Agentic AI breaks down when fed fragmented data from lakes or APIs. Entity-centric data products overcome this issue to become an agentic AI best practice.
AI adoption is accelerating across industries. According to the McKinsey & Company 2025 State of AI survey, 88% of enterprises now use AI in at least one business function, yet only about one third have successfully scaled beyond early pilots. At the same time, an EY global survey found that nearly half of enterprises say they are already adopting or fully deploying agentic AI, and many expect more than half of their AI workflows to become autonomous within two years.
Yet many of these initiatives stall once they reach production. The reason is rarely the model or AI platform. Most organizations encounter a deeper issue: their existing data architecture was built for analytics or application integration, not for the autonomous, real-time decisioning required by agentic AI. This gap highlights one of the most important emerging agentic AI best practices: establishing the right data foundation.
Data lakes and APIs are useful, but they cannot deliver the unified, contextual, and up-to-date data for AI agents to reason and act reliably. As organizations move from experimentation toward production, entity-centric data architecture becomes essential.
Most AI pilots stall at production because traditional data architectures were built for analytics and application integration, not autonomous, operational decisioning.
Entity-centric data architecture is emerging as one of the most important agentic AI best practices, enabling agents to reason over unified, contextual business data.
Data lakes fall short due to stale information, fragmented entity views, and excessive noise that slows agent responses and increases token cost.
APIs alone are insufficient because teams can never build enough endpoints to answer every possible question, and the continual demand for new APIs quickly becomes unmanageable.
Entity-centric data products unify all relevant data for each business entity and stay synchronized with systems of record, providing reliable operational context.
This architecture enables data agents to extract Minimum Viable Data and assemble agentic data payloads that are minimal, fresh, governed, and semantically rich.
Entity-centric data architecture also improves agentic AI data protection, supporting consistent masking, minimization, and entity-level access control.
For years, enterprises have relied on two main approaches for managing and exposing data: storing everything in data lakes, and exposing application-specific data through APIs. These methods work well for analytics, reporting, and point-to-point integration.
Agentic AI, however, introduces different requirements.
AI agents need:
A unified and trustworthy view of the business entity involved in the workflow
Only the minimum necessary data, to reduce cost, latency, and risk
Fresh operational context that reflects the current state of the business
Governance and lineage that remain intact when data reaches the agent workflow
Semantic clarity, including relationships and hierarchies
Traditional data architectures struggle to meet these needs. They were not designed to support autonomous reasoning or continuous interaction with real-time business conditions.
Entity-centric data architecture organizes information around business entities, not applications or schemas. An entity is something the business reasons about and acts on, such as a customer, device, account, invoice, order, or claim.
At the heart of this architecture is a single guiding question: What is the complete, governed, and real-time view of this entity that allows an AI agent to accurately answer any question about it, instantly?
Entity-centric data architecture is implemented through entity-centric data products, which:
Aggregate data from all relevant systems of record by business entities
Maintain synchronization with those systems
Encode semantics, hierarchies, and governance rules
Present a consistent, operational representation of the entity
Provide the foundation for extracting Minimum Viable Data for each agentic workflow
This creates a living representation of the business that supports accurate, timely agentic reasoning.
Data lakes play an important role in analytics, but they are not suited to real-time agentic AI workloads. Several limitations stand out:
Data freshness
Data lakes are usually fed by batch ingestion. AI agents working from stale data are likely to make inaccurate decisions.
Fragmented entity views
Information relevant to one entity (e.g., specific customer) is spread across many ingestion zones and tables. Reconstructing the entity requires significant and costly compute in the data lake, and it is not something that AI agents can reliably perform.
Excessive noise
Data lakes often contain far more data than an agent needs. This makes prompts larger, increases cost, slows responses, and reduces accuracy.
Limited semantics
Data lakes store raw fields, not the meaning, relationships, or business rules that agentic AI depends on.
Data exposure risk
Providing AI agents broad access to the data lake exposes far more information than necessary. Because lakes contain all raw and historical data, allowing an agent to query them directly creates significant privacy and security risk. These characteristics make lakes valuable for analytics, but misaligned with the operational needs of autonomous agents.
APIs are essential for interacting with systems, but they are not the right data foundation for agentic AI.
Key limitations include:
Application-centric design
APIs reflect how a system stores data, not how a business reasons about an entity. A card servicing API may expose statements, transactions, and payments separately, leaving the agent to infer how they relate.
Latency across systems
A single workflow may require calling many APIs across CRM, core banking, payments processing, rewards, and fraud systems. This compounds latency and conflicts with conversational responsiveness.
Overexposure or underexposure of data
APIs may reveal too much or too little information. Both patterns reduce accuracy and raise compliance concerns.
APIs cannot anticipate every question an AI agent will ask
Agentic AI introduces open-ended, conversational requests. When an agent needs data that an API does not expose, a new endpoint must be created. This leads to constant API expansion and an unsustainable burden on backend teams.
No embedded reasoning support
APIs deliver fields, not meaning. They do not encode relationships, hierarchies, or business rules. The reasoning burden shifts to the LLM or orchestration layer, which introduces inconsistency.
APIs remain critical plumbing, but they cannot serve as the primary data layer for agentic AI. Open-ended reasoning requires flexible, unified, real-time entity views that are not bound to static API endpoints.
Among emerging agentic AI best practices, entity-centric data products stand out because they directly address the architectural root cause of many production failures.
Entity-centric data products provide the unified, operational views that data lakes and APIs cannot offer. A data product:
Consolidates all relevant data for a business entity
Stays synchronized with source systems
Encodes semantic metadata and business rules
Represents the current state of the entity
Ensures that the data is tightly governed and isolated
Supports the extraction of the Minimum Viable Data needed for a task
This structure enables data agents to assemble precise, contextual datasets without requiring the AI agent to reconstruct meaning or stitch together system fields.
Agentic AI does not only answer questions. It understands situations, identifies actions, and follows through. AI agents invoke data agents whenever they need to reason over context derived from enterprise data. The data agent handles entity identification, data product selection, SQL generation for structured queries, and RAG for unstructured content.
With an entity-centric data product, the data agent can retrieve precise, real-time information that gives the AI agent the context it needs to reason effectively. This includes insights such as:
Once the AI agent decides on a course of action that affects systems of record, it invokes the data agent, which uses the relevant data products to execute the update.
Entity-centric data products enable this closed-loop cycle by providing unified structure, rich semantics, built-in governance, and real-time sync, so AI agents can operate with accuracy, consistency, and trust.
Credit card servicing provides a clear illustration of why an entity-centric data architecture is imperative for agentic AI. In banking environments, the data needed to support a credit card inquiry is typically scattered across multiple systems:
Answering even a simple question like “Why was this charge declined?” or “How much available credit does this customer have right now?” requires stitching together information from all these systems. This is slow, error-prone, and incompatible with transactional agentic AI.
A credit cardholder data product instance unifies all relevant information for a specific cardholder, including:
Customer identity and verification status
Current card accounts, limits, and available credit
Recent transactions grouped by merchant category
Rewards balances, accrual status, and expiring points
Fraud indicators and recent alerts
Payment history and upcoming due amounts
A data agent can then extract precisely the data required for the agentic workflow (the “Minimal Viable Data”) and deliver it as an agentic data payload. The AI agent receives a coherent, accurate, and contextual response instead of a fragmented multi-system patchwork.
Entity-centric data products also improve agentic AI data protection. Because each data product defines the attributes, semantics, governance, and relationships for a business entity, the enterprise can:
Compared with exposing entire tables or raw API responses, this significantly reduces risk while maintaining the accuracy and context needed for autonomous agentic workflows.
Data lakes and APIs will continue to play important roles in analytics and integration, but they are not enough to support agentic AI as the primary data foundation.
To deliver reliable and scalable agentic AI workflows, enterprises need a data architecture that mirrors how the business actually operates and that can deliver the right data for agentic AI: unified, minimal, contextual, governed, and fresh. Among emerging agentic AI best practices, adopting an entity-centric data architecture, implemented through live data products and operationalized by data agents, is one of the most important.
In the next post in this series, we will explore data agents in depth and examine how they plan and execute the data access and action steps that connect AI agents with enterprise systems.
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