Enterprise AI is moving beyond isolated prompts and standalone chatbots. The next stage is about building governed AI agents that can understand business context, use enterprise tools, follow security rules, interact with business objects, support human approvals and operate inside real workflows.
Oracle Fusion AI Agent Studio is designed for this shift. Oracle positions Fusion AI Agent Studio as a platform to build, test and deploy AI agents, with capabilities such as agent patterns, human-in-the-loop controls, native business objects, prebuilt templates, credentials store, knowledge store, testing, validation, monitoring, observability and tracing. The key message is clear: AI agents must be built into enterprise applications, not bolted on as disconnected AI layers.
Safe Harbor and Roadmap Note
Oracle’s material includes a safe harbor statement. The roadmap items are intended to outline general product direction and should not be treated as a delivery commitment. Development, release timing and pricing may change at Oracle’s discretion.
For business leaders, this means AI Agent Studio planning should be practical and phased. Organizations can explore available capabilities while preparing governance, security, data and operating models for future enhancements.
What Is Oracle Fusion AI Agent Studio?
Oracle Fusion AI Agent Studio is a platform for designing AI agents, deploying and extending prebuilt templates, and creating custom agents and multi-agent teams. The office hours deck positions it as a way to create AI agents with the same tools Oracle uses.
The platform includes capabilities across:
- Agent patterns
- Human-in-the-loop workflows
- Security and trust
- Native business objects
- Testing and validation
- Prebuilt templates
- Interoperability through MCP and A2A
- Credentials store
- Tools, topics and prompts
- Knowledge store
- Monitoring, observability and tracing
This makes AI Agent Studio more than a chatbot builder. It is an enterprise AI agent development and governance platform.
Why AI Agent Studio Matters for Enterprises
Many organizations are experimenting with AI, but enterprise AI requires more than creative prompts. Business users need agents that are secure, auditable, role-aware, workflow-ready and connected to systems of record.
Oracle’s AI Agent Studio approach addresses this by giving organizations a framework to design agents that can:
- Work with Fusion business objects
- Use tools and topics
- Support workflow agents
- Apply guardrails
- Connect with knowledge stores
- Use credentials securely
- Support monitoring and evaluation
- Enable human-in-the-loop approval
- Integrate with enterprise communication tools
- Support multi-agent architectures
This is important because AI adoption in enterprise applications must be controlled, explainable and scalable.
Delivered Templates: Copy Template vs Use Template
Oracle highlights an important difference between Copy Template and Use Template.
Copy Template automatically adds a suffix to all artifacts in the agent team and takes the user directly to the agent team canvas. From there, the user can edit agent team settings, agents, tools and topics.
Use Template takes the user through a step-by-step configuration process for each artifact in the agent team. This distinction matters during deployment. Teams that want faster duplication may use Copy Template, while teams that want guided configuration may prefer Use Template.
Pre-Delivered Workflow Agents
Oracle lists several pre-delivered workflow agents available for reuse, including:
- CX Service Request Resolution Workflow
- HCM Involuntary Interpreter Agent
- HCM Learning Creation Assistant
- HCM Employee Replacement Workflow Assistant
- HCM Time Assistant
- SCM Quote to Purchase Requisition Assistant
- SCM B2B Message Converter
- SCM Purchase Order to Sales Order Converter
- SCM Component Replacement Assistant
These templates help organizations start faster instead of building every workflow from scratch. For implementation teams, the opportunity is to identify which templates can be reused, which need extension and which require custom agent design.
AI Agent Studio Beyond Fusion Applications
Oracle’s roadmap FAQ addresses whether agents created in Studio can work outside Oracle Fusion Applications, such as with SAP as an ERP backend, OTM or WMS Cloud.
The deck states that there is currently no native support for integrations outside Fusion Applications. However, agents can be invoked using REST APIs outside Fusion Applications starting in 25D. Oracle also notes that federation between Fusion Applications and non-Fusion IDCS, along with the corresponding UI, is planned for 26A.
This is important for enterprise architects. AI Agent Studio can become part of a broader enterprise AI architecture, but non-Fusion connectivity requires careful integration planning.
MCP and A2A Support
Oracle’s roadmap also highlights support for MCP and A2A. MCP capability is planned for release 26A. The deck says users will be able to create connections to MCP servers available as SSE or Streamable HTTP, authenticate in the MCP tool definition, view available tools and select which tools to include in the configuration.
A2A is planned in basic form in 26A for Oracle agents to be called in third-party chat. Oracle also notes future roadmap enhancements to simplify this and to support calling third-party agents into Oracle agents. For businesses building enterprise AI ecosystems, MCP and A2A are important because agents will not operate in isolation. They will need to communicate with tools, platforms and other agents.
Multimodal Support and Document Interpretation
Oracle’s FAQ states that multimodal support, including image interpretation, will be available in release 26A. This expands the role of AI agents beyond text-only interactions. Over time, agents may be able to interpret documents, images, screenshots and other business artifacts as part of enterprise workflows.
For functions like service, HR, procurement, compliance and supply chain, multimodal capability can improve how agents process evidence, forms, images and operational documents.
Guardrails, Sensitive Data and Responsible AI
One of the most important enterprise AI questions is whether agents can identify and protect sensitive data. Oracle’s roadmap includes PII detection and blocking or obfuscation, along with configurable guardrails. Until those capabilities are available, Oracle recommends using Topics to add guardrail instructions.
Examples include instructing agents not to process personal information such as names, addresses, phone numbers or social security numbers, and not to provide financial advice when the request could be interpreted as regulated advice. For enterprises, this is a critical point. AI agent governance cannot be added after deployment. Guardrails, topics, permissions, role access and escalation logic must be part of the design.
Version Control and Security Access
Oracle states that AI Agent Studio currently supports draft and published versions of agents, with no additional version control features available at that time. The deck also notes that when deploying a custom agent, if the Security tab does not show roles in the dropdown, teams should ensure the ORA_DR_FAI_GENERATIVE_AI_AGENT_RUNTIME_DUTY role is assigned and permission groups are enabled.
This reinforces the need for strong security readiness before agent deployment. AI agents must be assigned correctly, governed through roles and tested before being exposed to users.
Multilingual Response Support
Oracle states that agents can support multiple languages in 25D, but language support may vary by release, product, use case or feature. The deck also notes that only English is available for Enterprise Performance Management features with AI.
Another important point: the AI agent’s response language is not yet automatically synchronized with the user’s sign-in language or preferred language. The administrator must configure the response language during agent design, or the user must explicitly specify the preferred response language in the prompt. This matters for global organizations. Multilingual rollout should include language governance, user guidance and agent response testing.
Exposing AI Agents Through Smart Actions
Oracle provides a practical example for exposing an AI agent through Smart Actions in CX Service Centre.
The prerequisites include:
- Sandbox access
- A published agent
The setup path is:
Application Composer → Common Setup → Smart Actions
The action is created as a UI-based action, with Agent Team selected as the action type. This shows how AI agents can become part of real application workflows rather than remaining separate tools. For service teams, this can bring AI assistance directly into the context where users are already working.
Workflow Agents and Vector DB
The office hours deck also covers workflow agents and Vector DB usage.
Oracle describes:
Document Processor
Extracts text or content from Fusion UCM files for downstream use. An example is processing a log file and extracting error details.
Vector DB Writer
Persists structured JSON as semantic vectors for future retrieval. An example is storing normalized enriched person and skill objects as vectors.
Vector DB Reader
Retrieves semantic objects or fragments using vector similarity search. An example is retrieving employees with similar skill vectors and career interests.
Oracle also notes that users do not need to create connections with Vector DB because it is preconfigured in AI Agent Studio. This is a major capability for retrieval-augmented generation and enterprise knowledge workflows.
Agent Design: Single Agent, Multi-Agent or Workflow Agent?
The deck includes a useful architecture decision framework.
Use a Single Agent When
A single role or persona can handle the entire task.
Use Multiple Agents When
- The path to an answer is not always clear
- Agents need to reason across multiple domains
- Faster experimentation and more natural conversations are needed
- The task is exploratory or analytical, not transactional
Use a Workflow Agent When
- Accuracy and consistency are required
- Every action must be logged and auditable
- The workflow touches systems of record
- The process should be predictable and repeatable
This framework is important because not every use case should be solved with the same agent pattern.
Oracle AI Agent Studio as a Platform
The platform visual on page 16 presents AI Agent Studio as a broad build, test and deploy environment.
It includes capabilities across:
Integration and Extensibility
- MCP support
- A2A agent cards
- Credential store
- Microsoft Teams
- Slack
- Dynamic custom headers
- Common integrations and connectors
Observability and Evaluation
- Monitoring
- Evaluation
- Test dataset management
- Agent tracing
- A/B comparisons
- Performance metrics
- Token usage
- RAG metrics
- Improved debugging
- Enhanced guardrails
- Auditing
LLMs
- OpenAI
- Meta
- Cohere
- xAI
- Anthropic
AI Agents
- New agent templates
- Seeded runnable agent teams
- Custom agents
- Agent builder assistant
- AI Agent Studio FAQ agent
- Fusion business object operations
- Environment promotion
Multimodal and RAG
- Image and document upload in chat
- Multimodal RAG
- SharePoint integration for RAG on external documents
Workflow Nodes
- Agent node
- Document processor
- LLM
- Vector read/write
- For-loop iteration
- Parallel execution
- Switch branching logic
- Human-in-the-loop
- Code and tool nodes
- Email, time and event triggers
This breadth shows that Oracle AI Agent Studio is being positioned as an enterprise-grade AI agent lifecycle platform, not a narrow assistant feature.
Prebuilt Templates in 25C and 25D
Oracle lists a wide range of prebuilt templates across 25C and 25D. The 25C templates cover areas such as public sector, service, sales and marketing, human resources, supply chain, finance and procurement. Examples include Service Request Triage Agent, Account Advisor Agent, Sales Analyzer, Benefit Analyst, Leave and Absence Analyst, Procurement Policy Advisor, Quality Inspection Advisor and Claims Policy Advisor.
The 25D list expands further across CPQ, service, incentives, HCM, supply chain and manufacturing, procurement, ERP and industry, and public sector. Examples include Quote Advisor, Case Analyzer Agent, Contract Summarization Agent, Lead Advisor Agent Team, Deal Advisor Agent Team, Payroll Run Analyst, B2B Message Converter, Product 360 Advisor, Supplier Portal Advisor and Access Request Assistant. For enterprises, these templates reduce starting friction. They also provide reference patterns for building custom agents in a governed way.
Prompt Design Examples: User Session, Unique ID and Pagination
Oracle includes practical prompt design examples that show how agents should interact with tools and APIs.
Get User Session
The example instructs the agent to retrieve the user’s identification details once at the beginning of the conversation using the getUserSession tool, then reuse details such as PersonNumber and ServerTime during the conversation.
Get Unique ID
The example shows how to call an inventory on-hand balance function, extract a unique ID from a self-link or fallback key and encode it for API usage.
Pagination
The pagination example uses an offset parameter to show supplier results in batches and asks the user whether they want to see the next set of suppliers before continuing.
These examples are valuable because enterprise agents must not only answer questions. They must use tools safely, handle API response structures, manage state and avoid unnecessary repeated calls.
Choice of LLMs
The final slide highlights LLM options across multiple providers, including OpenAI, Meta, Anthropic, Google, xAI and Cohere. The visual includes models such as GPT-5, GPT-4.1, GPT-4o, Gemini 2.5 Flash, Gemini 2.5 Pro, Claude Sonnet 4, Claude Haiku 3.5, Grok 3, Llama and Command R. The slide also indicates “comes with Fusion” and “bring your own” options.
For enterprises, this matters because AI strategy may depend on use case, latency, cost, governance, model capability, data sensitivity and deployment architecture.
What This Means for Business and IT Leaders
Oracle Fusion AI Agent Studio gives enterprise teams a structured way to move from AI experimentation to operational AI.
The practical implications are significant:
- Business teams can start with prebuilt templates.
- IT teams can govern access through roles and permissions.
- Architects can design single-agent, multi-agent or workflow-agent patterns.
- Security teams can define guardrails and sensitive data rules.
- Operations teams can embed agents into application workflows through Smart Actions.
- Developers can use APIs, business objects, Vector DB and workflow nodes.
- Leaders can monitor usage, performance, evaluation and traceability.
The organizations that will get the most value are the ones that treat AI agents as governed digital workers, not disconnected chat tools.
How NexInfo Can Help
NexInfo helps enterprises prepare for Oracle Fusion AI Agent Studio adoption with a structured approach across strategy, governance, implementation and managed support.
NexInfo can support:
- Oracle AI Agent Studio readiness assessment
- AI agent use case discovery
- Agent architecture design
- Prebuilt template evaluation
- Custom agent planning
- Workflow agent design
- Smart Actions deployment planning
- REST API and business object integration
- Vector DB and RAG readiness
- Guardrail and governance design
- Role and security setup review
- Testing and validation strategy
- Monitoring and observability planning
- User training and adoption support
- Post-deployment optimization
NexInfo helps organizations move AI agents from idea to governed enterprise execution.
Conclusion
Oracle Fusion AI Agent Studio represents a major shift in how enterprises can build and deploy AI inside business applications. The platform supports prebuilt templates, custom agents, workflow agents, business objects, Smart Actions, MCP, A2A, Vector DB, guardrails, observability, evaluation and multiple LLM options.
The real value will come from disciplined implementation. Organizations need clear use cases, clean data, secure roles, strong prompts, workflow governance, testing, monitoring and adoption planning. With the right roadmap, AI Agent Studio can help enterprises build AI agents that are not only intelligent, but also secure, auditable, workflow-ready and aligned with business outcomes.
NexInfo helps enterprises adopt Oracle Fusion AI Agent Studio with the right strategy, governance and implementation support.
FAQ
What is Oracle Fusion AI Agent Studio?
Oracle Fusion AI Agent Studio is a platform for designing, testing and deploying AI agents for Fusion Applications. It supports prebuilt templates, custom agents, multi-agent teams, workflow agents, business objects, tools, topics, prompts, knowledge stores, testing, monitoring and observability.
What is the difference between Copy Template and Use Template in AI Agent Studio?
Copy Template automatically adds a suffix to all artifacts in the agent team and opens the agent team canvas. Use Template takes users through a step-by-step configuration process for each artifact in the agent team.
Are there prebuilt workflow agents in AI Agent Studio?
Yes. Oracle lists pre-delivered workflow agents such as CX Service Request Resolution Workflow, HCM Learning Creation Assistant, HCM Employee Replacement Workflow Assistant, SCM Quote to Purchase Requisition Assistant and SCM Component Replacement Assistant.
Can Oracle AI agents work outside Fusion Applications?
The deck states there is currently no native support for integrations outside Fusion Applications, but agents can be invoked using REST APIs outside Fusion Applications starting 25D. Federation between Fusion Applications and non-Fusion IDCS is planned for 26A.
Does AI Agent Studio support MCP?
MCP support is planned for release 26A. Oracle says users will be able to create connections to MCP servers, authenticate in the MCP tool definition and select available MCP tools for configuration.
Does AI Agent Studio support A2A?
A2A is planned in basic form in 26A for Oracle agents to be called in third-party chat. Oracle also notes future roadmap enhancements to simplify this and support calling third-party agents into Oracle agents.
Can AI Agent Studio interpret images in documents?
Oracle states that multimodal support, including image interpretation, will be available in release 26A.
How does AI Agent Studio handle sensitive data?
Oracle’s roadmap includes PII detection, blocking or obfuscation and configurable guardrails. Until then, Oracle recommends using Topics to add guardrail instructions for handling personal information or restricted advice.
What is a workflow agent?
A workflow agent is used when a process needs accuracy, consistency, logging, auditability and repeatability, especially when touching systems of record. Oracle’s deck recommends workflow agents for predictable and auditable processes.
What is Vector DB used for in AI Agent Studio?
Vector DB is used for semantic retrieval. Oracle describes Vector DB Writer as persisting structured JSON as semantic vectors and Vector DB Reader as retrieving semantic objects or fragments using vector similarity search.
Can AI agents be exposed through Smart Actions?
Yes. Oracle provides a CX Service Centre example where a published agent can be exposed through Application Composer, Common Setup and Smart Actions using a UI-based action with Agent Team as the action type.
How can NexInfo help with Oracle Fusion AI Agent Studio?
NexInfo can help with AI Agent Studio readiness, use case discovery, prebuilt template assessment, custom agent design, workflow agent development, business object integration, REST API planning, guardrail design, security setup, testing, monitoring and user adoption.