How Oracle Is Reinventing the Database for the AI Era

For years, enterprise databases have operated behind the scenes. They store the transactions, protect the data, and keep business applications running.

That role is changing.

As enterprises move from AI experimentation toward agentic AI and increasingly autonomous workflows, the database is becoming an active part of the AI architecture.

In this conversation, R “Ray” Wang and Holger Mueller unpack Oracle’s rapid pace of database innovation throughout 2026. The announcements span AI, security, availability, infrastructure, application development, and more. Together, they point to a larger shift in how Oracle sees the database in the AI era.

Keep the data. Bring AI to it.

One of Oracle’s clearest points of differentiation is its approach to transactional data.

Rather than moving transactional data into a separate data lakehouse to support AI, Oracle is keeping that data in the Oracle Database while using an AI data lakehouse for unstructured content. Oracle AI Vector Search can then vectorize that content and make the resulting vectors available alongside transactional data.

The architectural implication is significant.

Enterprises don't have to move critical transactional data simply to make it useful for AI. That means less data movement, fewer duplicated security models, and less rework for IT teams.

As Holger Mueller points out in the conversation, enterprise businesses still run on transactions. As agents increasingly interact with those systems, keeping the data layer secure and accessible becomes even more important.

Agents are changing database requirements

The rise of AI agents also changes the workload. Traditional enterprise systems were largely designed around human users. Humans go home at night. They take vacations. They don't execute thousands of tasks simultaneously.

Agents don't have the same limitations.

The conversation highlights an example where a contact center with roughly 2,000 human agents could eventually have close to 10,000 software agents interacting with the back-end system.

That creates a very different availability and performance challenge.

Oracle's Maximum Availability Architecture is designed to address that shift, including a Platinum tier with faster failover and a Diamond tier focused on even higher levels of availability.

AI agents don't just create new applications. They create new demands on the infrastructure underneath them.

Security has to move closer to the data

AI also changes the threat model. If agents can access and act on enterprise data, protecting the application layer alone isn't enough. Security needs to be closer to the underlying data itself.

Oracle's approach includes deep data security, role-based access, SQL Firewall, Database Vault, transparent data encryption, identity propagation, and centralized security capabilities.

The interview also highlights support for post-quantum cryptography, addressing the emerging risk of attackers harvesting encrypted data today to decrypt it later as quantum capabilities mature. The larger takeaway is that security can't be bolted onto AI after the fact.

It needs to be part of the architecture.

Bringing cloud economics on premises

Another notable development is Oracle AI Database Cloud@Customer.

The offering brings Oracle's database capabilities and cloud operating model to customers that still need to keep infrastructure on-premises because of data residency, compliance, performance, or other requirements.

That includes AI capabilities, vector search, agent development, high availability, and Oracle-managed infrastructure while keeping the environment on-premises.

For enterprises with hybrid requirements, this could be an important middle ground between traditional infrastructure and public cloud.

APEX takes AI into application development

Oracle's AI push isn't limited to the database engine.

APEX, Oracle's low-code/no-code application development platform, is also getting an AI-driven evolution through APEX Lang.

The concept is intent-driven development. Developers and business users can describe what they want an application or agent to do using natural language rather than starting with traditional coding workflows.

Because APEX is tightly integrated with the Oracle Database, the approach can work with existing data structures and access models, rather than requiring developers to generate code externally and retrofit it into an enterprise environment.

That could make AI-powered application development considerably more accessible while retaining the enterprise controls built into the database.

The bigger picture

Taken together, these announcements tell a larger story. Oracle isn't treating AI as just another feature layered on top of the database.

It's redesigning the database around an environment where agents, AI workloads, security, application development, and transactions increasingly converge.

That matters because enterprise AI ultimately has to do more than generate content or answer questions. It has to interact with the systems that run the business. And those systems still depend heavily on databases.

The companies that win the next phase of enterprise AI won't just have the best models. They'll have architectures that can securely connect those models and agents to enterprise data, execute decisions reliably, and operate at scale.

Oracle's 2026 database roadmap suggests the database may be moving from the infrastructure underneath AI to one of the places where AI actually happens.