Disclosure: This report was commissioned by Oracle and conducted independently by The Futurum Group.
Enterprises are aggressively realigning their strategies around a singular board-level mandate: the transition from conversational AI to autonomous, agentic systems. This directive demands a move far beyond basic chatbots toward AI Shepherds capable of reasoning, planning, and executing complex business processes. Significant capital backs this ambition. According to Futurum’s 1H 2026 Data Intelligence, Analytics, & Infrastructure Market Sizing & Five-Year Forecast Report, the broader data and AI market should reach US$541.1 billion in 2026, growing at a 16.9% CAGR to surpass US$1.2 trillion by 2031. The Futurum 1H 2026 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey confirms that nearly half of enterprise leaders now rank AI-augmented/Agentic Analytics together with basic generative AI (GenAI) & large language models (LLMs) as the two top trends of 2026 (see Figure 1).
Figure 1: Expected Top Trends in Data Management & Analytics
Q: “Which of the following do you expect to be a top trend in data management and analytics between 2026 and 2029?” Base: 1H 2026 (n=818).
Source: Futurum Research, 1H 2026 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey.
The enterprise mandate has officially shifted beyond conversational chatbots. Decision-makers are now prioritizing autonomous, agentic systems capable of executing complex workflows over basic generative AI. However, this vision currently clashes with a harsh operational reality in which legacy infrastructure struggles under the weight of these new workloads. The primary barrier to success stems from the brittleness of the underlying data foundation rather than a lack of model intelligence. Ambition remains high, but as projects move from experimentation to reality, data movement has become the primary enemy of innovation. When agents search for context across fragmented silos, they suffer from high latency, massive token costs, and frequent hallucinations.
As this hype cycle matures, such pressures will certainly mount as companies seek to move from pilot to measurable value in real-world production environments. The current strategy of bolting AI onto the application tier imposes a cognitive tax, shifting compute cycles from reasoning to plumbing. To achieve the ROI demanded by stakeholders, infrastructure must evolve to treat AI as a native inhabitant of the data core.
To activate autonomous agents at scale, organizations must overcome the architectural flaws inherent in the first wave of generative AI implementations. For the past year, the industry has treated AI as an application-tier science project, an approach that flies directly in the face of enterprise readiness through escalating technical debt and debilitating architectural compromise that prevent enterprises from gaining the full value of agentic AI.
For example, agentic amnesia remains a major hurdle in enabling agents to reason, make decisions, and take actions autonomously. Most current AI agents are ephemeral and stateless, losing context as soon as a session ends. This requires developers to build complex, external memory layers using standalone vector stores and bespoke code. The result is a fragmented experience in which agentic systems cannot learn from prior actions or reason consistently across historical business records. Without persistent, data-resident memory, an agent operates like a genius goldfish with a ten-second attention span – impressive in a demo, but useless for running a supply chain at scale.
The weight of building and maintaining integration points has also reached a breaking point. Shuttling large amounts of enterprise data from secure databases to an external application-tier agent introduces security risks and latency. Fragmented stacks, where vectors, JSON, and relational data live in separate specialized stores, impose a decided complexity penalty that must be paid in both time and money. Every time an agent needs to check customer credit (relational) against recent support transcripts (JSON) while finding similar historical cases (vector), it must perform a distributed join over pipelines connecting multiple systems. This plumbing-heavy approach represents the single largest source of dissatisfaction among enterprise respondents in Futurum research (see Figure 2).
Figure 2: Top Data-Specific Factors Contributing to AI Project Failure
Source: Futurum Research, 1H 2026 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey.
AI project failures are driven primarily by deployment complexity and integration bottlenecks, highlighting the severe operational “tax” imposed by fragmented, application-tier AI architectures. Though not as readily apparent as integration complexity, the illusion of openness itself stands as a silent limiting factor for most enterprises. Many modern data platforms champion open table formats such as Apache Iceberg while subtly steering users toward proprietary catalogs in order to find and access data. This can force a choice between true cross-platform interoperability and performance, creating a velvet rope ecosystem that locks data away from the AI that needs it the most. Such infrastructure scalability bottlenecks prevent teams from focusing on higher-value applications.
A new architectural approach is required, one that delivers on the promise of autonomous systems without forcing these compromises. This new paradigm relies on four core principles that treat AI agents as secure, data-resident functions.
Reasoning at the source optimizes performance and security. The ideal foundation embeds execution, persistent memory, and security directly into the database engine, eliminating sluggish data movement. By treating the AI agent as a database-resident function, organizations ensure the agent operates on a single version of the truth with the same ACID guarantees that govern mission-critical transactions. Reasoning where the data lives enables zero-latency context, which is essential for grounding retrieval-augmented generation (RAG) in real-time operational data.
A modern AI foundation must possess multi-model fluency across documents, relational data, graphs, and vectors. Crucially, it must extend these capabilities to open table formats such as Apache Iceberg. This allows a converged engine to handle diverse data types without requiring data movement or duplication. This high- performance experience honors the fact that data has gravity. Whether the information is a JSON document in a lake or a relational record in a warehouse, the agent should query it through a single, unified interface.
To prevent hallucinations in mission-critical environments, the data foundation must enforce strict business logic, replacing probabilistic guesswork with deterministic facts. By using a centralized semantic layer, the architecture can ensure that AI agents operate on a unified set of business metrics and retrieve only grounded, validated information.
Security policies must be enforced natively at the data source using deep row-, column-, and cell-level controls. By anchoring permissions directly in the database, organizations know that if a user is not authorized to see a specific record, the AI agent acting on the user’s behalf cannot retrieve it – regardless of how cleverly a prompt is engineered. This proactive, data-resident security stance significantly reduces implementation complexity and inconsistency of application-level access controls while closing long-term compliance gaps.
Oracle AI Database 26ai, Oracle Autonomous AI Database, and Oracle Autonomous AI Lakehouse are engineered to deliver on this converged paradigm. Oracle is making a strategic play by addressing the flaws of the app-tier AI model, positioning the database as the primary control point for enterprise automation. By merging high-performance relational engines with open data lakes, Oracle has established its strategy as the primary execution environment for autonomous enterprise AI.
This release advances Oracle’s AI Designed for Data vision by integrating AI across the entire stack, including development, management, and analytics. Currently, enterprises are struggling to provide real-time working memory to their AI applications, relying heavily on complex external workarounds or static batch data (see Figure 3). A central piece of Oracle’s new architecture, the Unified Agent Memory Core, addresses this persistent statelessness problem directly. By building factual, short-term, and long-term experiential memory directly into the engine, Oracle allows agents to reason across diverse data types without the friction of external orchestration. This memory is tiered into working memory for ongoing conversations, experiential memory for learning from prior actions, and factual memory grounded in business records.
Figure 3: Current Enterprise Strategies for Agentic "Working Memory"
Source: Futurum Research, 1H 2026 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey.
The reliance on external vector databases, custom stream processors, external AI caches, and static batch data underscores the severe architectural fragmentation organizations face when equipping AI agents with real-time context.
Recognizing the multi-cloud reality, Oracle AI Database 26ai runs on OCI, AWS, Azure, and Google Cloud while Oracle Autonomous AI Lakehouse supports Apache Iceberg and the Delta Share protocol across the same hyperscaler clouds. The platform includes the Autonomous AI Database Catalog, which provides plug-and-play SQL access to data across Databricks Unity, AWS Glue, and Snowflake Horizon without requiring disruptive data movement. This approach solves the last-mile problem, enabling AI insights to be seamlessly operationalized within core transactional workflows.
Oracle AI Database 26ai moves beyond basic vector search by infusing agentic reasoning with hardware-level optimizations. The connection between enterprise pain points and Oracle’s architectural response explains the company’s physics-based performance advantage, which hinges on its highly converged and integrated technology stack.
To illustrate, in an effort to maximize inference efficiency, the industry is embracing ideas such as “Agentic Pruning,” an AI strategy that dynamically trims reasoning paths to save tokens, removes unnecessary agentic memory traces, and cleans up RAG indexes. This can speed up inference and reduce token costs, but the underlying infrastructure can quickly become the critical bottleneck due to data processing and movement overhead. Using a mix of semantic intelligence and brute-force hardware engineering, Oracle can prune the data top-down before it ever reaches the model using enriched semantic metadata from the Oracle Autonomous AI Data Catalog.
Relatedly, one of the biggest hurdles for Natural-Language-to-SQL (NL2SQL) at scale is the search space. When an LLM attempts to find an answer across thousands of schemas and millions of columns, the context window can quickly expand beyond available boundaries, leading to high costs, context rot (forgetfulness), and frequent hallucinations. Oracle AI Database 26ai addresses this problem by providing its own execution environment, performing coarse-to-fine semantic summarization at the data source. The database acts as an intelligent gatekeeper, ensuring the AI agent only sees relevant, pruned, and secure data before it attempts to generate a response.
Simultaneously, the company’s Lake Cache capability, used by the Autonomous AI Lakehouse, addresses network latency when querying object storage. In a standard multi-cloud configuration, a TPC-DS type query on external object storage can take a significant amount of time. By using Exascale infrastructure to treat external storage as local NVMe, Oracle reduces query response times. In addition, Oracle Data Lake Accelerator automatically spins up multiple query servers to speed up scanning of terabytes to petabytes of object store and deliver significantly faster query performance.
Oracle promises this kind of performance by encompassing the infrastructure’s ability to optimize itself autonomously while the data remains in an open lake (see Table 1).
Table 1: Problem/Solution Matrix — The Agentic Frontier
| The Enterprise Pain Point | Oracle's Architectural Solution |
|---|
Source: Futurum Research
Furthermore, native support for the Model Context Protocol (MCP) allows the database to act as a universal translator. It integrates directly with external agentic frameworks such as LangChain, LangGraph, and Amazon Bedrock. Instead of forcing developers to write custom connectors, Oracle provides a standardized way for agents to access the database for secure, grounded insights. This positions the database as the primary control point in the modern AI stack. Oracle recently introduced managed MCP servers in OCI to help enable secure, enterprise-grade agentic access to Oracle Database environments.
For mission-critical resilience, Oracle has formalized availability into its Maximum Availability Architecture Platinum and Diamond tiers. The Platinum tier delivers sub-30-second disaster failover on Exadata, while the Diamond tier targets ultra-critical applications with sub-three-second failover with zero data loss using active-active logical and synchronous Raft-based replication. In the agentic AI era, where hundreds or thousands of autonomous agents demand massive concurrency, even a minor synchronization lag can result in downtime and possible business consequences when an agentic decision or action goes awry. By offering mission-critical tiers, Oracle establishes performance levels that ensure the predictability required for 24/7 autonomous reasoning.
The transition from data architecture to data intelligence is accelerating, and the era of treating AI agents as application-layer science projects is coming to a close. Success in this US$1.2 trillion market will rely on the ability to provide real-time, secure context at the data source rather than the novelty of an LLM. Achieving sustained success with agentic AI depends far less on specific chat features of generative AI or the latest models and far more on the quality, accessibility, and performance of the underlying data foundation.
Early agentic solutions have forced enterprises into difficult compromises, leaving data practitioners dissatisfied with data quality, trust, and governance. To move past these limitations and achieve production-scale AI, Futurum recommends enterprise decision-makers adopt the following pragmatic steps:
Move beyond simple prompt engineering. Evaluate agentic pruning and semantic summarization capabilities that allow you to reason directly at the data layer. This approach systematically reduces the LLM search space and dramatically improves accuracy for Natural-Language-to- SQL tasks.
Review the integration tax created by standalone vector databases and specialized document stores. Collapsing these disparate systems into a converged data engine reduces the security surface area and ensures that agents operate on a single version of the truth with strict ACID guarantees and availability SLAs.
Utilize native vector capabilities to run high-performance AI searches directly on open formats such as Apache Iceberg. This ensures the organization avoids data lock-in while maintaining the flexibility to run workloads on the cloud of their choice.
Use tangible metrics to move the conversation from qualitative vibes to hard, quantitative data. Track task completion times and operational cost reductions to correlate AI investments directly with financial impact.
Executing these strategic steps is incredibly difficult if your underlying infrastructure forces you to bolt AI onto fragmented, legacy systems. To realize the true value of agentic AI at scale, organizations require a unified platform that eliminates architectural compromises.
This is where Oracle AI Database 26ai can change the calculus. Compared to other options in the market, Oracle is unique in that it provides architectural equivalency regardless of where the database is deployed. It is effectively commoditizing the foundation model layer while fiercely owning the high-value data, context, memory, and orchestration layers. By investing in Oracle’s converged data engine, companies can immediately accelerate the pragmatic steps outlined above – consolidating isolated stacks, securing data natively, and managing agentic memory at the source. For the enterprise, the path to production-scale AI is no longer about chasing the latest LLM. It is now about activating the intelligence already present in Oracle’s no-compromise AI for data foundation.
Data Intelligence, Analytics, & Infrastructure Practice Area, Led by Brad Shimmin
Futurum Research
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