Artificial intelligence in supply chain management is moving beyond isolated predictions and content generation. Oracle Fusion Cloud SCM 25D expands this direction through Oracle AI Agent Studio, giving organizations a platform to build, configure, test and deploy AI agents that operate within Oracle Fusion applications.

Oracle positions its enterprise AI strategy around a simple principle: AI should be built into business applications rather than added as a disconnected layer. The underlying model combines application context, security, governance, infrastructure and operational workflows so AI agents can work directly with enterprise processes.

For supply chain organizations, this represents an important shift. Instead of using AI only to summarize information or generate text, businesses can begin using agents to research issues, interpret documents, recommend actions, coordinate tasks and execute defined business processes across Oracle SCM.

Enterprise AI Requires More Than an AI Model

A large language model alone does not create an enterprise AI solution.

Oracle identifies several foundational requirements for enterprise AI, including:

Connected business systems
Business context
Process automation
Cloud-grade infrastructure
Continuous updates
Reliable scalability
Explain ability
Measurability
Traceability
Role-based access
Data separation
Audit trails

These requirements become particularly important in supply chain environments. A supply chain AI agent may need to work with purchase orders, inventory records, work orders, sales orders, supplier information, planning exceptions or maintenance history.

The value of the agent therefore depends not only on its ability to reason, but also on whether it understands the right business context and respects the organization’s security model. Oracle AI Agent Studio is designed to provide that enterprise foundation.

What Is Oracle AI Agent Studio?

Oracle describes AI Agent Studio as a development platform for building, testing and deploying extensible AI agents using the same tools Oracle uses. The platform is embedded into the broader Oracle Fusion environment.

Oracle’s architecture brings together:

Fusion Applications
Oracle AI Agent Studio
Oracle Cloud Infrastructure
AI models
AI data capabilities
Security and compliance
Open interoperability
AI agents
AI agent teams
AI workflow agents

This architecture is important because AI agents can interact with application-native business objects, roles and workflows instead of depending entirely on external integrations. Oracle describes this as a Fusion-native model where business objects, roles, workflows, security, audit and real-time execution are part of the AI environment.

Fusion-Native AI Changes How Agents Work

In many traditional AI implementations, the AI model operates outside the enterprise application. The business system sends data to the AI service, receives a response and then determines what should happen next. Oracle’s approach is different. AI agents are designed to operate within Fusion Applications, where they can work with application context and enterprise processes.

This can reduce the gap between:

understanding a business problem and taking action inside the business application.

For supply chain operations, that distinction is significant. An agent that merely identifies an inventory problem provides information. An agent that can identify the problem, understand the relevant supply chain context, recommend a course of action and participate in the corresponding Fusion workflow provides a much broader operational capability.

Preconfigured Agent Templates Accelerate Adoption

Organizations do not necessarily need to create every AI agent from scratch. Oracle AI Agent Studio provides preconfigured templates that can be configured, deployed and extended. Organizations can also create custom agents and multi-agent teams using Studio components.

Oracle describes support for:

Oracle-delivered agent templates
Custom agents
Single-agent workflows
Multi-step workflows
Multi-agent teams

This allows organizations to choose between extending a delivered capability and creating a new agent around a specific business requirement. For companies beginning their AI journey, prebuilt templates can reduce implementation effort. For organizations with specialized business processes, custom agents provide greater flexibility.

AI Agent Studio Supports Enterprise AI Governance

AI adoption in supply chain environments must be governed carefully. A supply chain AI agent may interact with commercially sensitive information such as supplier pricing, product costs, order details, inventory quantities or customer information. Oracle therefore emphasizes security and governance within AI Agent Studio.

The architecture presented includes:

Role-based access control
Role-based data access
Dynamic grounding
Guardrails
Request and response scans
Agent instructions
AI governance reviews
Audit trails
Prompt-injection protection
Toxic-language controls
Harmful-image controls
Zero data retention for supported model interactions

These controls are designed to bring AI execution closer to existing enterprise security and compliance requirements.

AI Agents Must Be Explainable and Measurable

One of the challenges with agentic AI is understanding why an agent produced a particular result or took a particular action.

Oracle therefore emphasizes four areas for managing AI agents in production:

Measure

Track accuracy, quality, safety, compliance, performance and cost.

Evaluate

Review semantic correctness, safety checks, latency and token usage.

Observe

Use dashboards, historical views and live monitoring.

Trace

Review individual steps, LLM calls, tools used and the complete execution path. This is particularly relevant as organizations move from informational AI assistants to agents that participate in business processes. The more operational responsibility an AI agent receives, the more important monitoring and traceability become.

Oracle AI Is Expanding Across SCM Applications

Oracle’s SCM AI strategy is not limited to one functional area.

The 25D presentation shows AI capabilities across:

Product Lifecycle Management
Supply Chain Planning
Procurement
Supply Chain Execution
Order Management
Logistics

Within these areas, Oracle references AI applications across product management, manufacturing, maintenance, quality, inventory, planning, procurement, transportation, warehouse management and other supply chain processes.

This creates a broader AI ecosystem rather than a collection of isolated point solutions. Supply chain processes are deeply interconnected.

A supplier problem can affect planning.
A planning problem can create an inventory shortage.
An inventory shortage can affect order fulfillment.
An equipment problem can affect manufacturing output.

Agents operating across SCM applications can potentially help organizations manage these dependencies more intelligently.

AI for Supply Chain Planning

Oracle highlights several AI-driven capabilities around Supply Chain Planning.

Examples in the presentation include:

Demand sensing and forecasting
Lead-time insights
Planning advisors
Automated forecast hyperparameter tuning
External machine-learning forecasting
Planning notes assistance
Supply Chain Planning process guidance
Data-quality analysis
Planning exception advisors
Supply disruption mitigation

These capabilities reflect an important shift in planning. Traditional planning applications generate recommendations, exceptions and forecasts. Agentic AI can provide an additional interaction layer that helps users interpret those outputs, understand exceptions and determine potential actions.

AI in Procurement

Procurement is another major area for AI Agent Studio.

Oracle’s SCM AI examples include:

Negotiation summaries
Supplier qualification questions
Supplier negotiation synopsis
Spend classification recommendations
Procurement policy guidance
Negotiation-message assistance
Purchase-order highlights
Supplier-registration attachment summaries
Purchase-order summaries
Sustainability policy advice
Negotiation requirement suggestions
Supplier recommendations

These capabilities can reduce the amount of manual reading and information gathering required during procurement activities. For example, procurement professionals may need to review multiple supplier documents before reaching a decision. AI-generated summaries or recommendations can help surface the relevant information more quickly while leaving the final business decision with the responsible user.

AI in Manufacturing and Quality Management

Manufacturing environments generate large amounts of operational information.

Oracle highlights AI use cases such as:

Generating quality inspection characteristics
Technician handoff notes
Production shift summaries
Inspection instructions
Operator shift notes
Quality inspection guidance
Workflow descriptions
Disposition assistance for rejected materials

These scenarios demonstrate where generative AI can reduce documentation effort. Instead of requiring supervisors and operators to manually create every shift note, handoff summary or inspection description, AI can help generate an initial version based on relevant operational context.

AI for Maintenance

Maintenance is another strong candidate for agentic automation.

Oracle’s 25D material references capabilities such as:

Repair suggestions based on service history
Repair summaries for maintenance technicians
Maintenance Advisor
Maintenance Work Order Builder
Post maintenance Work Reporting
Diagnostic Workflow Assistant

These capabilities can help reduce administrative effort surrounding maintenance activities. For example, a technician may perform urgent work and then need to document what was completed. An AI agent can help structure that information and support the creation of maintenance records. Similarly, a Maintenance Advisor can help surface information relevant to diagnosing and resolving an equipment issue.

AI for Inventory Management

Inventory management also receives several prebuilt agent templates.

The 25D presentation lists:

Material Expiration Advisor
Outbound Compliance Advisor
ASN Creation Assistant
Fulfillment Processing Assistant
Item Shortage Assistant

Inventory teams regularly manage exceptions. Materials may approach expiration dates. Orders may face shortages. Outbound transactions may require additional compliance checks. ASNs may need to be created or reviewed. Agents designed around these specific scenarios can help users focus attention on exceptions rather than routine information gathering.

AI for Order Management

Oracle’s 25D prebuilt agent templates also include several Order Management capabilities:

Order Exception Assistant
Data Access Advisor for Sales Orders
Sales Order Assistant
Purchase Order to Sales Order Converter

Order Management is an ideal area for agentic AI because users frequently need to investigate why an order is delayed, changed or blocked. Rather than manually navigating multiple screens, an agent can help research the relevant information and explain the situation.

AI for Product Management

Product Management templates include:

Product Comparison Advisor
Product 360 Advisor
Component Replacement Assistant

Product information can span engineering, suppliers, components, costs, compliance and lifecycle data. Agents can help users bring together relevant product information without manually searching across each underlying source. The Component Replacement Assistant is especially relevant when product changes or end-of-life notices affect supply chain operations.

Supply Chain Orchestration and Disruption Management

Oracle also lists a Supply Disruption Mitigator under Supply Chain Orchestration. Supply disruptions are inherently cross-functional.

A disruption may involve:

Suppliers
Inventory
Manufacturing
Transportation
Customer orders
Planning

An agentic approach can potentially help users gather information across those areas and coordinate the steps required to respond to the disruption.

From AI Workflows to AI Agent Teams

One of the most important concepts in Oracle’s presentation is the progression from deterministic automation toward increasingly autonomous AI.

Oracle identifies four patterns:

AI Workflows
Workflow Agents
AI Agents
AI Agent Teams

Each represents a different level of reasoning and execution.

AI Workflows: Logic-Based Automation

AI workflows use structured logic.

Oracle references components such as:

LLM nodes
Loops
If/then logic
Business-object functions

These workflows remain relatively deterministic. A defined process controls how the workflow progresses. One example presented is creating a purchase requisition from a supplier quote. This approach is useful when organizations know the required sequence of steps but want AI to assist with tasks such as extraction or interpretation.

Workflow Agents: Intelligent Process Automation

Workflow Agents introduce real-time AI reasoning into a structured business flow. Oracle provides the example of converting a customer PDF into a sales order. The overall business process is understood, but AI is used for steps where interpretation is necessary. For example, the system may need to identify fields in a customer document, resolve ambiguity or determine how extracted data should map into Oracle Fusion.

AI Agents: Goal-Based Execution

AI agents move further toward goal-oriented behavior. Oracle describes them as using reasoning and planning. Rather than following only predefined rules, an agent may research information, gather data and explain findings in pursuit of a specified objective. This is particularly useful for diagnostic or advisory scenarios. For example, a supply chain user might ask why an order cannot be fulfilled. The agent may need to investigate inventory, supply, order status and related exceptions before producing an explanation.

AI Agent Teams: Collaborative Execution

AI Agent Teams extend the model further. A lead or supervisor agent coordinates other agents that perform different parts of the work. Oracle’s example describes a team in which agents gather information, apply guidelines and deliver options under the coordination of a lead agent. This model is particularly relevant to complex supply chain processes because no single functional domain may contain all the information required to solve a problem. A supply chain disruption, for example, might eventually involve planning, procurement, inventory and order management agents working together.

Workflow Agents Can Transform Business Documents into Transactions

Document processing is one of the clearest use cases presented for workflow agents.

Oracle illustrates three examples:

Customer purchase order → Sales order

The workflow can ingest a PDF or email, recognize it as an order, extract customer, location, products, pricing and terms, review catalog and pricing exceptions, and then create and fulfill the sales order.

Supplier quote → Purchase requisition

The agent can identify the quote, extract supplier and item information, validate key values and create a purchase requisition.

Product Change Notice → Component replacement

The workflow can identify an end-of-life request, extract component details, check replacement recommendations and supply chain impact, and create an Engineering Change Order. These examples demonstrate the broader potential of agentic AI. The goal is not simply to summarize a document. It is to move from unstructured information to an enterprise transaction.

Human-in-the-Loop Remains Part of the Process

The document-processing workflow shown by Oracle includes a human-in-the-loop stage before the transaction is completed in Fusion. This is an important design principle. Not every AI-generated result should automatically become a transaction.

Human review can be especially valuable when dealing with:

Financial commitments
Supplier changes
Customer orders
Product changes
Engineering decisions
Compliance-sensitive processes

Agentic automation can reduce manual effort without necessarily removing business oversight.

SCM Prebuilt Agent Templates in 25D

Oracle Fusion Cloud SCM 25D includes a broad catalog of prebuilt templates across multiple SCM functions. The presentation specifically highlights templates across Maintenance, Manufacturing, Sustainability, Product Management, Inventory Management, Procurement, Order Management, Supply Chain Orchestration and Supply Chain Planning.

This is important for organizations evaluating AI Agent Studio. Instead of beginning with the question, “Where could we use AI?”, teams can start by reviewing the delivered templates against known operational pain points.

A warehouse organization might prioritize shortages and fulfillment.
A maintenance organization might prioritize work-order creation and post maintenance reporting.
A procurement organization might focus on supplier recommendations and quote-to-requisition automation.

The AI Agent Marketplace Expands the Ecosystem

The presentation also shows an AI Agent Marketplace where partner-created agent templates can be available within Fusion AI Agent Studio. Oracle describes partner templates as being built and tested in Agent Studio and validated against Oracle quality and design standards. Marketplace agents can operate independently or become part of broader agent teams. This expands the model beyond Oracle-delivered templates and gives implementation partners a way to build industry- or process-specific agent capabilities.

Why Oracle SCM AI Agent Studio Matters

The significance of AI Agent Studio is not simply the number of agents available. The more important change is how AI becomes connected to business execution.

Traditional generative AI often stops at:

Ask → Generate answer.

Agentic SCM introduces a broader pattern:

Understand → Research → Reason → Recommend → Validate → Execute.

That model is particularly relevant for supply chains because operational work is rarely limited to answering a question. Users need to investigate issues and then take action. Oracle AI Agent Studio provides a framework for connecting those steps within Fusion Applications.

Where Organizations Should Start

Organizations do not need to automate every SCM process immediately.

A practical starting point is to identify processes that have three characteristics:

Significant manual information gathering
Repetitive decision patterns
Clear business outcomes

Potential examples from Oracle’s delivered SCM agent portfolio include:

Investigating item shortages
Summarizing maintenance work
Processing supplier quotes
Reviewing order exceptions
Recommending replacement components
Managing planning exceptions
Creating ASNs
Analyzing material expiration
Resolving external purchase-price errors

Starting with a defined process makes it easier to measure whether the agent delivers operational value.

Governance Should Be Designed Alongside the Agent

Agent development should not focus only on prompts and instructions.

Organizations also need to define:

Which data the agent can access
Which user roles can invoke it
Which actions it may take
Which actions require human approval
What should be logged
How accuracy will be evaluated
How failures will be monitored
How performance and cost will be measured

Oracle’s AI Agent Studio architecture includes governance, access controls, guardrails, monitoring and execution tracing precisely because these requirements become increasingly important as agents move closer to transactional execution.

How NexInfo Helps with Oracle SCM AI Agent Studio

NexInfo helps organizations identify practical AI opportunities across Oracle Fusion Cloud SCM and translate them into governed business use cases.

For Oracle SCM AI Agent Studio, NexInfo can support:

SCM AI readiness assessment
AI-agent use-case discovery
Process prioritization
Prebuilt agent-template assessment
Custom agent design
Agent workflow configuration
Multi-agent use-case design
Business-object integration
Security and role-access design
Human-in-the-loop workflow planning
Agent testing and evaluation
Monitoring and traceability setup
Governance framework design
User acceptance testing
Deployment support
Ongoing AI-agent optimization and managed services

The focus should be on identifying where agentic AI can simplify real SCM processes rather than deploying AI simply because the technology is available.

Conclusion

Oracle Fusion Cloud SCM 25D expands the role of AI from embedded predictions and generative assistance toward agentic business execution. Oracle AI Agent Studio provides organizations with a platform for configuring delivered agents, creating custom agents, building structured AI workflows and coordinating multi-agent teams. Across planning, procurement, inventory, maintenance, manufacturing, order management, product management and logistics, Oracle is introducing agents designed around specific operational problems.

The most important shift is from AI that only provides information to AI that can participate in the workflow required to resolve the issue. For supply chain organizations, the opportunity is significant. But successful adoption will depend on combining AI capabilities with business context, security, governance, human oversight and measurable operational outcomes. Used effectively, Oracle SCM AI Agent Studio can become a foundation for a more intelligent, automated and responsive supply chain operating model.

FAQs

What is Oracle SCM AI Agent Studio?

Oracle AI Agent Studio is a development platform for building, testing and deploying extensible AI agents within Oracle Fusion applications using the same tools Oracle uses to develop its agents.

What is new for SCM AI Agent Studio in Oracle 25D?

Oracle SCM 25D includes prebuilt templates across Order Management, Maintenance, Manufacturing, Inventory Management, Procurement, Product Management, Sustainability, Supply Chain Orchestration and Supply Chain Planning.

Can organizations create their own Oracle AI agents?

Yes. Oracle AI Agent Studio supports extending Oracle-delivered agents as well as creating custom agents and multi-agent teams using Studio components.

What is the difference between an AI workflow and an AI agent?

Oracle describes AI workflows as logic-based automation using predefined nodes and rules, while AI agents use reasoning and planning to pursue goals. Workflow Agents sit between the two by combining structured workflows with real-time AI reasoning.

What are AI Agent Teams?

AI Agent Teams allow a lead agent to coordinate multiple specialized agents that collaborate on different parts of a business task.

Does Oracle AI Agent Studio support supply chain document processing?

Yes. Oracle shows workflow-agent examples that convert customer purchase orders into sales orders, supplier quotes into purchase requisitions and Product Change Notices into component-replacement processes.

How does Oracle govern AI agents?

The architecture includes role-based access, data access controls, guardrails, request and response scanning, audit trails, governance reviews and tools for measuring, evaluating, observing and tracing agent execution.

Which SCM areas can use AI agents?

Oracle presents AI capabilities across Product Lifecycle Management, Supply Chain Planning, Procurement, Supply Chain Execution, Order Management and Logistics, covering areas such as manufacturing, maintenance, inventory, warehouse management and transportation.

What are examples of Oracle Maintenance AI agent templates?

The 25D presentation lists Postmaintenance Work Reporting, Maintenance Work Order Builder and Diagnostic Workflow Assistant as Maintenance templates.

Can Oracle AI agents execute transactions?

Oracle’s workflow examples show agents participating in processes that can ultimately create Fusion transactions such as sales orders, purchase requisitions and engineering change orders, with human review incorporated into the illustrated workflows.

Oracle SCM AI Agent Studio 25D: Operationalizing Agentic AI Across Supply Chain Management