” he said, pointing out that the promise of generative AI is producing more material economically. “It’s not creating more for more’s sake, it’s targeted campaigns that get you much closer to one-on-one personalization.”

Early production gains are “massive,” Parsa claimed. This is because troubleshooting and early detection of problems now takes “hours, not days and weeks.”

SaaS companies’ data advantage

Like many SaaS companies grappling with an agent-driven future where pay-per-seat models are becoming less relevant, Adobe is emphasizing its data advantage. Parsa pointed out that more than 20,000 enterprises have built on Adobe’s platform over the years, giving the company enormous amounts of data alongside domain expertise.

Generative AI and AI agents do a good job of understanding the “corpus of world knowledge” and building some “useful capabilities for all of us,” Parsa acknowledged. “But these technologies stop at the enterprise walls, because those are ‘walled gardens.’”

Further, enterprise context is very complicated and spread across numerous applications, he noted. “It’s codified in documents; in some cases just tribal knowledge informs how people function on a day to day basis.” AI agents working on their own (like OpenClaw or Claude Cowork) break in the enterprise because they are “brittle” and not grounded in enterprise data, he said.

“We are a proxy for all of the enterprise context that lives inside our applications,” said Parsa. “We’re going to bring that into the AI layer much faster than a customer restarting that whole process with an AI platform.”

Ultimately, he said, Adobe is “adapting and adjusting” to customer feedback and consumer interaction with brands, as well as with the internet itself, as customer engagement undergoes a dramatic shift in the era of AI. As this unfolds, Parsa emphasized the importance of “open, open, open.”

“We absolutely are going to work with tech partners, we’re going to work with other SaaS companies to make sure that we stay flexible and meet the customer where they are,” he said.