Analyst(s): Brad Shimmin, Keith Kirkpatrick
Publication Date: March 25, 2026
Oracle has unveiled a sweeping set of agentic AI innovations that embed autonomous reasoning and persistent memory directly into its Oracle AI Database 26ai and Fusion Applications suite. By converging vector, JSON, graph, and relational data into a single engine, Oracle is attempting to eliminate the integration tax of fragmented AI stacks while enforcing security natively at the database row and column levels. This architectural move positions the database as the primary control point for enterprise automation, challenging the dominance of standalone vector stores and external orchestration frameworks.
What is Covered in This Article:
- Oracle’s launch of Oracle AI Database 26ai, featuring agentic innovations such as the Oracle Unified Memory Core and the Oracle AI Database Private Agent Factory.
- The expansion of Oracle AI Agent Studio for Fusion Applications including a new Agentic Applications Builder and Agent ROI Dashboard.
- Technical breakthroughs in Oracle Deep Data Security and Trusted Answer Search to mitigate LLM hallucinations and prompt injection.
- The strategic market impact on hyperscalers and best-of-breed AI startups as Oracle embeds AI as a native feature of its high-performance data infrastructure.
- Market intelligence from The Futurum Group highlights the expansion of the broader data and AI market to a projected $1.2 trillion by 2031.
The News: On March 24, 2026, Oracle announced a comprehensive suite of agentic AI innovations for the Oracle AI Database 26ai and its Fusion Applications ecosystem. Key announcements include the Oracle Unified Memory Core, which provides a stateful, persistent memory for AI agents within the database engine, and the Oracle AI Database Private Agent Factory, a no-code platform for deploying data-centric agents as portable containers.
Simultaneously, Oracle expanded its AI Agent Studio for Fusion Applications with an Agentic Applications Builder, allowing business users to orchestrate autonomous, multi-step workflows by embedding coordinated teams of AI agents into its Fusion Cloud Applications suite to automate and execute outcome-driven business processes.
These updates aim to address persistent enterprise friction points: data latency, security exposure, and the complexity of managing fragmented AI pipelines.
Oracle Positions AI Database 26ai to Lead $1.2 Trillion Market by Bridging the Agentic Reasoning Gap
Analyst Take: Oracle’s recent moves signal a calculated departure from the industry’s obsession with external, stateless AI architectures. By embedding agentic orchestration and persistent memory directly into the Oracle AI Database 26ai, Oracle is making a bold architectural bet: the database, not the Large Language Model (LLM), should be the primary control point for enterprise automation. This approach addresses the integration tax that has plagued early Retrieval-Augmented Generation (RAG) implementations, where data must be constantly shuttled between operational databases, standalone vector stores, and external models.
Solving the Integration Tax Through Convergence
The introduction of the Oracle Unified Memory Core is a technical attempt to give AI agents a brain that resides where the data lives. In most current AI setups, by the time an external agent retrieves context from a separate vector store, the underlying business reality (e.g., inventory levels or a customer’s credit status) may have already changed. This transition reflects a broader trend Futurum has been tracking: the move from manual pipeline plumbing to sophisticated agentic oversight. As detailed in the 1H 2026 Data Intelligence, Analytics, & Infrastructure Market Sizing & Five-Year Forecast Report, we are witnessing a shift from the “Data Technician” to the “AI Shepherd,” where the value lies in governing autonomous behavior rather than just writing syntax or moving bits.
By consolidating reasoning across vector, JSON, graph, and relational data within a single transactional engine, Oracle ensures that AI agents operate on a single version of the truth with the same ACID guarantees that govern mission-critical use cases, such as financial transactions. This convergence is technically superior to the typical “duct tape and baling wire” approach of modern RAG. When you extract data from a governed database to persist it in an external system, you expand the attack surface and lose the lineage. Oracle essentially treats the AI agent as a database-resident function. Think of it as a stored procedure with a PhD in autonomous planning.
The Strategic Moat and Market Ripple Effect
Oracle is effectively poisoning the well for pure-play AI automation startups. By providing the Agentic Applications Builder and prebuilt agents at no additional cost to Fusion Applications subscribers, they have set a high bar for third-party vendors. If a CIO can achieve 80% of their automation goals using tools already included in their existing ERP or HCM subscription, the hurdle for a third-party vendor to justify a new per-user license fee becomes nearly insurmountable.
According to The Futurum Group’s 1H 2026 Data Intelligence, Analytics, & Infrastructure Market Sizing & Five-Year Forecast Report, the broader data and AI market is projected to reach US$541.1 billion in 2026, growing at a 16.9% CAGR to surpass US$1.2 trillion by 2031. Oracle is positioning itself to capture this growth not by selling AI as a standalone product, but by selling AI as a feature of its high-performance data infrastructure. This strategy directly challenges hyperscalers who often promote fragmented stacks that require customers to manage separate vector databases and orchestration layers. The Futurum Signal for Agentic AI Platforms for Enterprise report currently places Oracle in a leadership position precisely because it has moved AI from a conversational experiment to a governed, production-scale workflow engine.
The Oracle Stack Gravitational Pull
Despite Oracle’s progress in openness—such as supporting Apache Iceberg through “Vectors on Ice” and embracing the Model Context Protocol (MCP)—the very gravity of Oracle’s stack remains a potential source of vendor lock-in. While Oracle is making strides in interoperability, the most seamless, high-performance experience is only available when an organization is all-in on the Oracle ecosystem. Enterprises with highly fragmented multi-cloud strategies may find the converged engine story less compelling if their customer data lives in one cloud while financial data sits in another.
Enterprises must weigh the ease of integrated tools against the risk of being unable to easily migrate these automated workflows if pricing models or strategic priorities change. However, for the Oracle-centric shop, the performance gains of running agents on-metal inside the database are too significant to ignore.
Moving Beyond the Application Layer
The release of Oracle Deep Data Security addresses a foundational flaw in many AI-first startups: the reliance on fragile application-level security. According to Futurum’s 1H 2025 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey Report, data quality, trust, and governance are the single largest points of dissatisfaction for 20% of enterprise respondents. Oracle is attacking this trust gap head-on.
By enforcing row-level and column-level access controls natively at the data source, Oracle ensures that if a user isn’t authorized to see a specific record, the AI agent physically cannot retrieve it, regardless of how cleverly the user might prompt the model. This is a crucial guardrail for regulated industries where trusted answer search (e.g., using deterministic retrieval over probabilistic synthesis) is a non-negotiable requirement. Most business leaders don’t want their AI to be creative with inventory or payroll. They want trusted facts. By using AI Vector Search to match natural language questions to vetted reports, Oracle provides the predictability of traditional BI within the accessibility of a chat interface.
Empowering the Citizen Developer via Agent Studio
The application layer expansion via the Oracle AI Agent Studio for Fusion Applications signals a transition from task-based AI to outcome-driven autonomous operations. The new Agentic Applications Builder utilizes natural language to allow users to construct applications focused on specific business outcomes. This aligns with the rocket ship growth trajectory Futurum is tracking for the Semantic Layer, which the 1H 2026 Data Intelligence, Analytics, & Infrastructure Market Sizing & Five-Year Forecast Report projects will grow by 19% in 2026 as it becomes the critical control plane for preventing AI hallucinations.
As agentic AI shifts from pilot projects to production, Oracle positions this as the next step in enterprise AI maturity, moving from copilots and discrete automations to orchestrated agent swarms capable of handling complex, cross-functional tasks. According to Futurum Group’s 1H 2026 Enterprise Software Decision Maker Survey (n=830), 39% of organizations expect GenAI to be delivered primarily via agents, with 43% ranking GenAI capabilities as a top software purchase criterion.
In a briefing for industry analysts, Steve Miranda, Oracle’s Executive Vice President, Applications Development, said Oracle is adding 23 new agentic applications, which allow users to set business objectives and receive AI-driven recommendations. Miranda said AI studio also enables users to create custom agents that aren’t designed to replace existing Fusion applications, but serve as a way to agentically and dynamically handle the processes that bridge between business functions, applications, or departments.
In essence, these agents are designed to sit on top of existing applications to enable users to more efficiently handle work processes that traditionally would’ve required API calls, and still may not have presented the data or outputs in the formats desired by users.
Two potential challenges may emerge with these types of agentic applications. First, while Miranda says that IT can set guardrails to ensure that worker-created agents are properly vetted and approved before being used, careful attention must be paid to ensuring that these guardrails respect organizational and regulatory rules around processes and data. This approach, while prudent, may slow rollout and deployment times, which are key selling points.
Furthermore, because Oracle does not charge an additional fee for using AI, and instead incorporates it into its traditional SaaS licensing fee, demonstrating value can be challenging. That’s likely why Oracle has announced the addition of its Agent ROI Dashboard. This dashboard is able to report on metrics such as time to task completion, close rate, number of turns, and other key telemetry statistics, which should help organizations address the performance of their agents.
By providing tangible metrics on operational cost reductions and task time saved, Oracle is giving IT leaders the evidence they need to move the conversation from qualitative vibes to hard, quantitative data that may eventually be correlated with direct financial impact, the key factor that helps business leaders assess the success of software investments.
What to Watch:
- Watch for whether AWS (Bedrock) or Google Cloud (Vertex) attempts to further collapse their data layers to match Oracle’s converged engine performance.
- Monitor the adoption rate of the Agentic Applications Builder among non-technical business users to see if mapping company-specific jargon into the AI Agent Studio overcomes the complexity of corporate taxonomies.
- Observe how many third-party agent frameworks (like LangChain or CrewAI) actually leverage the Autonomous AI Database MCP Server to verify Oracle’s claims of openness.
- As Salesforce pushes Agentforce, the battle will shift from marketing mindshare to the reliability of the underlying data plumbing—an area where Oracle holds a technical advantage.
- As agents gain autonomy, watch for the adoption of scoring systems like the Futurum ORBS Trust and Authentication Platform (FOTAP) to evaluate the transparency and ethical deployment of these autonomous systems.
See the complete press release on Oracle Unveils AI Database Agentic Innovations and the Expansion of AI Agent Studio for Fusion Applications on the Oracle website.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
Other Insights from Futurum:
Dataiku Pivots to AI Success. Can One Control Plane Master a Multi-Cloud Agent Wilderness?
Can a Database Truly Be a Genius? – IBM’s Shift Toward Agentic Autonomy
Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
