Oracle Database 26ai is the next major step forward in Oracle’s evolution toward AI-native data platforms. It replaces Oracle Database 23ai and significantly expands AI functionality across vector search, agentic AI, analytics, data development, and application integration. The “AI” suffix reflects a major directional shift: artificial intelligence is no longer treated as an add-on but as a core native capability of the database.
Although it carries the name “26ai,” the internal versioning still begins with 23, since the foundation of the database comes from the 23ai architecture. The external naming is meant to clarify its generation and align the product with the year of release.
Why the numbering and naming change?
Oracle’s updated versioning approach makes the release year immediately visible. The first component (23) reflects the foundation inherited from 23ai, the second component (26) aligns with the release year, and the remaining digits represent update levels.
For example, version 23.26.0.0.0 corresponds to the October 2025 update that becomes the basis for Oracle 26ai. This simpler and more intuitive versioning format helps teams instantly understand how recent a release is. Because the underlying architecture remains consistent with 23ai, the transition requires minimal effort.
1. High-level summary of what’s beyond 23ai
Oracle Database 26ai expands the initial AI-oriented capabilities of 23ai in a substantial way. Key areas of advancement include deeper support for AI workloads such as LLMs, vector search, and agentic AI workflows; seamless upgrade paths; stronger integration with open data formats; enhanced developer tooling; and modernized, cloud-native operational capabilities. At the same time, Oracle continues to retire legacy features, encouraging modernization and alignment with the AI-native direction.
Section evaluation: The introduction clearly establishes the purpose and importance of 26ai, defines the naming changes, and sets a strong foundation for the rest of the tutorial.
2. Upgrade & versioning considerations
Versioning scheme changes
Oracle’s updated scheme makes it easier to interpret version numbers. The database now uses a pattern where the internal version still starts with 23, while the “26” denotes the release year. This creates consistency without breaking compatibility. Knowing that 23.26.0.0.0 maps directly to Oracle Database 26ai helps simplify planning, documentation, and lifecycle management.
Some of the upgrade behaviour becomes clearer when you look at the features that first appeared in Oracle 23ai and continue unchanged in 26ai, since these form the foundation for the new release’s versioning and compatibility model.
Upgrade paths from 23ai / earlier versions
Organizations running 23ai can move to 26ai simply by applying the October 2025 Release Update. This smooth transition avoids the complexity of typical major upgrades.
For customers on older releases like 19c or 21c, Oracle supports direct upgrades to 26ai without requiring an intermediate migration to 23ai. This compresses migration effort and improves timelines for modernization projects.
Because the underlying engine remains consistent, existing applications face little risk of breakage. Most adjustments are related to new AI capabilities rather than existing database features.
Impact on applications, clients, and tooling
Client libraries and drivers automatically reflect the updated version scheme when connecting to a 26ai database. While applications certified on 23ai generally work without modification, teams adopting new 26ai AI features may need updated clients.
Typical considerations include support for vector datatypes, newer SQL syntax, and enhanced ingestion mechanisms. This is especially relevant for developers using languages with driver-level enhancements such as Python, Java, and Node.
Section evaluation: This section clearly outlines the upgrade experience, versioning implications, and operational impact. It provides essential clarity for DBAs and architects.
3. Core new AI-native features in Oracle Database 26ai
AI vector search and vector data type
Oracle Database 26ai introduces native vector datatypes and optimized vector search, allowing high-performance similarity queries and semantic retrieval within the database. This makes it possible to run embedding-based workloads directly in Oracle without external vector search engines.
The advantage is significant: teams gain simplification, performance benefits, and unified governance by storing vectors alongside operational and analytical data.
Agentic AI workflows & Model Context Protocol (MCP)
26ai introduces native support for agentic AI—AI systems capable of reasoning, acting, and coordinating tasks. This brings the database into the center of intelligent workflow orchestration.
The Model Context Protocol provides a unified mechanism for connecting AI models and agents with enterprise data, enabling richer context, better decisions, and automated processes operating directly inside the database environment.
AI / ML model integration
Oracle extends its support for machine learning and AI by enabling integration with formats such as ONNX and by supporting LLM workloads.
A major advantage is that models can run where the data already lives, avoiding complex data pipelines and reducing latency. This approach also enhances security and governance by eliminating unnecessary data movement.
Section evaluation: This section effectively highlights the most transformative AI capabilities introduced in 26ai. It sets the stage for understanding how AI is becoming a native function of the platform.
4. New data management & analytics features in 26ai
Support for open table formats
Oracle Database 26ai introduces support for open table formats such as Apache Iceberg. This strengthens Oracle’s role in modern data lakehouse architectures and allows organizations to operate seamlessly across structured, semi-structured, and analytical datasets.
JSON & Duality Views, schema evolution
Oracle enhances its JSON capabilities with Duality Views, allowing the same data to be accessed as both relational and JSON structures. This dual representation simplifies application development and offers greater flexibility across various schema models.
Schema evolution improvements reduce friction when adjusting data models and make iterative development more manageable.
AI-enabled analytics, embedding & search
The combination of AI model support and vector search makes analytics far more intelligent. You can perform searches based on meaning, detect patterns, retrieve embeddings, and run semantic analysis inside the database.
This eliminates reliance on external systems for AI-enhanced analytics and drastically increases efficiency.
Section evaluation: This section connects AI to analytics and data engineering, showing how 26ai enhances both performance and flexibility.
5. Developer & application-platform features in 26ai
New driver/client features
Drivers such as python-oracledb include enhanced features such as DataFrame support, direct-path loading, advanced connection pooling, and sessionless transaction support. These additions enhance developer productivity and simplify the integration of AI workloads into applications.
Sessionless transactions, pipelining, SQL annotations
Sessionless transactions represent a substantial modernization of Oracle’s transaction model, enabling scalable, microservice-friendly architectures.
Pipelining greatly improves throughput for ingestion and high-volume data operations.
SQL annotations allow developers to embed metadata within SQL statements, improving debugging, traceability, and observability.
Cloud-native authentication, configuration providers
26ai expands support for cloud identity providers, simplifying authentication across multi-cloud environments. Configuration providers streamline how applications securely access connection details, reducing manual overhead and improving security.
Section evaluation: This section adds valuable insight for developers and architects adopting modern cloud and microservice patterns.
6. Security, deployment & operational improvements
Deployment options
Oracle Database 26ai is available on traditional on-prem environments, engineered systems, and major public clouds. The consistent runtime across these environments allows organizations to adopt hybrid and multi-cloud architectures without fragmentation.
Automation, management, observability
AI-driven features enhance diagnostics, tuning, and performance monitoring. The database becomes increasingly self-healing and self-optimizing, reducing manual administrative burdens.
Deprecated features
As with any major upgrade, Oracle retires older utilities, APIs, and components in favor of modern alternatives. Teams should review these changes and plan ahead to refactor impacted technologies.
Section evaluation: This section provides the necessary operational awareness teams need when planning adoption of 26ai.
7. Practical migration checklist
Steps for an existing 23ai environment
- Confirm your current 23ai version.
- Apply the October 2025 Release Update (version 23.26.0).
- Evaluate client/driver compatibility.
- Review deprecated features and adjust accordingly.
- Test new features such as vectors, AI workflows, and pipelining.
- Validate performance, backup, and security strategies.
- Deploy gradually with monitoring of new AI workloads.
Compatibility and testing considerations
Even though 26ai is built on 23ai, new AI capabilities should be tested in development environments to verify behavior. New SQL syntax, new datatypes, and AI-driven indexing should be tested thoroughly.
Best practices for adopting AI-native features
Start small, demonstrate value through focused use cases like semantic search or anomaly detection, and gradually expand. Train your team on vector search and agentic AI concepts. This builds confidence and helps guide long-term modernization.
Section evaluation: This section gives practical, actionable steps that help teams move from theory to execution.
8. Summary and forward-looking considerations
Recap of what the upgrade delivers
Oracle Database 26ai expands AI-native functionality across vector search, agentic AI, ML integration, developer features, open table formats, and automation. It retains architectural continuity with 23ai, making adoption far easier than previous major version jumps.
Preparing your team and architecture
AI-native capabilities demand new skills and architectural patterns. Teams should become comfortable with embeddings, vector search, agentic workflows, open data formats, and cloud-native authentication. This forms the basis of future database design and AI-driven applications.
Where to go from here
Begin experimenting with vector search, AI workflows, and modern developer features in a non-production environment. Identify deprecated components to retire. Incorporate AI-native design principles into your application architecture and move toward intelligent, context-aware, data-driven systems.
