Oracle targets production bottleneck as enterprises struggle to deploy AI agents at scale
While nearly 70% of organizations plan to roll out AI agents, most stumble when moving beyond prototypes into live environments. Oracle is repositioning its database platform to address the security, reliability and operational challenges that block production deployments.
The ability to construct a working AI agent demonstration has become routine for many enterprises. Translating that proof of concept into a production system, however, remains a significant hurdle. Organizations frequently encounter obstacles around data protection, system reliability and cost control when attempting to move agents beyond the lab and into operational environments.
This widening gap between prototype and production is prompting database vendors to reconsider their platform architecture for AI workloads. According to Çetin Özbütün, executive vice president of product and research, data warehouse and autonomous database at Oracle Corp., nearly 70% of organizations intend to deploy AI agents, but the number that successfully reach production remains small. Özbütün outlined the challenge during a conversation with theCUBE Research's Dave Vellante at Oracle AI World.
AI is very easy to demonstrate, but when you try to bring it into production, enterprises usually encounter four different deployment difficulties. The first of them is around security. It's very hard to securely provide access to AI agents, and that's one of the top challenges.
Çetin Özbütün, Oracle Corp.
Building an AI architecture agents can trust
Security represents the initial barrier to production deployment. AI agents are uncovering database vulnerabilities at scale, and organizations frequently lack the ability to apply patches before malicious actors can exploit those weaknesses. Oracle's Autonomous AI Database addresses this through default encryption of data both at rest and in transit, with protections that cannot be disabled by users.
As soon as the vulnerability is found, before it's released anywhere else, we bring it into production in Autonomous AI Database.
Çetin Özbütün, Oracle Corp.
Continuous uptime represents the second critical requirement. Since AI agents operate continuously and generate unpredictable, intensive demands on data infrastructure, Oracle commits to 99.995% availability without exceptions. This metric reflects whether clients can establish connections and execute queries, rather than merely whether underlying systems are operational.
We patch our systems on a weekly basis, for example. All of our patches are online patches, so the database is up and running, accepting new connections, accepting new requests while we're running the patch.
Çetin Özbütün, Oracle Corp.
Oracle's Deep Trust AI architecture embeds security enforcement directly into the database layer, allowing organizations to define and apply their own security policies. The company releases new capabilities, such as expanded Deep Data Security features, into Autonomous AI Database immediately upon announcement, ahead of on-premises product releases.
https://www.youtube.com/embed/a-DuXwXcOlQ?feature=oembed
You do nothing as the user or administrator of Autonomous Database to get this functionality. As soon as a new functionality is available, like Oracle Deep Data Security, it becomes available for you to use on day one.
Çetin Özbütün, Oracle Corp.