Minimum Viable Data: The key to accurate, efficient, and protected agentic AI

Enterprises often assume that giving AI agents more data leads to better answers.  In reality, the opposite is true.

When AI agents receive large, unfiltered datasets, accuracy suffers, cost increases, and the risk of exposing sensitive information grows. This is especially problematic for agentic AI data protection, where reducing unnecessary data exposure is essential. 

To solve this, organizations are adopting Minimum Viable Data (MVD) as a foundational discipline to data for AI agents. MVD ensures that AI agents receive only the essential, contextual information needed to perform a task, enriched with the metadata required to understand it. The result is faster, more accurate, more cost-efficient, and more compliant AI. 

What is Minimum Viable Data? 

Minimum Viable Data is the smallest, most relevant dataset an AI agent needs to complete an action or answer a question.  

It’s not about giving the agent “less data.”  

It’s about giving the agent the right data, with the right semantic context, at the right moment.

MVD combines three principles: 

1. Task relevance: only information directly required for the agentic AI workflow

2. Context richness: delivered through metadata and semantic structure

3. Data minimization: a core part of agentic AI data protection 

This makes MVD both a performance optimization and a governance safeguard. 

Why AI agents fail without MVD 

When enterprises do not apply MVD, four predictable problems emerge: 

1. Accuracy declines
   More data introduces more noise. AI agents must sift through irrelevant attributes and fields, outdated or invalid values, and contradictory information. This weakens reasoning and increases hallucination. 

2. Responses slow down
   Large payloads take longer to retrieve and process. AI agents spend more time “finding the signal” inside oversized prompts. 

3. Costs climb rapidly
   LLM pricing scales with data volume. Without MVD, token usage grows unnecessarily, especially at production scale. 

4. Data exposure increases 
   Exposing unnecessary data raises compliance and privacy risk. MVD is one of the most effective levers for agentic AI data protection because it minimizes the surface area of sensitive information visible to AI agents. 

Entity-centric data organization: How MVD is delivered 

To deliver MVD consistently, enterprises need a data foundation built around entity-centric design. This means organizing data around a company’s business entities, like customer, invoice, order, or device – rather than around applications, databases, and tables. 
Importantly, entity-centric design is domain-specific, not abstract.  


Let’s take a look at an example.

Example: A billing-support AI agent 

For example, consider a telco customer service AI chatbot agent handling billing questions.

It does not need the customer’s full CRM profile, complete payment history, or every usage record ever generated. Instead, the AI agent needs a domain-specific, entity-centric slice of data that reflects how billing actually works for an individual. This includes the billing account and its hierarchy of lines or devices, the customer’s current plans and add-ons, recent invoices with meaningful line-item breakdowns, aggregated usage for the current and previous periods, relevant payments and adjustments, and key contract attributes such as discounts or term commitments.

This curated view allows the AI agent to answer most billing questions, like “Why is my bill higher this month?” or “What happens if I switch plans?”— without being overwhelmed by unnecessary data. And as the use case expands, enterprises can iterate on this billing data product by adding new data attributes, while still exposing only the Minimum Viable Data the AI agent needs for each task.  

Why MVD must be context-rich 

Some organizations mistakenly equate “minimum data” with “bare-bones data.” 
But for agentic AI, context matters as much as content.

A minimal dataset on its own lacks the meaning an agent needs to reason effectively. When that same dataset is enriched with metadata and semantic structure, it becomes far more informative and actionable.

Semantic metadata provides: 

• Attribute meaning 

• Relationships between objects 

• Classification, labeling, and data types 

• Constraints and business rules 

This semantic data layer ensures that even a small dataset carries the rich meaning an AI agent needs to reason accurately.

MVD is minimal in volume, but maximal in understanding.

How MVD works with data agents 

MVD becomes operational through data agents, which do far more than retrieve data. Data agents run inside the enterprise’s agentic framework and use their own reasoning capabilities to plan and execute the full sequence of steps required for an AI agent to complete a task.

A data agent determines: 

• Which data slice is required to answer the question or carry out the workflow 

• Which data product or products hold the relevant entity-level information

• How to retrieve it, including generating and executing text-to-SQL when structured queries are needed 

• Whether unstructured content should be pulled in through retrieval-augmented generation (RAG) 

• How to transform and assemble the response data into a coherent, task-specific representation 

• What downstream actions to take, such as updating a system of record or triggering an operational workflow 

Data agents also apply governance rules through data masking and enforce enterprise data protections as part of their reasoning and execution steps. They ensure that AI agents work with governed, contextual, and minimal data, and that the entire end-to-end operation adheres to enterprise controls. 

Why MVD is the first step to scalable, protected agentic AI 

Minimum Viable Data is the first essential component of the trio that makes agentic AI work at enterprise scale. Together with entity-centric data products and data agents, it forms the foundation for accurate, efficient, trusted, and protected agentic AI operations.  

MVD ensures AI agents receive only the data they need; data products unify and structure that data around real business entities; and data agents operationalize both by reasoning over what data to retrieve, how to transform it, and which actions to execute.

In the next post, we’ll take a closer look at the second part of this trio: entity-centric data products. We’ll explore why they outperform traditional data architectures for agentic AI, and how they provide the real-time, unified, and governed foundation that makes MVD possible in the first place.