Oracle’s Big Bet: AI Builders for Everyone Inside Fusion
Oracle’s new AI-native builder experience for Oracle AI Agent Studio inside Fusion Cloud Applications is a no-code, low-code, and pro-code enterprise AI builder that lets customers and partners create outcome-driven agentic applications that run natively against Fusion business objects, workflows, security, and approvals.
This move is less about another shiny AI toy and more about who gets to build the next generation of enterprise automation. Oracle announced the new builder on July 14, giving Fusion customers and partners the same tools it uses to create its own Oracle Fusion agentic apps. The core idea: no-code AI agents should not live at the edge of the suite; they should live inside it, inheriting governance, approvals, and audit trails by default. In a market flooded with disconnected copilots, Oracle is arguing that the real power play is integrated, governed, and production-ready AI, accessible to non-developers.
From Agents to Agentic Apps: Why Integration Beats Experiments
Oracle is drawing a hard line between standalone agents and what it calls Fusion Agentic Applications: outcome-driven systems backed by teams of specialized AI agents that reason, coordinate, decide, and execute work through Fusion business objects, workflows, tools, policies, approvals, and logged actions. This is not about a chatbot answering questions; it is about AI coordinating real transactions inside finance, supply chain, HR, and beyond.
Fusion Agentic Applications already exist for finance and supply chain, where they can make and execute decisions securely inside business processes by accessing enterprise data, workflows, policies, approval hierarchies, permissions, and transactional context. The July builder update extends that strategy from Oracle-delivered applications to customer- and partner-built applications. That directly attacks a well-known bottleneck: many enterprises never move AI beyond pilots because identity, data access, approvals, audit trails, observability, governance controls, and lifecycle management remain unsolved. Oracle’s stance is blunt: those problems should be solved in the runtime, not in every project.
No-Code AI Agents Meet Pro-Code: One Builder, Three Audiences
The most opinionated part of Oracle’s strategy is its refusal to pick a side in the no-code vs. developer tooling debate. The new builder experience brings no-code, low-code AI development, and pro-code into one Fusion-native framework. Business users can describe the agentic application they want in natural language through the Agentic Applications Builder, while professional developers and partners use the AI Studio Skill with Visual Studio Code, standard CLIs, Git-based workflows, and AI coding assistants such as Codex and Claude Code.
This blended model matters. It means a finance manager can design a no-code AI agent to accelerate financial close, while engineers refine the same agent’s behavior using CI/CD, local validation, debugging, and lifecycle management. Quote-worthy, Oracle states that the new builder experience supports no-code, low-code, and pro-code development for Fusion Agentic Applications. In practical terms, it reduces dependency on specialized AI engineers and opens enterprise AI builder responsibilities to permitted workers regardless of their technical skills.
Governed by Design: Why Native Runtime Is the Real Innovation
The headline feature is not that Oracle offers an enterprise AI builder; it is that the builder is fused into the same runtime as Oracle Fusion agentic apps. Agentic applications run natively in Fusion, act against Fusion business objects and workflows, and inherit security, governance controls, approvals, and auditability. They are positioned explicitly as complete business applications, not AI wrappers, for outcomes like accelerating financial close, improving collections, reducing service escalations, optimizing workforce operations, and streamlining supply chain execution.
That native runtime approach goes after the toughest problem in enterprise AI adoption: moving from prototype to production. Instead of asking every project team to bolt on identity, approvals, and observability, these capabilities are built in from day one. For compliance-heavy organizations, this is the only credible way to scale no-code AI agents. If an agent acts, the platform already knows who it is, what data it touched, which approvals it followed, and how to audit it.
What Changes for Enterprise Teams Next
For everyday Fusion users, this move is less about AI hype and more about a new way of working. Business users can now describe agentic workflows in plain language, while developers rely on familiar engineering practices to refine and operate them. Available at no additional cost, Oracle AI Agent Studio delivers orchestration, advanced testing, validation, and built-in security so customers and partners can create and manage AI agents and agentic applications inside Fusion.
The numbers show Oracle’s intent to make this mainstream: over 80,000 certified experts have been trained in Oracle AI Agent Studio to help organizations build, test, deploy, and manage AI across the enterprise, and the builder can extend the 1,000-plus AI agents and 22 Fusion Agentic Applications launched earlier this year. A new public GitHub repository will provide templates, starter projects, sample applications, reusable assets, and reference architectures for faster development. The question now is no longer whether Fusion will ship AI agents, but how far customers will go in reshaping their own processes with Oracle Fusion agentic apps.
