The company unveiled a set of role based AI agents inside Oracle Fusion Cloud Customer Experience that promise to take on repetitive tasks, deliver tailored recommendations, and accelerate decisions for marketing, sales, and service leaders.
The rollout signals how enterprise software makers are moving from generic copilots to task oriented agents that live directly in daily workflows.
Oracle said the agents run on Oracle Cloud Infrastructure and are prebuilt into Fusion Cloud, which means customers do not need to stitch together separate tools or train models before getting value.
The company framed the release as an efficiency and revenue play, positioning the agents as a way to cut busywork while nudging teams toward higher probability opportunities.
Chris Leone, executive vice president of Applications Development at Oracle, said in a press release, “The new AI agents in Oracle Fusion Applications help CX leaders deliver personalized support, deepen customer loyalty, and unlock new revenue opportunities with intelligent insights and agentic automation.”
In marketing, agents analyze account fit and buying groups, and assess whether audience data meets the criteria for predictive models.
In sales, agents can produce quick summaries of quotes and contracts, offer deal guidance based on internal references, and point sellers to cross sell and upsell options drawn from customer history.
In service, agents triage tickets, predict escalation risk, and convert chats or transcripts into structured service requests, with a field service agent that drafts work orders so technicians arrive with context.
By bundling agents that target specific jobs to be done, Oracle is betting that line of business teams will see faster time to value than from broad, free form chatbots.
The company also says the agents are natively integrated at no additional cost, an approach that could make adoption easier in tight budget cycles
Oracle’s move leans into defined roles and outcomes rather than open ended prompts, which may help customers measure impact with cleaner baselines such as conversion rates, sales cycle times, first contact resolution, and agent handle times.
The degree to which those metrics improve will depend on data quality, model governance, and how deeply customers align processes to the new automation.
Enterprises weighing adoption should focus on map the agent catalog to current pain points and pick high frequency tasks where automation can be verified quickly.
Ensure clean integration with CRM data and content repositories so that guidance and recommendations are trusted, not generic.
Set guardrails on when humans review suggested actions, especially in regulated sectors or complex deals. Clear ownership and success metrics will matter more than the novelty of agentic workflows.
