Oracle has added new prebuilt agents to Private Agent Factory, its no-code framework for building containerized, data-centric agents within AI Database 26ai.
These agents include a Database Knowledge Agent, a Structured Data Analysis Agent, and a Deep Data Research Agent.
While the Database Knowledge Agent translates natural-language prompts into queries to fetch specific facts, policies, or entities, the Deep Data Research Agent tackles more complex tasks by breaking them into steps and iterating across web sources, document libraries, or both, the company said.
The Structured Data Analysis Agent, meanwhile, is aimed at crunching tabular data —think SQL tables or CSV files — using tools like Python’s pandas library to generate charts, spot trends, flag anomalies, and summarize metrics, the company added.
The addition of these agents and Private Agent Factory to AI Database 26ai will help developers accelerate agent building in a secure and simplified manner, especially for enterprises operating in regulated industries, helping move pilots to production, analysts say.
“With AI Database Private Agent Factory, teams will be able to rapidly create AI agents or leverage pre-built ones, turning experimentation into production-ready solutions quickly. By embedding intelligence at the core of the database, Oracle is enabling a new era of agentic AI, where sophisticated, autonomous systems and applications can adapt and act at scale,” said Noel Yuhanna, principal analyst at Forrester.
Oracle’s rationale, Yuhanna added, reflects its broader strategy of making the database a central pillar of enterprise AI, given that execution ultimately depends on where the data resides.
That view is echoed by Stephanie Walter, practice leader of AI stack at HyperFRAME Research, who says Private Agent Factory is Oracle’s attempt to position itself as “the operational control layer” in enterprises rather than just the storage layer, by bringing data and AI closer together and reducing the need for data movement and external orchestration.
“Every major cloud provider is moving toward tighter coupling between data, models, and orchestration. Oracle’s differentiation is that it starts from the database outward, while hyperscalers typically start from the model or platform outward,” Walter said.
That differentiation is more than architectural nuance, according to Bradley Shimmin, lead of the data intelligence practice at The Futurum Group.
“By architecting agent orchestration directly into the database, Oracle is letting enterprises drop the duct-tape approach of complex, brittle data-movement pipelines that I would say continue to plague cloud-centric ecosystems, even those emphasizing zero-ETL capabilities,” Shimmin said.
That tighter integration also feeds directly into a more pragmatic concern for regulated industries: keeping sensitive data under control as AI agents move from experimentation into production.
“Most agent frameworks today assume you’re comfortable sending data to external LLM providers and orchestrating through cloud-hosted services. For regulated industries—including banking, healthcare, defense, and government—that assumption is a non-starter,” said Ashish Chaturvedi, leader of executive research at HFS Research.
“The Private Agent Factory meets those customers exactly where they are: behind the firewall, with the drawbridge up,” he added.
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