n October 2025, Oracle made an announcement that made most DBAs read the headline twice: Oracle Database 23ai was being renamed Oracle AI Database 26ai. A simple rebranding? Absolutely not. This is Oracle’s most significant database release positioning in years, and the technical changes underneath the name are substantial. As someone who has been tracking this release since its early cloud availability, let me walk you through what actually changed, what stayed the same, and what you need to think about before your organization makes the move.

First: Understanding the Version Numbering

This confuses everyone initially. Although previously known as Oracle Database 23ai, Oracle unified the name of the upcoming LTS version to Oracle AI Database 26ai in October 2025. The internal version is 23.26.x, making it effectively an enhanced version of 23ai.

So when you look at the actual binary: it’s still the same database engine lineage. There is no separate 24 or 25 version that you missed. The jump from 23 to 26 in the name reflects Oracle’s quarterly release update numbering — the 26th quarterly update of the 23c/23ai codebase.

Customers can simply apply the October 2025 release update to transition from 23ai to the currently available features of 26ai. There is no database upgrade or application re-certification required.

This is actually remarkable. Moving from 19c to 23ai requires a full database upgrade with AutoUpgrade, downtime planning, and application regression testing. Moving from 23ai to 26ai is a patch application. Same process as any quarterly RU.

What 26ai Adds on Top of 23ai — The Features That Matter to DBAs

Oracle AI Database 26ai includes over 300 new features with a focus on artificial intelligence and developer productivity. Let me focus on the ones with real operational impact rather than walking through a marketing checklist.

RAFT-Based Replication for Globally Distributed Database

This is the biggest architectural addition for DBAs managing distributed Oracle environments. Previously, Oracle’s Globally Distributed Database (formerly known as Sharding) used proprietary replication mechanisms. Oracle AI Database 26ai adds new RAFT protocol support to make it easier than ever to distribute or shard your database because of regulatory or performance requirements.

RAFT is a well-understood consensus protocol used by systems like etcd and CockroachDB. Oracle implementing RAFT for replica coordination means that distributed Oracle deployments now benefit from a battle-tested protocol with well-documented failure behavior, rather than Oracle-proprietary mechanisms that require Oracle-specific expertise to diagnose when things go wrong.

For organizations that have avoided Oracle Sharding due to operational complexity, this is worth re-evaluating. The management story has meaningfully improved.

SQL Firewall — Now Built In, No Extra License

SQL Firewall provides real-time protection against common database attacks by monitoring and blocking unauthorized SQL and SQL injection attacks, no matter the SQL execution path. It is included in Oracle AI Database

In 23ai, SQL Firewall existed but required separate configuration and in some configurations additional licensing considerations. In 26ai, it’s a core database feature. The mechanism: you train the firewall by running your application in learning mode, which captures the expected SQL patterns. Then you switch to enforcement mode — any SQL that doesn’t match the learned pattern is blocked or flagged.

-- Enable SQL Firewall for a specific user
BEGIN
    DBMS_SQL_FIREWALL.ENABLE_ALLOW_LIST(
        username    => 'APP_USER',
        enforce     => DBMS_SQL_FIREWALL.ENFORCE_SQL,
        block       => TRUE
    );
END;
/
-- Check violations
SELECT username, sql_text, firewall_action, violation_type,
       occurred_at
FROM dba_sql_firewall_violations
ORDER BY occurred_at DESC;

For regulated environments — banking, healthcare, government — this built-in SQL injection protection at the database layer is significant. It doesn’t replace application-layer input validation, but it provides defense-in-depth at the point where the SQL actually executes.

Quantum-Resistant Encryption

26ai includes quantum-resistant encryption as part of its security stack. This is forward-looking but increasingly important for organizations handling data with long-term sensitivity. Quantum computing threatens current RSA and ECC-based encryption algorithms — quantum-resistant (post-quantum) algorithms are designed to remain secure even against quantum attacks.

For most organizations, this isn’t an immediate operational concern but it is a compliance and architecture planning concern. If your data governance policy requires future-proofing against quantum threats, 26ai has a documented answer.

Private Agent Factory — Agentic AI Workflows Inside Oracle

Private Agent Factory unlocks agentic AI potential in enterprises with Oracle AI Database 26ai.This is Oracle’s implementation of AI agent orchestration directly inside the database.

The concept: an AI agent is an autonomous process that can perceive context, make decisions, call tools, and take actions — iteratively, without human intervention for each step. In 26ai, you can define agents that have access to your Oracle data, can query it using natural language converted to SQL, can call external APIs, and can chain multiple actions together.

-- Define an agent tool that queries the database
BEGIN
    DBMS_VECTOR_CHAIN.CREATE_PREFERENCE(
        preference_name => 'MY_AGENT_TOOL',
        preference_type => 'VECTOR_INDEX',
        preference_params => JSON_OBJECT(
            'vector_db_provider' VALUE 'oracle',
            'model' VALUE 'MY_EMBED_MODEL'
        )
    );
END;
/

For DBAs, this means enterprise AI applications can be built where the agent’s “memory” and “knowledge” live in Oracle — not in a separate vector database, not in a third-party AI platform’s proprietary storage. The data never leaves Oracle. The agent logic runs inside Oracle. This is Oracle’s answer to the proliferation of external AI infrastructure.

Apache Iceberg Lakehouse Support

The Oracle Autonomous AI Lakehouse service combines the power of Oracle AI Database with the freedom of an Apache Iceberg data lake. It lets customers run AI and analytic SQL on all Iceberg data, with Oracle Exadata-powered performance and pay-per-use serverless scaling.

For organizations that have invested in data lake architectures — data stored in Parquet format on object storage, managed by Apache Iceberg’s table format — 26ai allows Oracle Database to query that data directly using SQL, with the same optimizer and execution engine used for relational data.

-- Query an Iceberg table from Oracle 26ai
SELECT product_category, SUM(revenue) total_revenue
FROM ICEBERG.sales_data@my_lakehouse
WHERE sale_date > DATE '2025-01-01'
GROUP BY product_category
ORDER BY total_revenue DESC;

This is architecturally significant: organizations that separated their operational data (Oracle) from their analytical data (data lake) now have a unified query layer. You don’t have to move data between systems — you query both through Oracle SQL.

Deep Data Security for Agentic AI

Oracle Deep Data Security provides context-aware data access control for agentic AI in Oracle AI Database 26ai. As AI agents gain the ability to query and act on enterprise data autonomously, the access control question becomes critical. Who — or what — is allowed to access which data, under what context?

Deep Data Security extends Oracle’s existing VPD (Virtual Private Database) and Label Security mechanisms to understand AI context. An agent running a customer query can be restricted from accessing financial data even if the human user who initiated the agent session would have that access. The access control travels with the agent’s execution context, not just with the session credential.

What Stayed the Same — The Foundation is Unchanged

Everything that made 23ai a solid platform remains in 26ai:

• JSON Relational Duality Views — same feature, enhanced performance
• AI Vector Search — extended with sparse vector support and binary vector format
• SQL Domains — unchanged from 23ai
• True Cache — enhanced but architecturally the same
• Multitenant CDB/PDB architecture — still mandatory, still the only supported architecture
• AutoUpgrade for migration from 19c — same process, 26ai is a valid target

The On-Premises Story — A Major Shift

This is where 26ai represents a genuinely different strategic position from 23ai for many organizations.

23ai was primarily a cloud-first release. The on-premises version arrived later and with less fanfare. For organisations still running Oracle Database 19c on-premises — customers who didn’t want to migrate to the cloud — the announcement that Oracle AI Database 26ai would be downloadable for on-premise Linux platforms in January 2026 is huge. Until now, if you were running 19c on-premises and wanted AI features, your only real option was migrating to cloud-managed Oracle AI Database or related cloud services.

Oracle AI Database 26ai Enterprise Edition for Linux x86-64 was released in January 2026 as part of the quarterly Release Update (version 23.26.1).

This changes the conversation with on-premises customers who have resisted cloud migration for data sovereignty, regulatory, or infrastructure reasons. AI Vector Search, RAFT-based distributed database, SQL Firewall, quantum-resistant encryption — all of this is now available without touching OCI.

Upgrade Path: From 19c to 26ai

The most common question I’m getting from production environments: can we go directly from 19c to 26ai?

Yes. Direct migration from 19c is possible. However, moving to CDB/PDB is mandatory. The Non-CDB configuration has been abolished.

The AutoUpgrade process for 19c to 26ai follows the same methodology as 19c to 23ai:

# Analyze phase - no changes made, full pre-check report
java -jar autoupgrade.jar -config upgrade.cfg -mode analyze
# Fix everything reported in the analysis report, then:
java -jar autoupgrade.jar -config upgrade.cfg -mode deploy

Key pre-checks that will likely flag issues for 19c non-CDB databases:

• Non-CDB architecture: must convert to CDB/PDB before or during upgrade
• Deprecated initialization parameters from 19c
• Timezone file version (must be current)
• Invalid objects (run utlrp.sql first)
• Character set compatibility

The non-CDB to CDB conversion is the step that requires the most planning. I covered this in detail in an earlier post. If you’re on 19c non-CDB and planning to go to 26ai, that conversion is not optional — budget time for it in your upgrade project plan.

What This Means for Your 2026 Roadmap

If you’re currently on 19c: your upgrade target is 26ai, not 23ai. 23ai is being superseded. Premier Support for 19c runs until April 2027 — you have time to plan properly, but the planning should start now.

If you’re currently on 23ai (cloud): you may already be on 26ai features depending on your OCI service update schedule. Check your database version with SELECT * FROM v$version — if you see 23.26.x or later, you’re running 26ai features on the 23ai platform.

If you’re on-premises and have avoided the cloud specifically to avoid AI features: that argument no longer holds. 26ai is downloadable. The features are there. The question now is whether your organization’s Oracle strategy is ready for an AI-native database, regardless of deployment model.