Oracle has taken a decisive step to turn AI agents into a daily tool for marketing, sales, and customer service. The company announced the integration of AI agents by role within Oracle Fusion Cloud Applications, focusing on Customer Experience (CX). The promise is ambitious but concrete: automate processes, analyze connected data, and unlock revenue opportunities without teams having to leave the workflows they already use every day.
The new agents, which run on Oracle Cloud Infrastructure (OCI), are pre-integrated into Fusion and included at no additional cost, according to the company. Being embedded in existing business processes, they do not require users to “switch applications”: they act as co-pilots that suggest, execute, and document steps, with controls to maintain consistency, security, and traceability at an enterprise scale. “AI agents are transforming customer engagement,” stated Chris Leone, EVP of Applications Development at Oracle. With these agents, he argued, “organizations can scale quality experiences to drive more business and keep customers satisfied.”
What is a role-based AI agent, and why does it matter in CX?
In Oracle’s terminology, a role-based agent is a specialized assistant for tasks related to marketing, sales, or service. It’s not a generic chatbot: it understands its use cases, operates on connected data (account profiles, interaction signals, commercial documents), and takes actions within Fusion CX itself (e.g., segmenting an audience, completing a quote, generating a work order). Being native, it “understands” the semantics of suite data and adheres to the governance and permissions set by IT.
The practical result is less friction for the user and more operational consistency for the organization. Most importantly, it enables marketing, sales, and service to work in a coordinated manner using shared signals (interactions, purchases, incidents, contracts) without having to “patch” disparate tools.
What’s new in Marketing: three agents for prioritization, personalization, and qualification
1) Account Product Fit Agent
Goal: help prioritize accounts most likely to buy.
How it works: combines Ideal Customer Profile (ICP), predictive scoring, account data, and interaction signals to flag high-intent clients.
Benefit: allocates resources to accounts with higher conversion potential, accelerating the pipeline without increasing team effort.
2) Buying Group Definition Agent
Goal: personalize by persona with greater precision.
How it works: identifies buying roles by industry and product using a mapping algorithm of titles.
Benefit: strategies that speak directly to the decision-maker (finance, technical, procurement), increasing relevance and response rates.
3) Model Qualification Agent
Goal: better direct and personalize the content.
How it works: recommends the targeted audience using predictive models and verifies if existing data meet the criteria.
Benefit: delivers the right message to the right segment and helps diagnose when data gaps inhibit execution.
What’s new in Sales: from offer advisor to upsell recommendations and quote summaries
1) Deal Advisor Agent
Goal: accelerate closing with expert knowledge at hand.
How it works: provides product and pricing guides, use cases, references, and relevant documents so sellers can share quickly with customers.
Benefit: less search time and more valuable conversations.
2) Quote Assistant Agent
Goal: streamline the quote process.
How it works: responds to questions about the deal and provides actionable info to craft proposals without delay.
Benefit: reduces back-and-forth cycles.
3) Product Recommendations Agent
Goal: identify cross-sell and upsell opportunities.
How it works: analyzes history, preferences, and quotation data to suggest bundles or add-ons.
Benefit: increases average revenue per opportunity.
4) Quote Summaries Agent
Goal: provide instant updates on a deal.
How it works: generates a quote summary with history, key points, and next steps.
Benefit: offers immediate context for prioritization and action.
5) Contract Advisor Agent
Goal: understand a contract without heavy effort.
How it works: provides a summary of obligations and key clauses.
Benefit: reduces legal-operational friction during closing.
6) Lead Advisor Agent
Goal: analyze a lead instantly.
How it works: synthesizes behavior, engagement, profile, and account data, recommending next actions.
Benefit: less time diagnosing, more time engaging with the customer.
What’s new in Service: smart triage, self-service, and ready-to-go work orders
1) Triage Agent
Goal: improve resolution times and team capacity.
How it works: analyzes requests, understands problems, and prioritizes by product, category, severity, and sentiment.
Benefit: fairer queues and easier SLA adherence.
2) Self-Service Agent
Goal: reduce repetitive tasks.
How it works: guides customers step-by-step via web, portal, or mobile to resolve issues independently.
Benefit: frees agents for higher-value incidents.
3) Service Request Creation Agent
Goal: turn conversations into tickets with context.
How it works: creates requests from chats, call transcriptions, or emails with relevant data.
Benefit: reduces copy-paste and info loss.
4) Work Order Agent
Goal: faster dispatch and first-time resolution.
How it works: generates work orders with pre-filled fields (title, notes, type, account, contact).
Benefit: technicians arrive with verified info and improve first visit resolution rate.
5) Service Request Clustering Agent
Goal: detect recurring incidents.
How it works: groups similar requests to reduce duplicates and address root cause.
Benefit: boosts productivity and allows team learning.
6) Escalation Prediction Agent
Goal: prevent escalations.
How it works: evaluates case sentiment and attributes to predict which tickets might escalate.
Benefit: enables timely intervention (e.g., prioritize or assign to an expert).
Native, pre-integrated, and at no additional cost: what it means for IT
Oracle emphasizes that the agents run on OCI and are pre-integrated into Fusion “at no extra charge”. Operationally, this entails:
- Less ad hoc integration: no need to “paste” external AI engines; the agents already understand the objects and rules of Fusion.
- Consistent governance and permissions: agents respect the roles and policies of the tenant.
- Centralized maintenance: updates, model improvements, and guardrails are managed from the suite.
For CIOs and architects, the proposal reduces the shadow IT risk (hidden integrations) and facilitates observability and auditing within the same environment as other corporate applications.
What changes in daily operations? Three quick examples
- B2B Marketing: a team preparing an industry-specific campaign calls upon the Buying Group Definition Agent to map roles (technician, buyer, sponsor). Within minutes, persona-specific segmentation is personalized with actual database data.
- Enterprise Sales: for a complex discount request, the Quote Assistant answers questions about pricing structure, and the Deal Advisor provides relevant use cases and references; the rep quickly prepares a well-argued proposal.
- Field Support: the Triage Agent prioritizes by severity and sentiment; the Work Order Agent fills out the order with validated data. The technician arrives with context, and the customer notices the difference in the first visit.
Integration within the Oracle Fusion Cloud Applications suite
The CX agents complement Oracle Fusion’s overall ecosystem: ERP, HCM, SCM, and CX with integrated AI that enables finance, HR, supply chain, and customer experience to share data, processes, and controls. The goal is well-known: execute faster, make better decisions, and reduce costs by minimizing rework, swivel chair activities, and coordination errors.
Key considerations (and questions to ask in a pilot)
- Data quality and connectors: agents perform best when the source data is clean and connected (interactions, contracts, products).
- Controls and guardrails: policies for PII, rate limits, explainability of recommendations, and action traceability.
- Impact measurement: SLA in service, conversion rate in sales, preparation time in marketing. Without metrics, perception dominates.
- Adoption: train teams on “how to ask” and “how to approve” an agent — this is as important as technical capabilities.
Conclusion: Skilled AI — less “demo,” more process
The announcement does not promise a one-size-fits-all AI, but agents with expertise: marketing that prioritizes and personalizes; sales that argues, responds, and summarizes; service that classifies, anticipates, and dispatches. Embedded in Fusion CX and supported by OCI, these agents aim to scale within the enterprise without adding more tools. If data quality is maintained and adoption is guided by metrics, they can become true levers for efficiency and new revenue streams.
Frequently Asked Questions
Do AI agents incur additional costs in Oracle Fusion CX?
No. Oracle states that agents are pre-integrated into Fusion Applications and run on OCI at no extra charge, operating within the existing flows and permissions.
What data do agents use, and how is it governed?
They operate on connected data within Fusion (accounts, offers, interactions, contracts, cases) and respect tenant roles and policies. Ensuring data quality and aligning permissions / PII are key to maximizing results.
Can I customize which agents to use and where?
Yes. They are embedded in processes for marketing, sales, and service; organizations can activate, configure, and supervise their deployment according to priorities and internal controls.
How can I measure the ROI of these agents in practice?
Define metrics by domain: in marketing, pipeline and response rate; in sales, cycle time and close rate; in service, SLA and first-time fix (FTF). Compare before/after and review data quality and adoption to attribute improvements rigorously.