Oracle is expanding its AI development platform with a new builder experience for Oracle AI Agent Studio, giving customers and partners new ways to create AI-powered business applications that run natively inside Oracle Fusion Cloud Applications.

The announcement signals Oracle’s continued push beyond AI assistants toward what it calls “Fusion Agentic Applications”—business applications built around teams of AI agents that can execute enterprise workflows while operating within existing security, governance, and compliance frameworks. The release addresses a challenge many organizations face as AI projects mature: moving from experimental agents to production-ready enterprise systems.

Moving Beyond AI Assistants

Most enterprise AI deployments today revolve around standalone chatbots, copilots, or workflow automation tools that operate alongside core business applications. While these approaches can improve productivity, they often require organizations to build separate security, identity, governance, and auditing capabilities before they can be deployed in production.

Oracle is positioning Fusion Agentic Applications as an alternative. Rather than connecting external AI services to enterprise applications, the new applications execute directly inside Oracle Fusion, allowing them to interact with business objects, workflows, approvals, and enterprise policies without requiring separate runtime environments.

The updated builder experience combines no-code, low-code, and professional development tools into a single framework. Business users can create applications through natural language interfaces, while developers can use familiar tools such as Visual Studio Code, Git-based workflows, command-line interfaces, and AI coding assistants including Codex and Claude Code.

“Enterprise software is moving beyond systems that record work to systems that actively drive and execute outcomes,” said Chris Leone. “With this new builder experience, customers and partners can build Fusion Agentic Applications that are backed by specialized agent teams and run natively inside Oracle Fusion Applications, where the business objects, workflows, security, approvals, and auditability already exist. This is fundamentally different from building disconnected AI automations and then trying to bolt on enterprise controls later.”

Addressing Enterprise AI Deployment Challenges

One of the primary obstacles to enterprise AI adoption has been operationalizing AI systems after proof-of-concept deployments. Organizations frequently discover that moving AI applications into production requires solving challenges related to authentication, data access, observability, lifecycle management, governance, and regulatory compliance.

Oracle’s latest release attempts to address these concerns by embedding AI execution within the existing Fusion Applications runtime. As a result, AI applications inherit the same governance controls, security policies, approval workflows, and audit trails already used across Oracle’s enterprise applications.

The platform also introduces new developer capabilities designed to simplify application development. These include reusable templates, reference architectures, starter projects, and a public GitHub repository intended to accelerate implementation. Oracle is also expanding interoperability, enabling organizations to orchestrate Oracle-built, third-party, partner, and custom AI agents through a common execution framework.

Another enhancement is the expansion of the Oracle AI Agent Marketplace, which will now support complete agentic applications in addition to individual AI agents. Oracle says more than 80,000 professionals have already been certified on Oracle AI Agent Studio, reflecting growing enterprise interest in agent-based application development.

A Broader Shift Toward Agentic Enterprise Software

The announcement reflects a broader trend across the enterprise software industry as vendors compete to move beyond AI copilots toward autonomous systems capable of executing business processes.

Rather than simply answering questions or generating content, these applications are designed to coordinate multiple specialized AI agents that can perform tasks such as accelerating financial close processes, improving collections, optimizing workforce operations, or streamlining supply chain activities.

By embedding AI directly into enterprise applications instead of layering automation on top, vendors hope to reduce the operational complexity that has slowed enterprise AI adoption.

What Comes Next

As enterprises move from AI experimentation to production deployments, governance and operational reliability are becoming as important as model performance. Organizations increasingly need AI systems that can operate within existing business processes while meeting enterprise requirements for security, compliance, and auditability.

Oracle’s expanded AI Agent Studio reflects this shift by focusing less on standalone AI assistants and more on AI-native enterprise applications. Whether this integrated approach proves more attractive than independent AI platforms will likely depend on how organizations balance flexibility with the operational advantages of running AI inside existing enterprise software ecosystems.