A year ago, Oracle AI World served as the first major showcase for new CEOs Clay Magouyrk and Mike Sicilia. Oracle divided its story neatly: Clay Magouyrk focused on infrastructure while Mike Sicilia focused on AI and applications. 

This year, those pieces started to come together around a bigger idea: AI should not simply tell businesses what is happening. It should help them decide what happens next and, increasingly, do the work. Mike Sicilia captured the ambition simply: “The goal isn’t more AI in your business. It is making your business capable of doing more.” Every conference this year focused on outcomes and adoption at scale. Oracle AI World was no different.  However, 2026 marks a strategic shift for Oracle. Oracle wants Fusion to become the governed authority layer through which AI interprets business context and executes work, including work that spans non-Oracle systems. Much of the conference doubled-down on this strategy.  

Oracle Fills Out The Agentic Stack 

Oracle is tackling a fundamental problem with enterprise agents: They need more than an LLM to do real work. They need business context, identity, permissions, and guardrails. Fusion Claw provides a governed runtime for agents to execute tasks, Fusion Companion gives employees a natural-language interface for getting work done, and enterprise ontologies map the relationships, policies, processes, and actions agents need to understand the business. 

Oracle also gives agents their own identities, with task-limited access and human verification for sensitive actions, rather than letting them inherit a user’s credentials. And autonomy isn’t all-or-nothing. Oracle demonstrated autonomous work in truck rolls for field service and nurse scheduling, while finance scenarios still required human approval. That’s the more realistic near-term model: match the level of autonomy to the process, its risk, and the consequences of getting it wrong. 

But greater context and autonomy raise another issue: ownership. If Oracle builds a company’s business map from its applications, email, chats, wikis, and policies, that map becomes valuable intellectual property in its own right. Enterprises should ensure they own it and can take it with them. In the agentic era, portability won’t just mean moving data and models. It will mean moving the business context that makes those agents useful.   

AI Infrastructure Gets More Diverse 

The agentic story also has a significant infrastructure story underneath it. Oracle expanded OCI’s AI compute roadmap with a growing range of accelerators aimed at different AI workloads: 

    • NVIDIA Vera Rubin NVL72: Designed for frontier-model training, high-throughput inference, and agentic AI systems. 
    • Google TPUs: Bring an alternative to GPUs for large-scale AI training and production inference to OCI. 
    • Cerebras CS-3: Targets extremely low-latency inference and reasoning workloads. 
    • AMD Instinct MI455X GPUs and EPYC CPUs: Expand OCI’s rack-scale AI infrastructure and options for performance- and cost-sensitive workloads. 
    • SambaNova SN50: Adds another specialized architecture for inference-focused workloads. 

The bigger story isn’t another list of chips. No single accelerator architecture will suit every AI workload. Training, reasoning, real-time inference, and agentic workloads have different requirements. Oracle is betting that OCI’s advantage can come from offering diverse compute architectures tied together by a high-performance network fabric. However, customers should distinguish between today’s OCI and the roadmap. Some of the most differentiated capabilities, including Google TPUs and Cerebras CS-3, aren’t expected on OCI until 2027. 

Customers Put The Technology In Context 

The external presenters provided some of the most memorable reality checks. Here were some of the most notable:  

Fabio Fazzino and Sophie Buresi of QSRP, whose restaurant platform includes Burger King Italia, described agents that identify product shortages, unusual transactions, and delivery problems. Their lesson was refreshingly practical: “The hard part is not AI.” The challenge is turning an AI-generated insight into something an operations employee can understand and act on in five seconds, while building enough trust that people actually use it. 

At CMS, Jeneen Iwugo described using roughly 300 AI models to find patterns and identify potential fraud the Medicare program in the US, with early pilots with the State of Missouri for Medicaid. The ambition is increasingly to move from identifying suspicious activity to recommending action and eventually stopping questionable payments before the money goes out. 

Hetal Doshi of the U.S. Department of State illustrated the complexity of doing this at global scale. Operating across more than 270 locations and processing roughly 100,000 passports daily, State must modernize while accommodating everything from open environments to secret and top-secret information. Its efforts have helped some name checks return 50% to 60% faster and cut some runtimes roughly in half. 

The Bottom Line 

More agentic autonomy means more enterprise risk. CIOs should match the level of autonomy to the business risk involved, then scale only when agents deliver accurate outcomes, controlled exceptions, reliable recovery, complete audit trails, and acceptable costs.  

Just as important, don’t trade process automation for a new form of lock-in. As Oracle manages more business context, agent identities, application configuration, and transaction execution, customers must retain control of the artifacts underneath them. Demand the ability to inspect, change, version, export, and reuse ontologies and configurations. The measure of agentic AI isn’t how many agents you deploy. It’s where you can safely give machines more authority and prove that doing so produces a better business outcome 

If you want to discuss Oracle AI World, we’d love to connect with an inquiry or guidance session. Please reach out at inquiry@forrester.com to speak with us. 

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