Setting up an enterprise application is slow work, and much of it gets repeated. Teams work out which settings depend on which, put the steps in the right order, test the result, and then do it all again for the next customer. The knowledge sits in documents that go stale with every release.
On Monday at Oracle AI World in Las Vegas, Oracle said it wants to move that knowledge into code. The company published the Oracle Fusion Setup and Configuration Registry, or FSCR, as a public Git repository. It holds Oracle's setup and configuration expertise as machine-readable skills that AI agents can run.
According to Oracle, the skills cover configuration options, dependencies, sequencing, execution, testing, and validation. An agent can take business requirements or an existing configuration and turn them into a runbook. It then makes the changes in Oracle Fusion Cloud Applications and tests the results. Human reviewers check the finished configuration, ask for adjustments, and approve it for deployment. Oracle says assistants and coding agents from OpenAI and others can use the registry.
Chris Leone, Oracle's executive vice president of Applications Development, said during the keynote that his team will own the registry. It will be updated with every release and with any patch that affects setup. He also said the skills warn you before you change an object that can't be reversed. "Agents will be the operator, and the registry will be the authority," he said.
Leone put it this way: "The Oracle Fusion Setup and Configuration Registry is like having an expert Oracle product manager guiding teams through the entire setup and configuration process." The release also carries supporting statements from Accenture Edge, Deloitte, PwC, Grant Thornton, and Apex IT. Each describes building its own accelerators on top of the registry.
The registry feeds Oracle AI Success Navigator, which now includes six agents: discovery, design, configuration, testing, integration, and innovation. Lexington Health, an early adopter, said the approach has cut certain design and configuration work by 80 percent during implementation. That is one customer's figure. Oracle didn't share a broader benchmark.
The keynote also showed what the agents do once they're running. Oracle calls its runtime Fusion Claw. It handles long-running, multi-step work such as reconciliation, analysis, and optimization, and Oracle says it supports continuous replanning and deterministic execution. Oracle says 25 Claw-powered agentic applications are available now across ERP, HCM, supply chain, and customer experience. Leone said Oracle is shipping 75 packaged agentic applications in total.
The live demo was a shipping load planner. The agent started with trucks at 61 percent utilization and proposed a plan to reach 88 percent, saving $58,000. It flagged five loads for a planner to review and cleared the other 149. After Leone approved, it rechecked the current state of the system before pulling in 117 orders and making the changes. Then it produced what he called an outcome receipt: who did the work, what actions were taken, and which standard operating procedures it followed. "AI with receipts," he said.
For developers, Oracle says customers and partners can build their own agents and agentic applications in Oracle AI Agent Studio for Fusion Applications, using no-code or pro-code tools. The release lists built-in observability, ROI measurement, and safety controls.
Oracle also announced Fusion Companion, which lets people work with Fusion through supported AI tools. It ships first as a plugin in OpenAI's marketplace for ChatGPT, with more AI clients planned. Companion decides whether a request becomes a transaction, an agent, an agentic application, or a long-running Claw job, and it applies the user's permissions. A second piece, Fusion Agentic Home, is a native mobile experience for Android and iOS that is voice-first.
The most useful material for builders came from the customers. QSRP, which Oracle says runs about 1,500 restaurants, built agents with Oracle for product availability, loss prevention, and delivery claims. In one demo, an agent grouped failed orders across stores and traced them to a single missing configuration in an Uber Eats integration. After approval, it fixed every affected store in one pass.
Sophie Buresi, QSRP's head of AI, said the pilot confirmed two things. "The hard part is not so much about AI. If you have the right data, AI is pretty simple," she said. The other is that operations teams have to trust a tool before they use it. To go from a few restaurants to hundreds, she said QSRP needs reliable data integration with its platform, results measured against the business case, and trust from the people running the restaurants.
Spectrum's approach was similar. Derek Dreyfus, the company's VP of HR technology and AI strategy, said his team treats governance as an accelerator. If an answer is repeatable and governed, an agent handles it. Anything involving a pay change, a process exception, or something sensitive in an employee's record goes to a person.
Oracle CEO Mike Sicilia built the whole keynote around trust. "Trust isn't something we can announce; it's something that we must earn," he said. For developers, trust shows up as things you can inspect: a receipt for every action, a threshold that sends exceptions to a human, and a registry in Git that you can read before you commit to it.
Oracle's materials leave some questions open. They don't cover licensing terms for the registry, how partner extensions will be vetted, or pricing for the Claw-powered applications. The best test is whether agents configure Fusion from the registry as cleanly as the demo suggested. Because the repository is public, teams can read the skills and check that for themselves.
