Modernizing Data Architecture for Agentic AI Systems

As businesses increasingly recognize the transformative potential of artificial intelligence, many large enterprises find themselves stuck in the proof-of-concept phase. A recent McKinsey report on the State of AI for 2025 indicates widespread experimentation, yet only a select group of “high performers” are realizing substantial business value. While 23% of organizations are reportedly scaling agentic AI systems, their widespread adoption is still limited.

Boston Consulting Group points out that approximately 70% of deployment hurdles stem from people and process issues, rather than model performance. However, inadequate data infrastructure remains a significant impediment, causing project delays and hindering the full potential of AI initiatives. Addressing these foundational data challenges is crucial for unlocking advanced AI capabilities.

The Database Bottleneck: Impediments to AI Adoption

Many engineering teams continue to rely on database architectures primarily designed for transactional applications. These systems are ill-suited for modern AI requirements, which often involve a complex mix of structured and unstructured data, alongside live event streams. This legacy architecture typically exhibits three key characteristics that impede AI adoption: rigid schemas and silos, outdated logic, and the treatment of AI as a secondary, “bolt-on” component.

Rigid Schemas and Data Silos

Current enterprise resource planning (ERP) and customer relationship management (CRM) systems are built with inflexible schemas, operating in isolated environments. Data warehouses, search indexes, and vector stores often reside in disparate locations with distinct contractual agreements. Their varying data models and application programming interfaces (APIs) make it challenging to query across systems without extensive translation or synchronization efforts.

These older systems were not designed to comprehend semantics or contextual relevance. Consequently, the AI layer is forced to stitch together these disparate pieces of information before it can even begin to derive meaning from the data. This fragmented approach adds significant complexity and overhead to AI development.

Outdated Logic and Data Drift

A second critical issue arises when systems are updated infrequently, perhaps only once nightly. This creates significant time gaps between data changes and system updates. Agentic AI systems might be reasoning with stale information, while the underlying source data has already evolved dramatically. This “index drift” has long-term consequences, as vector stores and search indexes lose synchronization with the operational reality.

Security policies also suffer from this divergence. Permissions or access controls updated in source systems may not immediately propagate to the AI’s cached or copied data. Operating with inconsistent or outdated context severely compromises accuracy, trust, and compliance, potentially leading to significant operational risks.

AI as a Bolt-On Component

Thirdly, since most existing systems are not inherently AI-native, AI capabilities are often integrated as separate, “sidecar” components. This typically results in separate security, lineage, and observability frameworks for AI functionalities. Such an approach complicates auditing and compliance, treating AI as a distinct feature set rather than an integral part of a unified access, usage, and behavior trail.

This fragmented integration creates operational blind spots, where incidents or data leaks can go undetected. When the AI sidecar operates outside the organization’s core governance framework, security teams lack visibility into potential policy violations. Research from the RAND organization confirms these experiences, highlighting how companies frequently underestimate the crucial requirements for data quality, lineage, access control, and deployment scaffolding necessary for reliable AI.

Preparing the Data Layer for AI Agents

Traditional database stacks generally assume clear distinctions between transactional processing (OLTP), analytical processing (OLAP), and search functionalities. However, agentic AI use cases blur these boundaries considerably. Agents demand durable read-write interactions and real-time triggers, necessitating low-latency joins across text, vectors, and graph relationships, all while maintaining consistent security. Legacy data patterns, which involve shipping data to separate indexes and stores, introduce latency, duplication, and significant risk.

The prevailing trend is toward converging semantic retrieval and policy closer to operational data. This is evident in cloud platforms integrating vector and hybrid search capabilities directly into operational stores. Examples include MongoDB’s Atlas Vector Search, Databricks’ Mosaic AI Vector Search, and OpenSearch’s Neural/Vector Search. Similarly, Postgres communities are expanding with pgvector. While these “bolt-on” approaches offer some benefits, they often introduce their own set of challenges. This has spurred a new generation of AI-native databases designed to address these fundamental gaps.

Migrating an entire database infrastructure is a complex, risky, and expensive undertaking. For large organizations deeply invested in their current database stacks, a complete shift might not be immediately feasible. However, for greenfield, AI-native projects, intentional database selection is paramount. It is crucial to choose a database model that inherently supports the specific needs of agentic systems.

Agentic systems are designed to plan, utilize tools, write back state, and coordinate across various services. These advanced functionalities require particular data conditions:

• Long-lived memory with persistence and retrieval: Beyond simple chat windows, agents need to store and recall information over extended periods.
• Durable transactions: This ensures that updates issued by agents are trustworthy and consistent.
• Event-driven reactivity: Support for subscriptions and streams is vital to keep user interfaces and other agents continuously synchronized.
• Strong governance: This includes robust low-level security, data lineage tracking, auditing capabilities, and stringent personally identifiable information (PII) protection.

Major AI frameworks increasingly emphasize these critical data requirements. LangGraph and AutoGen, for instance, are incorporating persistent, database-backed memory. Nvidia and Microsoft’s reference architectures prioritize data connectors, observability, and security within their agent factory designs. Even traditional database giants like Oracle are integrating agent tooling directly into their latest database releases. This underscores that the core challenges for AI are not merely model-centric, but fundamentally concern state, memory, and policy management.

Rebuilding the Data Layer for AI: Best Practices

To effectively rebuild the data layer for AI, several best practices should be adopted:

• Build for Adaptability: Prioritize first-class support for mixed data types, including relational, document, graph, time series, and vector data. Flexible schemas are essential, allowing AI to reason over entities, relationships, and semantics without encountering rigid extract, transform, load (ETL) processes.
• Commit to Openness: Embrace standard interfaces, open formats, and active participation in open-source communities. This approach enables teams to combine the best available embedding models, re-ranking tools, and governance frameworks, while simultaneously mitigating vendor lock-in risks.
• Embrace Composability: Integrate real-time subscriptions and data streams, position functions close to the data, and establish unified security. This ensures that retrieval, reasoning, and actions operate against a single, trustworthy source of truth.

When considering which database model to adopt for specific use cases, organizations typically work with a diverse set of (often open-source) databases, each optimized for different workloads. For example, MySQL and PostgreSQL excel with transactional data, while MongoDB and Couchbase offer flexible document storage for dynamic application data. This “polyglot persistence” approach means teams select the most suitable tool for each scenario.

The question then becomes how to integrate AI into existing stacks, what alternatives are available, and whether a complete database refactor is necessary to bring AI agents into a company.

Unified Operational Stores with Vector and Hybrid Search

Platforms like MongoDB Atlas, Databricks Mosaic AI, and OpenSearch are bringing approximate-nearest-neighbor and hybrid retrieval capabilities directly alongside data. This strategy significantly reduces synchronization drift, ensuring that AI systems operate with the most current information. Postgres extends its capabilities with pgvector, allowing teams to standardize on SQL for vector operations. Similarly, Oracle Database 23ai and 26ai are integrating native vector search and agent builders into their core relational database management systems (RDBMS), signaling a clear shift toward AI-aware data layers.

This approach is particularly well-suited for simpler AI projects that rely predominantly on a single data tool, such as MongoDB, OpenSearch, or Postgres. However, the reality for many enterprise AI systems and agents is that they frequently depend on multiple data sources and tools. Searching and retrieving data across such a heterogeneous environment can be challenging. The ability to natively store and search across a mix of data models in a single location can significantly enhance an organization’s capacity to leverage its data for building sophisticated AI systems.

Purpose-Built Vector Databases

Specialized vector databases such as Pinecone, Weaviate, and Milvus are designed to deliver high scalability and low latency for vector operations. Many enterprises pair these with their operational databases when high-performance, advanced vector features are a critical requirement for large-scale embedding and vector search workloads. While highly effective for specific tasks, this approach necessitates managing and operating an additional, separate database system, adding to infrastructure complexity.

Multi-Model Databases

Multi-model databases offer a convergent solution to these challenges. SurrealDB, an open-source example, unifies relational, document, graph, and vector data, providing ACID transactions, row-level permissions, and live queries for real-time subscriptions. For AI workloads, it supports integrated vector and hybrid search within the same engine that enforces company governance policies. Its event-driven features, such as LIVE SELECT and change feeds, keep agents and user interfaces synchronized without requiring external brokers.

For many development teams, this approach significantly reduces the number of moving parts between the system of record, the semantic index, and the event stream. This simplification streamlines data management and enhances the overall efficiency of AI deployments.

Principles for AI-Ready Architecture

Integrating AI into traditional environments often proves challenging. Engineering teams frequently contend with multiple copies of data, leading to data drift and inconsistent access control. The continuous cycle of embedding refreshes and index rebuilds can cause latency spikes and degrade data quality. Furthermore, separate policy engines result in audit gaps across chat, retrieval, and actions taken by AI systems.

An AI-ready data layer, however, can store entities, relationships, and embeddings together, allowing queries with a single policy model. Real-time subscriptions can push changes directly into agent memory, eliminating the need for cumbersome nightly backfills. By enforcing row-level security and lineage at the data source, every retrieval becomes compliant by default, significantly improving trust and reliability.

This is not merely theoretical; public case studies demonstrate tangible benefits when organizations streamline their data sprawl. For instance, LiveSponsors rebuilt a loyalty engine, reducing query times from 20 seconds to 7 milliseconds while unifying relational and document data. Aspire Comps scaled to 700,000 users in just eight hours after consolidating backend components. These examples highlight substantial gains in both data consolidation and AI readiness.

AI initiatives frequently falter not because models are insufficient, but because the underlying data architecture lags behind ambitious goals. The most efficient route from pilot project to profitable deployment involves modernizing the database layer. This ensures that retrieval, reasoning, and action occur against a single, governed, real-time source of truth.

Key considerations for creating the optimal conditions for AI agents include:

• Design for Retrieval and Relationships: Treat graph, vector, and keyword search as equally important components. Knowledge graphs, when combined with retrieval-augmented generation (RAG), are becoming standard for delivering explainable and lineage-aware answers.
• Co-locate State, Policy, and Compute: Position embeddings close to the system of record and embed policy (such as role-based access control and row-level security) directly into the database. This minimizes data hops and improves efficiency.
• Make Memory Durable: Agents require persistent memory and the ability to resume workflows. This is supported by modern frameworks like LangGraph and AutoGen, as well as enterprise “AI factory” designs from leading technology providers.
• Prefer Open Building Blocks: Utilizing open-source options, such as pgvector, Weaviate, Milvus, and OpenSearch, mitigates vendor lock-in risks and accelerates learning curves. These tools are particularly effective when paired with an open operational database.

In addition to these design principles, three practical steps are crucial for preparing the environment:

1. Start with Bottlenecks: Identify and eliminate any unnecessary data hops between your application, vectors, and policy enforcement points.
2. Adopt a Unified, AI-Aware Data Layer: Whether by evolving existing platforms or adopting a new unified engine, consolidate data silos and co-locate semantics, relationships, and state.
3. Measure Business Impact: Quantify improvements in milliseconds and dollars. Look for reductions in latency, increased accuracy, and gains in productivity. Real-world examples demonstrate that sub-10-millisecond retrieval and significant stack simplification lead to faster feature development and substantial cost savings. Case studies from SurrealDB customers, including LiveSponsors, Aspire, and a collaboration involving Verizon, Samsung, and Tencent, showcase both the technical and organizational benefits derived from a simplified data layer.

Databases have always been fundamental to traditional software applications. With the emergence of agentic AI, their role must evolve. Databases are poised to become the “agentic memory” at the core of reliable agentic systems. The critical question is not whether to rethink data strategies for agents, but rather how swiftly organizations can equip agents with the necessary memory to accelerate decision-making and drive innovation.