Oracle today announced agentic AI innovations architected directly into Oracle AI Database, including Private Agent Factory and Deep Data Security. Check out the main announcement blog and the press release for all the details. Industry analysts and influencers also commented on today’s news, from the value it provides for customers to its competitive advantages. Following are some of their comments:
“All too often, AI models running against the database are playing a game of telephone: the model manages its own context window while the database handles the transaction state. Add agents to the game, and the level of uncertainty on whether model context and database state are in sync compounds. Oracle AI Database’s unified agent memory ends this game of telephone by unifying context and the data state within a single, shared memory pool. It delivers atomic synchronization ensuring real-time fidelity, eliminating staleness, and optimizing inference throughput. With Oracle AI database unified agent memory, you can be sure that your model and data will be on the same page.”
—Tony Baer, Principal, dbInsight
“We’re entering the era where AI agents become the new interface to enterprise systems. But agents are only as powerful as the data infrastructure behind them. The Oracle AI Database platform, combining trusted enterprise data, scalable vector capabilities, and built-in memory for agents, will define the next generation of AI applications.”
—Steve Nouri, CEO, GenAI.Works
“Agentic AI is rapidly becoming a driver of transformational change. However, adopting and integrating Agentic AI in the enterprise has proven difficult without a data foundation that supports a variety of data types in near real-time, from transactional and analytical to spatial, vector, and document data that LLMs can use for inference and decision making. As an enterprise-class converged database, Oracle AI Database brings these capabilities together with built-in tools for vector embeddings and vector search across internal and external data, including in Apache Iceberg tables. Even with all of these tools, agent building and deployment have remained an expert task; Oracle is now helping simplify the process with a drag-and-drop GUI in AI Database Private Agent Factory, while at the same time Deep Data Security helps address AI-era threats and provides guardrails for agents working within Oracle AI Database. With these latest enhancements, Oracle’s converged database continues to evolve for the age of Agentic AI.”
—Devin Pratt, Research Director, IDC
“Autonomous AI Vector Database is a low-cost, fully managed vector database with superior capabilities for AI developers and data scientists. It enables organizations to integrate AI vectors with business data for mission-critical applications, including transactions, analytics, parallel SQL, and scale-out—backed by enterprise-grade security, one-click scalability, diverse data types, and a free tier. This is a transformative breakthrough that advances AI innovation without compromising reliability.”
—Ron Westfall, Vice President and Practice Leader, HyperFRAME Research
“A solid data foundation is no longer a nice-to-have, in many ways it’s the make-or-break factor for AI success. While LLMs excel with public knowledge, they falter without seamless, governed access to trusted and controlled knowledge bases of private business data. With agentic AI now demanding real-time operational data for autonomous decision-making, solutions such as Oracle’s AI Database—trusted by 97% of Fortune Global 100 customers—are critical, architecting AI directly into the database for simple, fast, secure access. Organizations ignoring this foundation risk not just project failures, but irrelevance in an AI-driven economy.”
—Stephen Catanzano, Senior Analyst, Omdia
“A lot of enterprise AI still looks impressive in demos but becomes much harder in real deployments. What stands out in Oracle’s approach with Oracle AI Database is bringing agent capabilities closer to trusted enterprise data. As AI systems start needing memory, context, and reliable execution, that architectural shift can make agentic AI much more practical to deploy in real-world enterprise environments.”
—Alex Wang, Head of Education Strategy, GenAI.Works
“In the era of agentic AI, a unified memory core is essential for agents to maintain context across diverse data types, such as vector, JSON, graph, columnar, spatial, text, and relational, without the latency or staleness of external syncing. Only Oracle AI Database delivers this in a single, mission-critical engine with concurrent transactional and analytical processing, high availability, and ironclad security, enabling real-time reasoning over live business data. Organizations without this foundation will struggle with fragmented, unreliable agents, while those leveraging Oracle gain a decisive edge in scalable AI deployment.”
—Steven Dickens, CEO and Principal Analyst, HyperFRAME Research
“In today’s AI landscape, where numerous platforms require routing sensitive data through external services, Oracle’s AI Database Private Agent Factory emerges as a unique offering. A no-code agent factory empowers the data platform’s consumption layer for business analysts with visual builders, pre-built agents, and multiple deployment options, delivering the full potential of Oracle AI Database without exposing data to third parties. It’s the superior choice for enterprises demanding ironclad security, rapid scalability, and true AI for data innovation.”
—Rob Strechay, Principal Analyst, theCUBE Research & Smuget
“Fractured AI—scattering agents across tools, single-purpose databases and clouds—is like a brain fumbling through notebooks and devices: slow and inefficient. Oracle AI Database integrates everything seamlessly in its core engine, delivering real-time agentic AI like a unified mind or nonstop flight. Competitors can’t match this speed and simplicity for business-critical decisions.”
—Ron Westfall, Vice President and Practice Leader, HyperFRAME Research
“With the announcement of an extensive set of agentic AI tools and solutions, Oracle is now in a position to address the complexities of the AI Stack, as defined by HyperFRAME Research. Data gravity is a driving force of the AI Stack. Oracle AI Database is already a system of record for many of the world’s largest corporations. With its core agent memory capabilities, Oracle AI Database is extending this foundation to support enterprise data, agents, and memory. Other AI Stack complexities that Oracle addresses include: Oracle’s converged database bringing agentic AI close to the data that models depend on while reducing the integration challenges of point solutions; Exadata-powered Oracle AI Database delivering the performance and scalability to support agentic AI applications across multicloud, hybrid, and on-premises deployments; enabling business users, with Private Agent Factory, and developers, with Autonomous AI Vector Database, to more easily develop, deploy, and manage agents; and providing data security at the source to help prevent unauthorized access by users, agents, and LLMs, as well as data leakage to third parties. With Oracle AI Database helping customers manage the complexities of the AI Stack, they will be better positioned to move from experimentation to enterprise-scale deployment.”
—Stephanie Walter, Practice Leader, AI Stack, HyperFRAME
“To successfully deploy agentic AI across the enterprise requires a data foundation that can scale to support new and unpredictable levels of workloads and provide secure access to business data. Oracle AI Database, powered by Exadata—and available on all the leading clouds—provides that data foundation with deep data security built-in at the source. With most of the world’s corporate data already in Oracle databases, adopting agentic AI is now that much easier. Using Oracle AI Database, customers can choose AI models, agentic frameworks, and build, deploy and run agents against all of their data—structured and unstructured—without moving it. For Oracle AI Database customers, this is clearly the fastest, easiest and safest path to integrating agentic AI into their business processes.”
—Holger Mueller, Vice President and Principal Analyst, Constellation Research
“Oracle just redefined the agentic AI marketplace in a substantially clever way. Oracle made unified agents an integral part of the Oracle AI Database. That means agents store context in a single place. That’s hugely significant because it eliminates the complexity of multiple formats, single-purpose databases, inefficient stops, and even multiple types of agents. Unlike alternative externally orchestrated agents, Oracle’s Private Agent factory empowers true in-database agentic AI with tenancy-isolated instances, built-in agent orchestration, and superior native privacy. A game changer for enterprises advancing to agentic AI.”
—Marc Staimer, Sr. Contributor to theCUBE Research
“Oracle just unleashed the world’s first ‘no limits’ vector database. It’s the only one that scales into the full Autonomous AI Database—with graph, spatial, and beyond. Start lean, then as AI demands more uniform and secure access to an increasing number of data types, grow to full power with just one click, and eliminate extra subscriptions and lower your overall monthly cloud bill.”
—Steve McDowell, Principal Analyst & Founder, NAND Research
“As KuppingerCole Analysts has consistently recognized over nearly a decade of Leadership Compass research, Oracle has established a strong position in enterprise data security. With the rise of agentic AI, the challenge is shifting toward governing how autonomous systems access and use enterprise data. Oracle’s approach is to integrate trust directly into the core of the Oracle AI Database, not only protecting data against emerging AI-era risks but also providing a secure environment for running AI models. Deep Data Security enforces fine-grained controls at the data layer, down to row and column level. This is particularly relevant when AI agents generate queries dynamically or interact with data through retrieval-augmented techniques. In parallel, the Private AI Services Container enables organizations to run AI models in controlled private environments, reducing the risk of exposing sensitive data to external providers. Together, these capabilities reflect an integrated, data-centric approach to securing agentic AI, where trust, governance, and control are built into the platform itself.”
—Alexei Balaganski, CTO and Lead Analyst, KuppingerCole Analysts
“As agentic AI pushes enterprises to reason over more data, faster—often across multiple clouds—open table formats like Apache Iceberg become a staple for customers. Oracle’s support for vectors stored directly in Iceberg tables and object storage (“vectors on ice”) is aimed at enabling low-latency retrieval without forcing customers into walled garden data silos or outdated pipelines. Combined with Oracle’s Open Agent Specification for portable workflows and integrations across frameworks and major model ecosystems (e.g. LangChain, Bedrock, Vertex AI, and OCI Generative AI), Oracle’s emphasis is on interoperability, security, and minimizing lock-in. Because Oracle’s AI Database deployment options span hyperscalers and on-prem estates, organizations can align AI architectures to data locality and compliance edicts without migrating core data and workflows.”
—Dave Vellante, Co-CEO and Chief Analyst, theCUBE Research
“Enterprises are clamoring for tools and techniques that enable them to incorporate Agentic AI into their business workflows. Oracle has released an impressive array of agentic AI capabilities built into Oracle AI Database that address this need. First, it supports the rapid development and deployment of agents while maintaining security and guard rails. Second, it provides a unified memory core, which lets users store context for AI agents—one system of record for agents, memory, and enterprise data—perfectly leveraging Oracle’s converged database architecture. Third, it includes a fully-managed vector database that enables developers to build vector-powered applications very quickly. Because the vector database is built on Oracle Autonomous AI Database, it inherits all its enterprise-grade qualities and offers a one-click upgrade to the full database. Oracle has made a straightforward task of building an agentic AI system that greatly streamlines workflows, expands the intelligence of the enterprise, and delivers reliable, deterministic outcomes.”
—Carl Olofson, Principal Analyst, DBMSGuru LLC
“Agent memory is one of the messier problems in enterprise AI right now. Teams are interfacing with vector stores, graph databases, and document layers that don’t share a security model. Oracle’s move to collapse that into the database layer is the right direction, and we expect other database vendors to follow.”
—Alex Wurm, Principal Analyst, Nucleus Research
“According to Deloitte, 75% of companies plan to invest in agentic AI. However, only 11% have agents running in production. Part of this adoption gap is that companies don’t know how to get started with agents. Oracle addresses this gap by introducing tools that make it easier for business users and developers to build agents. Oracle AI Database Private Agent Factory is a GUI-based, no-code platform that enables business users to build, test, and deploy intelligent data-centric agents or workflows. For developers, Oracle Autonomous AI Vector Database accelerates production, vector-powered applications with intuitive APIs and an easy-to-use web interface. With these tools and other capabilities, Oracle AI Database is enabling organizations to move agentic AI from investigation and experimentation to production at enterprise-scale.”
—Richard Winter, CEO, Wintercorp
“In today’s agentic AI world, agent frameworks are exploding, and each framework is inventing its own way to define data access, reasoning, and workflows. This can create a nightmare for developers through both complexity and lock-in. What enterprises need is a way to develop an agent once and make it available across models and frameworks. This “develop once and use everywhere” approach to AI agents is what Oracle proposes in the Open Agent Spec, an open-source, framework-agnostic configuration language for defining AI agents and agentic workflows. It makes agents portable across platforms, similar to how ONNX standardizes portability for ML models, and it supports protocols such as MCP and A2A. Through Oracle’s Agent Spec’s modular design, developers can literally create independent building blocks, delivering fast reuse and easy composition of AI agents. This specification, which Oracle is releasing under Apache and Universal Permissive License, comes at a good time for Oracle, its developer ecosystem, and beyond to the industry. Its strategy is to save developers a lot of repetitive work and accelerate the adoption of enterprise-scale agentic AI.”
—Bradley Shimmin, Vice President & Practice Lead, Data Intelligence, Analytics & Infrastructure The Futurum Group
“Agentic AI is transforming enterprise security. Risks now extend beyond user logins to include which agents can see and process data, what they can infer by combining sources, and which actions they can take, such as initiating transactions or triggering workflows across the data estate. Traditional application-layer and identity controls remain necessary, but they are no longer sufficient on their own in this setting. Oracle AI Database Deep Data Security handles these risks by enforcing granular, auditable access at the data layer for both users and AI agents. Controls are anchored in an auditable system of record that applies policies and trusted data filters. Effective data-layer controls are an essential precursor for the defense against AI-driven threats which will require support from AI agents and frontier models.”
—David Floyer, CTO, theCUBE Research