Oracle unveils new role-based AI Agents across Fusion Cloud Applications and supply chain

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Oracle is pushing its enterprise cloud platform further by rolling out a new wave of AI-driven capabilities and role-based Agents designed to sit natively inside Oracle Fusion Cloud Applications. Rather than treating artificial intelligence as a bolt-on, the company is weaving these agents directly into core business workflows so that planning, operations and customer-facing teams can work with more context, less manual effort and faster feedback loops.

With this update, Oracle is targeting two fronts at once: on one side, it is tightening control and visibility over complex supply chain and manufacturing processes; on the other, it is embedding intelligent, task-focused agents into marketing, sales and service functions to turn traditionally reactive processes into proactive, data-aware journeys.

Process manufacturing gets smarter in Oracle Fusion Cloud SCM

Within Oracle Fusion Cloud Supply Chain & Manufacturing (SCM), the company is introducing a set of new tools tailored for process manufacturers, particularly those that work by blending ingredients such as food and beverage producers or chemical companies. The goal is to raise the bar on production control, quality management and regulatory compliance while keeping everything traceable from raw material to finished product.

These enhancements are meant to help organizations maintain consistent quality at scale, fine-tune production in near real time and keep regulators satisfied by providing clear, auditable records across every step in the supply chain. Instead of juggling spreadsheets or isolated systems, production teams can see ingredients, batches and outputs flowing through one integrated cloud environment.

One of the key additions is advanced recipe and yield management. Manufacturers can align formulas, recipes and batch definitions with AI-assisted what‑if scenarios, using simulated changes to understand how adjustments might affect yield or cost before touching the shop floor. The system can also model operational performance to track process losses and batch quantities, giving planners a clearer picture of where material or efficiency is being left on the table.

When it comes to batches, SCM now supports defining acceptable size ranges that the system can use for automatic recipe selection, along with tracking intermediate inputs and outputs. This helps ensure that, as demand fluctuates, the right combination of recipes and batch sizes is chosen without constant human intervention, reducing waste and misalignment between planning and execution.

On the execution side, the platform can now sequence materials within operations, recover coproducts and by‑products and offer more fine‑grained material traceability. That includes lot‑specific unit-of-measure conversions, capturing lot grades, preventing the use of expired lots and automatically calculating product expiration dates based on the shelf life of underlying ingredients. For industries with strict safety and labeling requirements, that level of detail can be a key differentiator.

New AI Agents for marketing, sales and service in Fusion Applications

Alongside the SCM improvements, Oracle is rolling out a portfolio of prebuilt, role-based AI Agents for customer experience teams working in marketing, sales and service. These agents are delivered as part of Oracle Fusion Cloud Applications and are integrated out of the box, so organizations do not have to build every workflow from scratch or manage additional licensing.

The idea is to help commercial teams accelerate everyday processes by having agents that can surface unified data, automate repetitive tasks and provide predictive insights directly inside the tools people already use. Instead of spending time collecting information from multiple systems, users can rely on the agents to bring key data points to them at the right moment.

For marketing departments, the new agents cover functions such as Program Planning, Program Summary and Program Orchestration, helping teams coordinate campaigns from initial design to execution and post‑campaign review. Additional agents focus on Purchase Groups, Customer Insights and Audience Analytics, allowing marketers to refine segmentation and understand which audiences are responding best to specific tactics.

There are also agents specialising in creative support, including Copywriting and Image Selection helpers that can suggest messaging options and pick visuals aligned with campaign goals. While marketers still keep control over brand voice and final approvals, these agents are meant to reduce the time spent drafting variations or hunting for suitable images.

On the sales side, Oracle is introducing agents for Contact Intelligence, Quote Generation, Renewal management and territory-focused support. These are designed to help salespeople prioritize outreach, build quotes more quickly and spot expansion or renewal opportunities that might otherwise be buried in CRM records. By analyzing interaction histories and pipeline data, the agents can suggest where to focus next and flag deals that may need attention.

Customer service teams are getting their own set of agents as well, with capabilities around work order scheduling, customer self‑service and automated processing of attachments. The goal is to raise first‑contact resolution rates and improve efficiency in support centers by routing tasks more intelligently and extracting relevant information from documents without manual data entry.

For organizations that want to go further, Oracle is also pointing to AI Agent Studio for Fusion Applications, where customers and partners can build custom AI Agents tailored to their specific use cases. This allows them to extend beyond the prepackaged options and design specialized workflows for niche processes or industry‑specific requirements.

AI Agents for supply chain planning, procurement and logistics

Beyond customer-facing operations, Oracle is also adding a broad set of supply chain-focused AI Agents that aim to automate workflows and speed decision-making across planning, procurement, manufacturing, maintenance and logistics. These agents run on Oracle Cloud Infrastructure and, like the others, are offered as preconfigured capabilities without additional cost.

On the planning and sourcing side, there is a Planning Cycle Agent designed to coordinate planning tasks automatically, reducing manual handoffs between stakeholders. A Component Replacement Agent can manage component substitutions when parts are unavailable or phased out, while a Planning Measures Expression Agent helps create planning measures and key performance indicators to track how well the supply plan is performing.

Another notable addition is the Autonomous Sourcing Agent, which can run competitive sourcing events with suppliers. By automating portions of the bidding process, it aims to shorten sourcing cycles and standardize how organizations evaluate offers, potentially reducing procurement risk and improving cost outcomes.

In manufacturing and maintenance, dedicated agents support tasks such as estimating work order maintenance costs, optimizing shipments to external processors and automatically assigning warehouse activities. A Maintenance Work Order Cost Advisor can help planners and maintenance teams estimate the financial impact of upcoming work, while an External Processing Shipment Agent works to streamline logistics for operations handled by third parties.

Inventory teams get assistance from an Inventory Task Allocation Agent that assigns warehouse tasks automatically, and an Inventory Aging Advisor Agent that identifies slow‑moving stock. By flagging items that linger in storage, the system can prompt teams to adjust purchasing plans, run targeted promotions or reallocate inventory before it becomes obsolete.

Logistics and order management also benefit from specialized agents. A Wave Investigation Advisor can help resolve warehouse issues related to wave planning, while a Task Management Assistant prioritizes orders at risk, directing attention to shipments that may miss service‑level agreements. This helps operations managers intervene earlier instead of reacting after delays have already affected customers.

Additional agents, such as a Purchase Order to Sales Order Converter, can extract data from PDF documents and automatically create corresponding orders, removing the need for manual rekeying. A Product Configuration Agent focuses on automating complex configuration scenarios, while a Service Parts Advisor recommends the right parts for after‑sales service, aiming to increase first‑time fix rates in field operations.

AI at the core of Oracle’s enterprise strategy

The introduction of these new AI Agents lines up with Oracle’s broader push to embed intelligence across its technology stack, a theme underscored at the recent Oracle AI World Tour 2026 event in Madrid. During the conference, company leaders emphasised that AI is moving from pilot experiments to routine, everyday tools for businesses in Spain and beyond.

Under the banner of “Trust, Choice and Scale”, Oracle outlined a strategy based on bringing AI directly to where enterprise data already lives. Instead of shuttling information between multiple platforms, the company advocates running AI workloads close to the data within its cloud infrastructure and databases, which it argues can enhance security, reduce latency and simplify compliance.

Executives described Oracle’s AI as something that is built-in across the full stack rather than layered on top. From databases and infrastructure to line‑of‑business applications like Fusion Cloud, AI capabilities are being written into the products so that customers can access them within familiar interfaces rather than stitching together separate tools.

Part of this vision is a large catalog of preconfigured AI Agents: hundreds of agents for Fusion Applications and additional ones tuned for industry workloads. These agents can tackle tasks such as financial variance analysis or candidate matching in human resources, hinting at future expansions beyond the sales, service and supply chain domains announced so far.

Data sovereignty and multi‑cloud support were also highlighted as central pieces of the strategy. Oracle pointed to its EU Sovereign Cloud Region in Madrid, operated by local legal entities and staff based in the European Union, as an example of how the company is trying to balance regulatory compliance with performance. For organizations operating under strict privacy or jurisdictional rules, that combination can be a deciding factor when deploying AI at scale.

To tie these elements together, Oracle is developing an AI Data Platform that aims to unify the management of AI assets and enterprise data. The platform is positioned as model‑agnostic, giving customers access to a broad range of leading models — from providers like OpenAI, Google or Meta — so that each organization can pick the option that best fits its use case without being locked into a single vendor.

Real-world adoption and the role of clean, trusted data

Examples shared at Oracle events in Spain illustrate how these AI capabilities and agents are being adopted in practice. Some telecommunications providers are reportedly working with dozens of AI use cases, building digital twins of their mobile networks in the cloud and exploring new services such as GPU resources offered as‑a‑service to local enterprises.

In the consumer goods sector, companies are turning to Oracle’s AI data platform to unite financial and operational information under a common language. This helps simplify reporting and speeds up decision-making after mergers and acquisitions, where data fragmentation can otherwise slow down integration efforts and day‑to‑day management.

Healthcare and financial services have also been flagged as early beneficiaries. In hospitals, clinicians can rely on AI‑assisted workflows to reduce the time needed to process clinical information, sometimes cutting diagnostic timelines from weeks to hours. In banking, intelligent agents are helping to streamline credit processes that used to take months, with automation handling much of the document checking and risk analysis.

Oracle executives have repeatedly stressed that AI is becoming less visible in daily operations, in the sense that users interact with familiar applications while the intelligence operates behind the scenes. Hundreds of services and prebuilt agents are quietly orchestrating complex processes, whether that is generating annual reports through natural language queries or coordinating tasks in industries like hospitality or public safety.

However, a recurring message is that none of this works effectively without clean, integrated and trustworthy data. Before deploying agents at scale, organizations are encouraged to invest in consolidating and governing their data within secure environments, including sovereign cloud regions when regulatory requirements demand it. Only then can AI Agents deliver reliable outcomes rather than amplifying existing data quality issues.

Across these announcements, Oracle is positioning its new AI Agents as practical tools that sit on top of a broader architecture focused on integrated AI, data sovereignty and industry-specific workflows; together, these elements are meant to help enterprises automate more of their operations, tighten control over critical processes like supply chain and manufacturing and give business teams faster, more contextual insights without stepping outside their core applications.

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