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Oracle just made a pretty direct move into operational AI. Not the usual demo heavy generative AI stuff. Something closer to actual day to day enterprise use.
The company announced a new set of agent based AI capabilities built straight into its main database platform. The update was shared by Jenny Tsai Smith, Senior Vice President of Database Technologies at Oracle, during a media briefing hosted by Oracle Japan on April 3.
The direction is clear. Enterprises are moving past experimenting with generative AI tools. Now they want systems that actually do work. Not just generate content. Execute tasks. Handle workflows. Reduce manual effort.
That is what オラクル is trying to tap into here.
From AI Experiments to Actual Work Getting Done
A lot of companies have already played around with generative AI. Chatbots. content tools. some automation. But most of it sits on the side. It does not really plug into core business systems.
Oracle is pushing AI deeper. Right into the database layer where enterprise data already lives.
The idea is simple in theory. If AI agents sit close to the data, they can act on it faster. No need to move data around constantly. No need to build separate pipelines for every use case.
Smith made it pretty clear. Companies are now asking one thing. How do we get productivity gains from AI. Not just experiments. Not pilots. Actual output.
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What Oracle Actually Launched
There are a few different pieces here. All tied together but serving slightly different roles.
First one is the Oracle Autonomous AI Vector Database. This comes with a web based interface and APIs. Developers and data teams can build and manage vector databases without going too deep into complexity.
It supports multiple data types. JSON. relational data. graph. spatial. That matters because most enterprises do not have clean uniform data. Everything is mixed.
Also important. Oracle is offering this on low cost and even free cloud tiers. That is not random. It is clearly aimed at getting more teams to start using it without heavy upfront investment.
Then there is Oracle AI Database for Agent Memory. This is where things get more interesting. It stores interactions between users and AI agents over time.
So instead of stateless AI responses, you get context. The system remembers. It improves. It builds a layer of continuity. That leads to faster responses and better outputs over time.
Next is the Oracle AI Database Private Agent Factory. This one is less technical on the surface. It is a no code environment.
Business users can create AI agents without writing code. That is a big shift. Usually this kind of thing is locked inside engineering teams.
Pre-built agents are also part of the plan. Knowledge retrieval. data analysis. deep research using retrieval augmented generation. More of these will roll out gradually.
Security Is Not an Afterthought Here
Oracle is clearly aware of the pushback around AI risks.
Oracle Deep Data Security is part of the package. It gives centralized control over who can access what. Permissions are tied to roles. Everything can be audited.
It also tackles newer AI specific risks. Prompt injection attacks are one of them. These are becoming more common as AI systems interact with sensitive data.
Then there is the Oracle Private AI Services Container. This is for companies that do not want to rely on public cloud AI.
It uses containerized environments built on Kubernetes and Docker. AI models run inside controlled setups. Data stays where it should. Performance is still high.
For a lot of enterprises, especially in regulated industries, this matters more than flashy AI features.
Open Standards Instead of Lock In
Oracle also added native support for Apache Iceberg.
That means it can directly handle Iceberg based vector data. It also improves indexing for faster search.
The bigger message here is about openness. Enterprises are tired of getting locked into proprietary systems. Oracle is trying to position itself as more flexible this time.
Whether the market fully buys that is another question. But the intent is clear.
日本のテック業界にとって意味すること
This is landing at an interesting time for Japan.
Companies are under pressure. Labor shortages are real. The workforce is aging. Productivity has to improve without simply adding more people.
At the same time, a lot of enterprises are still running on legacy systems. Some of them are decades old. That slows everything down.
Oracle’s approach tries to remove some of that friction. Instead of forcing companies to rebuild everything, it layers AI into existing data systems.
The no code angle is also important. Japan does not have unlimited advanced tech talent. Letting business teams build AI agents themselves changes adoption speed.
This could play out across manufacturing, finance, retail, logistics. Pretty much any sector that relies heavily on structured and unstructured data.
日本で事業を展開する企業にとっての意味
For companies on the ground, the shift is subtle but important.
AI is no longer just for insights. It is moving toward action. Systems that do things. Automate workflows. Handle internal processes.
The security and private deployment angle is also very relevant in Japan. Data privacy expectations are high. Risk tolerance is low.
Industries like banking, healthcare, and government tend to move slower with public cloud AI. Container based private AI setups may fit better into their compliance frameworks.
Support for open standards like Apache Iceberg also gives companies more flexibility. They are not locked into one path. They can experiment more.
That could lead to more collaboration across vendors and platforms.
Bigger Shift Happening in the Background
This is not just Oracle shipping new features.
It reflects a bigger shift. AI is merging with data infrastructure and business operations. The boundaries are getting blurry.
Software used to be tools. Now it is starting to act more like autonomous systems.
For Japan, this creates both upside and pressure.
Companies that adopt agent based AI early can improve efficiency and move faster. Those that delay may struggle to keep up, especially as global competition increases.
At the end of the day, this is about how companies interact with their own data.
Oracle is betting that the future is not just analyzing data. It is acting on it in real time.
That shift is going to define how enterprise tech evolves over the next decade.
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