Agentic AI is emerging as the next phase of enterprise AI adoption. The shift is subtle but important. Instead of systems that respond to prompts, enterprises are beginning to explore systems that can plan, reason, and take action across business processes. The potential is clear: automating multi-step workflows, accelerating decision-making, and extending agents deeper into operational execution to improve efficiencies.
But while the ambition is evident, the path to production is not. Despite widespread investment, most enterprise deployments remain in pilot phases or narrowly scoped use cases. The limiting factor isn’t model capability, but the infrastructure required to support these systems in real-world environments.
Agentic systems depend on continuous, governed access to enterprise data and must operate within the same parameters as mission-critical IT for security, governance, performance, and reliability. As these systems scale, architectural cracks begin to surface. Data moves between systems, governance fragments, and security controls become inconsistent. What works in a controlled environment becomes difficult to manage in production at scale.
This is driving a broader architectural rethink. Rather than treating AI as a layer applied on top of existing systems, enterprises are starting to evaluate where control should reside — where data access is enforced, where governance is applied, and where execution takes place. As agentic workloads scale, the data layer is emerging as a logical control point.
This paper examines that shift, the architectural options available to enterprise IT, and the implications for building and operating agentic systems at scale.
The adoption data tells a clear story about where enterprise agentic AI stands today. According to Deloitte’s 2026 State of AI in the Enterprise report, nearly three-quarters of companies expect to deploy agentic AI within two years, yet only 23% use it even moderately today — and only 21% have a mature governance model in place.
That gap is structural, rooted in infrastructure challenges that most current agentic AI frameworks were not built to solve. Moor Insights & Strategy (MI&S) identifies three primary barriers that define the production threshold for most organizations:
The implication is straightforward: Solving for agentic AI in the enterprise requires rethinking where control is established — not at the application layer, where governance is difficult to enforce consistently, but at the data layer, where access, security, and execution can be anchored in infrastructure that enterprises already trust.
The production gap is not for lack of trying. As enterprises have experimented with agentic AI, several distinct approaches have emerged. Each has genuine strengths, along with inherent structural limitations that become visible as deployments scale.
The most common starting point for technically sophisticated organizations is assembly: combining open-source orchestration frameworks with commercial LLM APIs, purpose-built vector databases, custom RAG pipelines, and whatever data connectors the team can integrate. The appeal is real, because it enables maximum control, model flexibility, and the ability to optimize each component independently.
In practice, this introduces structural challenges. Data moves between systems — from operational databases to vector stores, and from vector stores to context layers. This leads to synchronization lag and duplication risk. Further, policies governing data access don’t extend across the stack.
For startups and technology-native enterprises with strong AI engineering teams, this approach can work. For the broader enterprise market, it becomes difficult to sustain.
Cloud providers offer managed orchestration platforms that abstract much of the integration burden and accelerate deployment. These platforms work well when data is already in the cloud, ready for AI use, and governed within the provider’s security model.
However, enterprises with on-premises data estates, hybrid environments, or strict data residency requirements find that this approach creates significant friction. This is especially true in regulated industries, where data movement is often the hardest part of the problem to solve.
There are also longer-term considerations around vendor lock-in. When orchestration, data, models, and infrastructure converge under one vendor, switching becomes painful, and costs become real.
A third approach has emerged around purpose-built AI infrastructure — vector databases, embedding services, and specialized agent memory systems layered between operational data and agent frameworks.
But purpose-built rarely means purpose-governed. A vector database optimized for fast similarity search was not designed with enterprise access controls, transaction guarantees, or compliance audit trails as primary design objectives. What can accelerate early deployments can also become too complex in the real world.
All of these approaches are moving the industry forward, but they share a common limitation. They treat the data layer as something to integrate with, rather than the foundation to build upon.
That distinction is at the core of Oracle’s approach.
Oracle’s approach to the enterprise agentic challenge reflects a clear and differentiated architectural philosophy. While most enterprise agentic AI frameworks are built as orchestration layers that call external databases and services, Oracle has inverted the model by architecting AI capabilities into the database and letting agents execute close to the data they depend on. This can be viewed as a control plane for agentic AI — or an AI operating system that manages critical components right next to the data.
The logic builds on Oracle’s existing strengths. Its converged database supports relational, vector, graph, JSON, spatial, and columnar data in a single unified engine. It also includes the security, consistency, transaction guarantees, and operational reliability required for mission-critical enterprise workloads. The Oracle AI Database 26ai release extends this foundation by enabling agent memory, as well as agent creation and execution, within the same system.
For regulated industries —or any organization that values data privacy — this matters. Architectures that require data to leave well-governed environments introduce compliance risk that may be unacceptable. Oracle’s model keeps data where it belongs and brings AI to it.
From an MI&S perspective, this represents a meaningful differentiation. While Oracle’s model doesn’t totally eliminate complexity, it greatly simplifies AI implementation by locating the architecture in a layer that enterprises already know how to manage. Prioritizing governance and consistency over architectural flexibility, which can become unwieldy, aligns well with the needs of many larger enterprise IT organizations.
What follows is a closer look at five capabilities Oracle has introduced to support its agentic AI approach. Taken together, they reinforce a clear point of view: AI should be built where the data already lives, not stitched together across fragmented systems.
Oracle AI Database Private Agent Factory is a no-code platform that enables business analysts and domain experts to build, test, deploy, and manage data-centric AI agents and multi-agent workflows. It runs as a container that’s deployable on-premises, in a private cloud, or in any public cloud within the customer’s own tenancy. This keeps enterprise data within the customer’s security perimeter throughout the agent lifecycle.
Three pre-built agents are available out of the box: a Database Knowledge Agent for RAG-based information retrieval, a Structured Data Analysis Agent for schema-aware semantic querying, and a Deep Research Agent for multi-step complex analysis. These cover the most common enterprise agentic use cases and enable rapid deployment without custom development. For organizations that need to go further, the Agent Builder enables code-free custom agent design, combining LLM components, data connectors, APIs, and specialized sub-agents into hierarchical, multi-agent systems.
Private Agent Factory’s most consequential design decision is not its no-code interface, but its deployment model. Running as a container in the customer’s own environment eliminates the architectural pattern that most enterprise security teams find hardest to accept: enterprise data leaving the governed perimeter to reach external AI services.
Equally important is the platform’s commitment to determinism. Agents deployed in production maintain validated, predictable behavior with controls designed to reduce drift from model updates.
Current enterprise agent development approaches require significant developer effort, including assembling LLM APIs, vector stores, orchestration frameworks, embedding models, and custom tool integrations into a pipeline. As noted earlier, the result is architecturally fragile — difficult to validate, hard to govern, and prone to behavioral drift in production.
From an MI&S standpoint, Private Agent Factory is well aligned with the needs of this segment of the market and is well suited for building agents that interact with a wide range of enterprise data.
Oracle’s Autonomous AI Vector Database is aimed squarely at developers and data scientists building semantic search, RAG, and agentic applications. On the surface, it checks the expected boxes: support for Python, REST, and SQL, a clean development experience, and a straightforward path from early experimentation into production. As requirements scale, it expands into the full Autonomous AI Database with a single click, without requiring data migration or re-architecture.
And this last point is quite compelling, and a significant distinction. Standalone vector databases work well in early stages. They’re easy to deploy and test, and they help teams move quickly. However, they also introduce a second system into the architecture, which is where things often start to break down. Making this setup work requires synchronization pipelines to keep vector data aligned with source systems, separate security and governance models, and increasingly complex cross-database queries. While manageable in pilots, this model becomes more difficult to maintain in production.
Oracle’s model avoids that divergence from the start. Vector data and business data operate within the same governed system, simplifying retrieval, maintaining consistent security policies, and ensuring that auditability isn’t an afterthought.
Agents without memory aren’t agents — they’re tools. Every interaction starts from scratch, with no persistent context or accumulated learning. While stateless agents can answer questions, they can’t manage complex, multi-step workflows that unfold over time.
Oracle Unified Agent Memory Core provides a persistent, governed memory layer for enterprise AI agents, built natively on Oracle AI Database. It organizes memory across three functional layers that map to how agents use context:
Current enterprise agent memory solutions are fragmented — separate vector stores, graph databases, and document stores, each with its own security model and consistency guarantees. None of these are auditable as a unified system.
In what may seem like a recurring theme, Oracle’s approach co-locates agent memory with enterprise data in the same converged database, enabling low-latency reasoning across vector, JSON, graph, relational, text, spatial, and columnar data. Access controls, transaction guarantees, and retention policies that govern business data also govern agent state. This eliminates the need for external AI caches and multiple trips to isolated data sources, accelerating agents’ ability to execute their intended functions.
MI&S sees the Unified Agent Memory Core architecture as addressing one of those critical yet often overlooked infrastructure topics. Organizations that get it wrong could spend years retrofitting agent memory onto stacks that were not designed for it.
AI agents introduce a security problem that application-layer access controls were never designed to solve. Agents can autonomously generate arbitrary SQL queries, potentially accessing any data that the underlying service account can reach. These “digital workers” can be manipulated through prompt injection to expose sensitive records. They can also bypass application-layer security checks that were designed to constrain human interaction patterns.
Consider how most enterprise agent deployments are configured. Agents connect to databases through highly privileged service accounts — broad credentials granted to the infrastructure, with user-level access enforcement left to the application layer. This model was already imperfect for human-facing applications. At agent scale, where hundreds or thousands of autonomous sessions may be running simultaneously, it breaks down entirely. An organization simply can’t trust every application in the chain to enforce the right policy, every time, for every agent interaction. The math doesn’t work, and the risk quickly moves from theoretical to real.
Oracle Deep Data Security addresses this by making the database the control plane. Row-, column-, and even cell-level access rules are defined in SQL and enforced at runtime. An agent can generate any query, but the database enforces what that user is allowed to see.
The real-world impact is significant. Prompt injection attacks fail at the database, not the application. RAG workflows over vector embeddings are subject to the same access governance as direct SQL queries. Audit trails are generated automatically, and access rules can be updated centrally without touching application code.
For CxOs and IT leaders, this level of access control should be a baseline requirement.
A significant portion of enterprise data doesn’t live in operational databases. In many cases, some of the most valuable enterprise data lives in data lakes. Apache Iceberg has emerged as a dominant open table format in this space, adopted broadly across enterprise data platforms.
The challenge is that most AI vector search today operates on data extracted from those lakes and loaded into a separate vector store. This creates synchronization lag, pipeline maintenance overhead, and a governance divide between lake data and database data.
Oracle Vectors on Ice closes that gap. Oracle AI Database generates vector embeddings from Iceberg-resident data in place, runs high-performance similarity search directly against Iceberg tables without data movement, and creates HNSW (hierarchical navigable small world) vector indexes that are automatically updated as the underlying data changes.
The result is straightforward: Data lake data becomes a first-class participant in agentic AI workflows without a separate extraction pipeline. Combined with Oracle AI Vector Search, an agent can reason across structured database content and data lake content through a single query, under consistent security policies, without the developer managing the boundary between the two systems.
Vectors on Ice is easy to underestimate on a feature list. However, it removes a barrier that is rarely explicitly named: the gap between where enterprise data lives and where AI can actually search it. For organizations with mature data lake investments, that gap — which has been a real constraint — is now closed.
For most IT leaders, the first challenge in adopting agentic AI is not technology selection. It is an architectural governance problem — one that must be addressed before the technology selection can be made responsibly. The five Oracle capabilities examined above point to a set of considerations that every CIO, CDO, or CAIO should be working through right now, regardless of which platform they ultimately choose.
Don’t begin by selecting an LLM or orchestration framework. Start with where enterprise data actually lives and what it takes to give agents reliable, governed access to it. That answer constrains everything else.
Most enterprise data estates are deeply heterogeneous, spanning transactional databases, document databases, warehouses, lakes, object stores, vector stores, streaming platforms, and decades of business logic baked into ERP and CRM systems. Agents typically access only a subset of this data, which can lead to limited or unreliable outputs. Evaluate platforms based on how they handle the data architecture you actually have — not the cleanroom architecture you don’t.
Every enterprise has a governance profile — non-negotiable constraints on data residency, access controls, compliance requirements, and security posture. In many cases, these constraints are set by regulators, not IT leadership.
Many of today’s most developer-friendly, fastest-to-deploy agentic AI platforms require data to leave the governed perimeter or rely on third-party AI APIs for core functions. For organizations where data privacy is critical, these platforms are non-starters.
Don’t assume governance constraints will relax over time. For most regulated industries, they are becoming more restrictive, not less.
Production agentic AI systems will fail. It’s not a question of if, but when. There are three failure modes that CxOs and IT leaders should specifically evaluate:
Governance-by-design architectures that enforce access controls at the data layer, ensure deterministic workflows, and automatically generate audit trails are better positioned to contain, detect, and recover from all three of these failures.
The agentic AI evaluation process in most organizations is still heavily influenced by demo quality and time to first working prototype. While these are legitimate signals, they are poor proxies for what matters in production: total cost of ownership across the full agent lifecycle.
The most commonly underestimated lifecycle costs include:
These costs do not appear in a proof-of-concept evaluation. They appear 18 months after deployment, when the organization is trying to scale what worked in a pilot and discovering that the architecture was not designed for that scale.
A final consideration is the distinction between infrastructure you own and infrastructure you trust. Cloud-managed services, third-party AI APIs, and vendor-hosted orchestration platforms all involve some degree of trust delegation. In these cases, some combination of your data, your business logic, or your agent workflows run on infrastructure that you don’t control.
The question is whether enterprise vendor risk management frameworks have been updated to account for the specific risks of agentic AI, including agents that can access sensitive data on behalf of users, generate queries that expose personally identifiable information (PII), or execute actions with real business consequences.
For many organizations, the right answer is a tiered approach. Cloud-managed infrastructure is used for lower-sensitivity workloads where trust delegation is acceptable, while on-premises or a private cloud deployment is used for workloads that handle sensitive data or operate in regulated environments. The key is to make these decisions deliberately, based on a clear understanding of the trade-offs involved in trust delegation — not by default because a particular platform was easy to start with.
The infrastructure decisions enterprises make in 2026 will shape their AI capabilities for the next five to seven years. This isn’t hyperbole — it’s a pattern seen in every prior infrastructure cycle, from cloud migration to data warehouse consolidation to hybrid networking. Organizations that moved early and deliberately built structural advantages that compounded over time. Those that optimized for speed and deferred the hard architectural decisions spent years trying to retrofit governance onto systems that weren’t built to support it.
Agentic AI is following a similar trajectory. The enterprise market is currently in a phase where pilot velocity is rewarded and architectural rigor is optional. That phase doesn’t last. The moment these systems move into production at scale, the shortcuts taken during pilots become visible — and expensive.
In the agentic AI world, Oracle AI Database functions as the control point for what comes after the pilot. Its in-database execution architecture keeps agents close to the data, eliminating the movement, latency, and exposure that come with external orchestration frameworks. It does this while delivering the governance, security, and scale that production enterprise environments demand.
For enterprises where data gravity, compliance, and scale aren’t negotiable, that’s not a minor distinction; it’s the deciding factor. Given this, Oracle deserves serious evaluation.
More broadly, MI&S recommends that enterprise AI and IT leaders take four concrete steps:
Put simply, build agentic AI where the data gravity is — where the data resides. Otherwise, trying to maintain consistency between your AI dataset and business data estate will add enormous cost and complexity.
The agents are coming. The question is whether your foundation will hold.
For more information on Oracle AI Database agentic AI capabilities, visit Oracle.
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