Major overview of Oracle AI Database 26ai features and use

 Oracle AI Database 26ai features and use

Oracle AI Database 26ai features, It focuses on application development, AI, high availability, security, distributed databases, performance, analytics, and manageability rather than listing every minor initialization parameter or API enhancement.

Release context: Oracle AI Database 26ai is the long-term support release that replaces Oracle Database 23ai. Existing 23ai environments can transition by applying the relevant Release Update, without a conventional database upgrade or application recertification. Features introduced during the 23ai innovation cycle are therefore part of the broader 26ai feature set. 

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1. AI and Generative AI

1.1 Oracle AI Vector Search

• Native VECTOR data type for storing numerical embeddings.
• Semantic similarity search based on meaning rather than exact keyword matching.
• Exact and approximate nearest-neighbor vector search.
• Vector indexes for scalable similarity search.
• Ability to combine vector predicates with:
  • Relational filters
  • Full-text search
  • JSON
  • Spatial data
  • Property graphs
• Support for enterprise Retrieval-Augmented Generation, or RAG.
• Database-native document loading, transformation, chunking, embedding, retrieval, and LLM integration.
• Vector search over structured and unstructured information.
• Support for vectors stored in Oracle tables and Apache Iceberg tables. 

1.2 Unified Hybrid Vector Search

Oracle can combine different retrieval techniques in one workflow:

• Semantic vector search
• Keyword and Oracle Text search
• Relational SQL predicates
• JSON filtering
• Spatial filtering
• Graph relationships
• Business-rule filters

This is particularly useful for enterprise RAG because exact terms such as employee IDs, product codes, locations, and policy names can be combined with semantic similarity. 

1.3 Select AI

Select AI provides a natural-language interface to enterprise data:

• Converts natural-language questions into SQL.
• Generates natural-language explanations from query results.
• Supports conversational interaction with database data.
• Supports RAG over private enterprise content.
• Uses database metadata and annotations to improve generated SQL.
• Can integrate with supported external LLM providers.
• Helps reduce the need for applications to build separate natural-language-to-SQL layers. 

1.4 Select AI Agent

• Create and operate AI agents close to governed enterprise data.
• Agents can use database objects and procedures as tools.
• Agents can invoke external tools through REST interfaces.
• Agents can interact with MCP servers.
• Supports multi-step agentic workflows involving retrieval, reasoning, and actions.
• Database security and auditing can be applied to agent activity.

1.5 Model Context Protocol support

• Oracle Database can participate in the MCP ecosystem used by AI applications and agents.
• Database capabilities can be exposed as discoverable agent tools.
• Managed MCP endpoints can expose Select AI Agent tools.
• MCP clients can invoke governed database functions and retrieval workflows.
• Integration logic can be kept closer to the data rather than creating a separate custom middleware service. 

1.6 Private Agent Factory

• No-code or low-code creation of private enterprise AI agents.
• Deployable in a customer-controlled environment.
• Designed to work against protected enterprise information.
• Helps create agents without sending all business data to externally managed systems.
• Complements Select AI and database-native agent capabilities. 

1.7 Private AI Services Container

• Supports deployment of selected AI services in customer-controlled infrastructure.
• Helps organizations keep AI processing closer to sensitive database data.
• Useful for regulated, disconnected, or tightly controlled environments.
• Reduces dependency on sending information to a public AI endpoint. 

1.8 Embedding and model interoperability

• Integration with leading LLMs.
• Support for ONNX embedding models.
• Ability to generate embeddings through database-controlled workflows.
• Support for open agentic AI frameworks.
• Greater flexibility in selecting embedding and language models.

1.9 In-database machine learning

• Train and score models without moving data outside the database.
• Enhancements to algorithms for improved text and data classification.
• Better algorithm performance and flexibility.
• Integration of machine-learning output with SQL, analytics, and application workloads.
• Reduces data movement and separate ML infrastructure requirements. 

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2. Application Development

2.1 JSON-Relational Duality Views

• Present normalized relational data as application-friendly JSON documents.
• Read and update the same underlying data through either SQL or JSON.
• Avoid maintaining separate relational and document copies.
• Reduce dependence on complex Object-Relational Mapping frameworks.
• Updatable JSON documents remain transactionally consistent with relational tables.
• Accessible using SQL, REST, document APIs, and MongoDB-compatible interfaces.
• Supports optimistic or lock-free concurrency control.
• Fine-grained rules can control whether parts of the document are insertable, updateable, or deletable. 

2.2 Native JSON capabilities

• Native JSON data type and optimized binary JSON storage.
• SQL/JSON query and transformation functions.
• JSON collection tables.
• JSON search indexes.
• JSON schema validation capabilities.
• JSON document access through SODA and supported document APIs.
• Integration of JSON with vector, spatial, graph, and relational queries. 

2.3 JavaScript stored procedures

• Develop database stored procedures in JavaScript.
• Use JavaScript for server-side application logic.
• Access database data directly from JavaScript procedures.
• Allows JavaScript developers to build data-intensive logic closer to the database.
• Complements existing PL/SQL, Java, SQL, and external-language support. 

2.4 Operational Property Graphs and SQL/PGQ

• Create property graphs over operational relational data.
• Query graph relationships using the ISO/IEC SQL Property Graph Queries standard.
• Run graph analysis without copying operational data to a separate graph database.
• Combine graph results with relational, JSON, vector, and spatial operations.
• Useful for fraud detection, dependency analysis, network analysis, recommendations, and customer relationships. 

2.5 Lock-Free Reservations

• Reserve portions of a numeric resource without locking the complete row.
• Designed for heavily updated records such as:
  • Account balances
  • Inventory quantities
  • Seats
  • Credit limits
  • Quotas
• Validation is performed without conventional row-lock serialization.
• Final updates are applied at commit.
• Improves throughput and reduces blocking for high-concurrency OLTP applications. 

2.6 Priority Transactions

• Applications can assign transaction priorities.
• A low-priority transaction blocking a high-priority transaction can be automatically aborted.
• Helps protect business-critical operations.
• Reduces the need for DBAs to identify and terminate blockers manually.
• Maintains better throughput under contention. 

2.7 Data Use Case Domains

• Define reusable domain-level business semantics for columns.
• Examples include:
  • Email
  • URL
  • Currency
  • Password
  • Phone number
  • Product identifier
• Centralizes data validation and semantic information.
• Applications and development tools can use domains for code generation and value validation.
• Helps maintain consistent definitions across tables and applications. 

2.8 Data annotations

• Attach business descriptions and semantic context to schemas and data.
• Give AI systems a clearer understanding of table and column meaning.
• Improve natural-language-to-SQL accuracy.
• Improve AI-generated application code.
• Reduce ambiguity where technical database names differ from business terminology. 

2.9 SQL enhancements

Major developer-facing enhancements include:

• Native SQL BOOLEAN data type.
• SELECT statements without a FROM clause.
• GROUP BY using a column alias or column position.
• Direct joins in UPDATE and DELETE.
• Unicode 15.0 support.
• Improved SQL syntax compatibility for developers migrating from other platforms.
• Wide tables supporting as many as 4,096 columns.
• Assertions for declarative business rules spanning one or more tables. 

2.10 Assertions

• Define declarative business rules across one or multiple tables.
• Address cases that are difficult to implement with normal check constraints.
• Reduce dependency on complex custom triggers.
• Enforce rules consistently regardless of which application modifies the data.
• Handle concurrency and serialization concerns within the database.
• Allow a single central rule to replace duplicate validation logic in multiple applications. 

2.11 Transactional Event Queues and Kafka APIs

• Kafka-compatible APIs for Oracle Transactional Event Queues, or TxEventQ.
• Existing Kafka applications can connect with fewer code changes.
• Messaging can participate in Oracle transactions.
• Supports event-driven application and microservices architectures.
• Reduces the need to operate a separate event platform for certain database-centric workloads. 

2.12 Sagas for microservices

• Database-supported saga patterns for long-running distributed transactions.
• Helps coordinate transactions across multiple services.
• Supports compensation when one stage of a business transaction fails.
• Avoids holding traditional distributed locks across services.
• Appropriate for order, payment, inventory, and shipment workflows. 

2.13 Redis-compatible access

• Oracle AI Database and True Cache can provide Redis-compatible server functionality.
• Redis objects can be backed by Oracle database objects.
• Cached data can be synchronized automatically when underlying data changes.
• Offers a database-managed alternative for selected Redis-style caching patterns.
• Can be used with Oracle Database, Active Data Guard, and True Cache configurations. 

2.14 Developer connectivity and APIs

The 26ai development ecosystem includes:

• JDBC and Universal Connection Pool
• Python python-oracledb
• Node.js node-oracledb
• Oracle Call Interface
• ODBC
• .NET providers
• Oracle REST Data Services
• SODA and document APIs
• MongoDB-compatible API
• JavaScript, PL/SQL, Java, C, C++, Python, and other supported programming interfaces 

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3. High Availability and Scalability

3.1 Oracle True Cache

• Primarily in-memory, automatically managed cache for Oracle Database data.
• Transactionally consistent with the primary database.
• Offloads read-intensive workloads.
• Applications can connect directly for read-only operations.
• JDBC applications can route read-only sections to configured True Cache instances.
• Provides fresher and more consistently managed data than many manually maintained application caches.
• Designed mainly for read scalability and performance, not as a disaster-recovery replacement for Data Guard. 

3.2 Oracle RAC

Oracle Real Application Clusters remains the foundation for:

• Active-active database instance availability.
• Scale-out of database processing.
• Instance failover.
• Service-based workload management.
• Online maintenance with reduced application disruption.
• Fast Application Notification and connection-pool integration.

The 26ai high-availability documentation continues to group RAC enhancements alongside general and Data Guard improvements.

3.3 Oracle Data Guard

Major 26ai Data Guard areas include:

• Physical standby protection and disaster recovery.
• Data Guard Broker automation.
• Fast-Start Failover.
• Active Data Guard read offload.
• Multitenant and per-PDB protection enhancements.
• Improved hybrid cloud support.
• Better automation-oriented output and management.
• Greater control over role-transition targets.
• Application Continuity and rolling-maintenance improvements.

Oracle’s 26ai new-features guide specifically organizes Data Guard, RAC, and general improvements under High Availability. 

3.4 Globally Distributed Database with Raft replication

• Built-in replication for sharded databases.
• Consensus-based commit using the Raft protocol.
• Does not require configuring Data Guard or GoldenGate for this replication model.
• Declarative replication configuration.
• Subsecond failover capabilities.
• Improves fault isolation and availability for globally distributed applications.
• Helps optimize hardware utilization in sharded environments.

3.5 Directory-based sharding

• Dynamically determines the location of records by sharding key.
• Maintains key-to-shard mapping in a directory.
• Supports large numbers of key mappings.
• Allows individual or bulk movement of keys between shards.
• Useful for data residency, tenant placement, load balancing, and geographic distribution.
• Delivers horizontal scalability with shard-level fault isolation. 

3.6 Automatic data movement after sharding-key updates

• Automatically moves a row when its updated sharding key maps to another partition or shard.
• Reduces application-side data-movement logic.
• Supports business changes such as customer relocation, organizational reassignment, or jurisdiction changes. 

3.7 Application Continuity and Transaction Guard

• Helps applications survive planned and unplanned outages.
• Replays eligible database work safely after recoverable failures.
• Transaction Guard provides a reliable transaction outcome.
• Reduces duplicate transaction risk.
• Integrates with RAC, Data Guard, database services, JDBC/UCP, and connection pools.

These capabilities remain central to Oracle’s high-availability stack, with 26ai expanding high-availability and failover integration. 

3.8 Online operations

Oracle 26ai continues Oracle’s broader support for online:

• Patching and rolling maintenance
• Schema changes
• Table and index maintenance
• Data movement
• Reorganization
• Application upgrades using Edition-Based Redefinition

Availability depends on deployment type, licensing, and the specific operation.

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4. Security

4.1 Oracle Deep Data Security

• Database-enforced authorization for users, applications, analytics, and AI agents.
• Propagates the original user or agent identity and execution context to the database.
• Declarative SQL policies enforce access at:
  • Row level
  • Column level
  • Individual-cell level
• Applies authorization regardless of whether data is reached through an application, SQL tool, analytics platform, or AI agent.
• Helps mitigate excessive AI-agent privilege and prompt-injection consequences.
• Centralizes authorization instead of duplicating it in every application.
• Provides an additional defense if application-level authorization is bypassed or incorrectly implemented.

4.2 Oracle SQL Firewall

• Built into Oracle AI Database.
• Inspects incoming SQL statements.
• Learns or defines approved SQL behavior.
• Detects, logs, alerts on, or blocks unauthorized SQL.
• Helps defend against SQL injection and compromised application credentials.
• Enforcement occurs inside the database, regardless of the SQL execution path.

4.3 Schema-level privileges

• Grant privileges across objects in a schema without using broad system privileges.
• Simplifies privilege administration.
• Reduces large sets of individual object grants.
• Supports least-privilege access more effectively.
• Makes access easier to manage as new objects are introduced into an application schema.

4.4 DB_DEVELOPER_ROLE

• Predefined role for application developers.
• Provides a curated set of privileges required to design, build, and deploy database applications.
• Reduces the practice of assigning broad DBA-like privileges to developers.
• Supports least-privilege development environments. 

4.5 Multi-factor authentication

• MFA can be enabled for native Oracle Database users.
• Strengthens authentication beyond a password-only model.
• Helps protect privileged and sensitive database accounts. 

4.6 TLS 1.3 and simplified TLS

• Support for TLS 1.3.
• Newer cipher suites provide stronger protection for data in transit.
• Simplified client/server TLS configuration.
• Helps reduce configuration errors while improving transport security. 

4.7 FIPS 140-3 preparation

• Oracle AI Database 26ai supports preparation for FIPS 140-3 compliance.
• Organizations may need to review and replace older encryption algorithms.
• Relevant for regulated and government environments. 

4.8 Constrained Kerberos delegation

• Introduces control over unconstrained versus constrained Kerberos ticket delegation.
• Reduces exposure associated with forwarding broad ticket-granting credentials.
• Useful for enterprise single sign-on environments. 

4.9 OAuth 2.0 and Microsoft Entra ID integration

• OAuth 2.0 authentication support through supported database clients.
• Integration with OCI IAM and Microsoft Entra ID, formerly Azure AD.
• Enables cloud-based single sign-on to Oracle Database services and supported on-premises databases.
• Helps application teams avoid embedding permanent database passwords. 

4.10 Long passwords

• Native database passwords can be up to 1,024 bytes.
• Supports stronger password and passphrase policies.
• Better accommodates externally generated credentials. 

4.11 Read-only users and sessions

• A user or session can be restricted to read-only operations irrespective of other granted privileges.
• Useful for reporting, troubleshooting, audit access, and production support.
• Reduces the possibility of accidental data modification. 

4.12 Existing enterprise security capabilities

Oracle 26ai also retains Oracle’s established security stack:

• Transparent Data Encryption
• Data Redaction
• Virtual Private Database
• Real Application Security
• Label Security
• Database Vault
• Unified Auditing
• Fine-Grained Auditing
• Privilege Analysis
• Key Vault integration
• Native network encryption
• Backup encryption
• Data Safe integration

Licensing requirements vary by edition, option, cloud service, and deployment platform.

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5. Analytics and AI Lakehouse

5.1 Autonomous AI Lakehouse

• Combines Oracle AI Database analytics with Apache Iceberg data lakes.
• Executes Oracle SQL and AI workloads over Iceberg data.
• Supports relational, JSON, graph, spatial, and vector analytics.
• Uses Exadata-powered processing.
• Offers serverless, pay-per-use scaling.
• Available across OCI, AWS, Google Cloud, and Microsoft Azure.
• Designed to interoperate with Iceberg ecosystems, including data managed through platforms such as Databricks and Snowflake. 

5.2 Apache Iceberg support

• Query open-format data-lake tables.
• Combine lakehouse data with operational database data.
• Store vectors in Iceberg tables.
• Create vector indexes over supported Iceberg vector data.
• Apply vector similarity and structured business predicates in one workflow.
• Reduce forced movement of lakehouse data into proprietary formats. 

5.3 Converged analytics

• SQL analytics over relational data.
• JSON analytics.
• Graph analytics.
• Spatial analytics.
• Text search.
• Vector similarity search.
• In-database machine learning.
• Lakehouse analytics.

The advantage is that these workloads can be combined in a single database platform rather than requiring a separate specialized database for each data model.

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6. Performance and Scalability

6.1 Wide tables

• Maximum table or view width increased to 4,096 columns.
• Useful for:
  • Machine-learning feature stores
  • IoT data
  • De-normalized analytical models
  • Large packaged applications
• Can simplify designs that previously had to split attributes across multiple tables. 

6.2 Hybrid Columnar Compression improvements

• Faster compression and decompression.
• Improved compression ratios for newly created or rebuilt HCC tables.
• Potentially reduced storage and I/O requirements.
• Exact benefit depends on the data and compression level. 

6.3 Consolidated background services

• Consolidates database maintenance and service actions into a more flexible group of background processes.
• Reduces dependence on many dedicated background processes.
• Improves process and resource management.
• Particularly relevant for consolidated environments with many PDBs. 

6.4 Concurrency improvements

• Lock-Free Reservations reduce row-level contention.
• Priority Transactions protect important transactions.
• JSON-Relational Duality supports optimistic concurrency.
• True Cache offloads read workloads.
• RAC and sharding provide scale-out options.
• Improved HCC reduces analytical storage and I/O overhead. 

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7. Database Architecture and Data Management

7.1 Multitenant architecture

Oracle 26ai continues the CDB/PDB architecture for:

• Database consolidation
• Tenant isolation
• Rapid provisioning and cloning
• PDB relocation
• Centralized patching and administration
• Resource governance
• Fleet-standardized operations
• PDB-level availability and disaster-recovery patterns

7.2 Converged data model

A single Oracle Database can manage:

• Relational data
• JSON documents
• Vectors
• Property graphs
• Spatial data
• XML
• Text
• Time-series-style data
• Blockchain and immutable tables
• Large objects and multimedia content

Oracle positions this converged model as a way to eliminate separate data stores and synchronization pipelines for each data type. 

7.3 Immutable and blockchain-style data protection

• Append-only and tamper-resistant data-management patterns.
• Useful for audit trails, financial records, compliance records, and chain-of-custody requirements.
• Complements auditing and enterprise security controls.

7.4 Automatic storage and lifecycle functions

• Partitioning
• Compression
• Heat Map
• Automatic Data Optimization
• Online table and index operations
• SecureFiles
• Recovery Manager
• Flashback technologies

These are established capabilities carried into 26ai rather than completely new 26ai inventions.

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8. Manageability, DevOps, and Operations

8.1 Simplified transition from 23ai

• Oracle 26ai replaces the 23ai product identity.
• Existing 23ai installations transition by applying the October 2025 Release Update or a later relevant RU.
• No traditional database upgrade is required for the 23ai-to-26ai transition.
• No application recertification is required solely because of the product-name transition. 8.2 Container support and Free edition

• Oracle AI Database 26ai Free is available as a container image.
• Full and Lite image variants are available.
• Lite images reduce image size and improve pull time for simpler development and CI/CD scenarios.
• Container settings can enable Archive Log mode and Force Logging.
• Useful for local development, testing, training, and automated pipelines.

8.3 Observability and automation

• Improved machine-readable administration output.
• Integration with Oracle Enterprise Manager and cloud management services.
• Data Guard Broker automation.
• Fleet Patching and Provisioning.
• Automatic performance diagnostics.
• Automatic Workload Repository.
• SQL Monitor and SQL tuning facilities.
• Data Safe integration for security assessment and monitoring.

8.4 Autonomous operations

Depending on the chosen Autonomous Database service:

• Automated configuration
• Automated backup
• Automated patching
• Elastic scaling
• Automatic tuning
• Automatic indexing
• Built-in availability management
• Serverless consumption models
• Autonomous AI Lakehouse capabilities

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9. Quick Category Summary

Category	Most important Oracle 26ai capabilities
AI	AI Vector Search, Unified Hybrid Vector Search, Select AI, Select AI Agent, MCP, Private Agent Factory, RAG, ONNX embeddings
Application development	JSON-Relational Duality, JavaScript procedures, SQL/PGQ graphs, annotations, domains, assertions, SQL enhancements
Microservices	Sagas, TxEventQ Kafka APIs, lock-free reservations, REST/JSON APIs, priority transactions
High availability	RAC, Data Guard, Active Data Guard, Application Continuity, Transaction Guard, True Cache
Distributed database	Sharding, directory-based sharding, Raft replication, automatic data movement
Security	Deep Data Security, SQL Firewall, schema privileges, developer role, MFA, TLS 1.3, OAuth2, Entra ID
Analytics	Autonomous AI Lakehouse, Apache Iceberg, graph, spatial, JSON, vector and SQL analytics
Performance	True Cache, HCC enhancements, wide tables, lock-free concurrency, RAC and sharding
Manageability	Multitenant, autonomous operations, containers, fleet tooling, 23ai-to-26ai RU transition

Highest-Priority Features

For architecture evaluation or an Oracle 19c-to-26ai roadmap:

1. AI Vector Search and enterprise RAG
2. JSON-Relational Duality Views
3. Oracle Deep Data Security
4. Oracle SQL Firewall
5. True Cache
6. Data Guard and Application Continuity enhancements
7. Lock-Free Reservations and Priority Transactions
8. Schema-level privileges and DB_DEVELOPER_ROLE
9. TxEventQ Kafka APIs and microservice Sagas
10. Autonomous AI Lakehouse and Apache Iceberg
11. Operational Property Graphs with SQL/PGQ
12. Globally Distributed Database with Raft replication

Important licensing note

Feature availability is not identical across Free, Standard Edition 2, Enterprise Edition, Exadata, Autonomous Database, and cloud database services. Some capabilities are included in the base database, while RAC, Active Data Guard, Partitioning, Advanced Security, Database Vault, and other functions may require a specific edition, option, engineered system, or cloud service entitlement. Always validate the exact deployment against the current Oracle Licensing Information User Manual and your Oracle ordering documents.