Exploring Oracle’s AI Database 26ai and Its Agentic Use Cases
John: Hey everyone, welcome back to the blog! Today, we’re diving into something exciting from the world of AI and databases: Oracle’s AI Database 26ai and how it’s targeting agentic use cases. If you’re a tech enthusiast who’s curious about how AI is transforming data management, this is going to be a fun ride. I’m John, your go-to AI blogger, and joining me is Lila, who’s always got those spot-on questions to keep things grounded.
Lila: Hi John! So, I’ve heard about this Oracle AI Database 26ai, but I’m not totally sure what it means. Can you break it down for us beginners?
The Basics of Oracle AI Database 26ai
John: Absolutely, Lila. Oracle AI Database 26ai is the latest version of Oracle’s flagship database, announced just a couple of days ago at Oracle AI World 2025. It’s essentially an “AI-native” database, meaning AI is baked right into its core for handling data in smarter ways. This isn’t just a rename from their previous 23ai—it’s a long-term support release packed with features like AI vector search, support for Apache Iceberg, and even quantum-resistant encryption. The big focus here is on agentic use cases, which are all about using AI agents to automate tasks within your data workflows.
John: If you’re into automation and want to see how tools like this fit into the bigger picture, our deep-dive on Make.com covers features, pricing, and use cases in plain English—it’s a great way to understand how automation platforms can complement databases like this: Make.com (formerly Integromat) — Features, Pricing, Reviews, Use Cases.
Lila: Agentic use cases? That sounds fancy. What exactly does that mean, and why is Oracle targeting them?
What Are Agentic Use Cases?
John: Great question! “Agentic” refers to AI agents—think of them as smart, autonomous helpers that can perform tasks on their own, like decision-making or automating processes without constant human input. In the context of Oracle’s database, these agents are embedded directly into the data layer, making it easier for developers to build automated workflows. For example, an AI agent could analyze customer data in real-time and trigger actions like personalized marketing emails or inventory adjustments.
John: From what I’ve seen in recent reports, Oracle is integrating tools like agent builders and something called MCP Server to help with this. It’s all about making databases not just store data, but actively use AI to process and act on it, which is a game-changer for businesses dealing with massive datasets.
Lila: Okay, that makes sense. Like having a robot assistant inside your database. What are some key features that make this possible?
Key Features of AI Database 26ai
John: Spot on with the robot assistant analogy, Lila! Let’s list out some of the standout features based on the latest announcements:
- AI Vector Search: This allows for similarity searches, which is perfect for things like recommendation engines or image recognition within the database itself.
- Autonomous AI Lakehouse: It supports Apache Iceberg for better data lakes, meaning you can handle unstructured data more efficiently across clouds like OCI, AWS, Azure, and Google Cloud.
- Agent Builders and Integration: Tools to create and deploy AI agents that automate workflows, integrating with Oracle’s Fusion Applications for enterprise-wide use.
- Quantum-Resistant Encryption: ML-KEM encryption to keep data secure against future quantum computing threats.
- Unified Data Model: Handles relational, JSON, and graph data seamlessly, with data annotations to improve AI model accuracy.
John: These features are designed to power what Oracle calls the “AI for Data Revolution,” where AI isn’t an add-on but part of the database’s DNA. Recent news from Oracle AI World highlights how partners like NVIDIA are collaborating to accelerate this, with supercomputers like Zettascale10 enhancing AI processing.
Lila: Wow, that’s a lot. Are there any real-world examples or current developments we can point to?
Current Developments and Real-World Applications
John: Definitely! Just yesterday, Oracle announced expansions to their AI Agent Studio for Fusion Applications, including a new marketplace for pre-built agents and integrations with large language models (LLMs). This means businesses can quickly deploy agents for tasks like financial forecasting or supply chain optimization.
John: On the trends side, verified X accounts from Oracle insiders and tech analysts are buzzing about how 26ai replaces 23ai as the go-to long-term release. For instance, startups like Baseten and Luma AI are shifting to Oracle Cloud Infrastructure for their AI workloads, citing the high-performance AI-native database. It’s also multi-cloud friendly, which is huge for companies not locked into one provider.
Lila: Interesting! But what about challenges? Is this all smooth sailing?
Challenges and Considerations
John: Not entirely, Lila. While it’s innovative, adopting an AI-native database like 26ai requires upfront investment in skills and infrastructure. There’s also the need to ensure data privacy, especially with built-in AI agents handling sensitive info. Oracle addresses this with advanced security features, but organizations must plan migrations carefully—upgrading from older versions isn’t always straightforward, as noted in database expert blogs.
John: Another point is the learning curve for developers new to agentic AI. It’s powerful, but you need to understand how to build and manage these agents without creating unintended automations.
Lila: Got it. Looking ahead, what’s the future potential here?
Future Potential of Agentic AI in Databases
John: The sky’s the limit, really. With Oracle pushing AI integration across their ecosystem, we could see more seamless AI-driven apps in sectors like healthcare, finance, and retail. Imagine agents that predict market trends in real-time or automate compliance checks. Partnerships with NVIDIA and others suggest faster AI training and deployment, potentially leading to zettascale computing for massive datasets.
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Lila: That sounds promising. Any FAQs that come up often?
FAQs on Oracle AI Database 26ai
John: Sure, here are a few common ones:
- Is 26ai a replacement for 23ai? Yes, it’s the new long-term support version, so if you’re planning an upgrade, now’s the time.
- How does it support multi-cloud? Through compatibility with major providers, allowing data analytics across environments.
- What’s the cost? Pricing varies, but it’s part of Oracle’s cloud offerings—check their official site for details.
John: And if you’re exploring automation alongside this, don’t forget our guide on Make.com—it’s packed with insights to get you started: Make.com (formerly Integromat) — Features, Pricing, Reviews, Use Cases.
John: In reflection, Oracle’s AI Database 26ai is a bold step toward making AI a core part of data management, empowering businesses to automate intelligently. It’s exciting to see how this evolves, blending power with practicality for everyday users.
Lila: Totally agree—it’s like giving databases superpowers! My takeaway: Start small with understanding agents, and you might just revolutionize your workflows.
This article was created based on publicly available, verified sources. References:
- Oracle targets agentic use cases with AI Database 26ai | InfoWorld
- Oracle AI Database 26ai Powers the AI for Data Revolution | MarketScreener
- Oracle at Oracle AI World 2025 Keynote: Pioneering AI Integration By Investing.com
- 26ai: Oracle AI Database debuts with Autonomous AI Lakehouse and Apache Iceberg support across OCI, AWS, Azure, Google Cloud
- Oracle Expands AI Agent Studio for Fusion Applications with New Marketplace, LLMs, and Vast Partner Network
- Oracle AI Database 26ai: how a database becomes “AI-native” – Techzine Global
- Oracle adds AI capabilities to core database and launches a lakehouse platform – SiliconANGLE