What are data agents? The bridge between agentic AI and enterprise data.

Data agents connect agentic AI systems to live, trusted enterprise data enabling AI agents to answer, decide, and act – rapidly, responsibly, and reliably

1. Data agents are specialized AI agents that connect agentic AI systems to enterprise operational data, retrieving and transforming information while enforcing governance. 

2. Most agentic AI projects fail in production not because of the model’s capability, but because the trusted enterprise data for AI agents is inaccessible. 

3. Minimum Viable Data (MVD) is a core principle where data agents provide only the smallest, most relevant dataset needed for each task, reducing costs and improving accuracy. 

4. Entity-centric data products organize information around business entities (customers, orders, devices) rather than applications, providing the unified foundation data agents need. 

5. Data agents implement agentic AI data protection by automatically applying access controls, data masking, audit logging, and compliance rules. 

6. Organizations can deploy pre-built data agents for common scenarios or build custom agents using no-code tools like the K2view Data Agent Builder. 

7. Data agents reduce AI costs by up to 70%, by limiting token usage through MVD principles, while companies implementing them report a 66% increase in productivity. 

8. Unlike APIs that expose static endpoints, data agents reason about business context and autonomously determine what data to retrieve from which systems through chain-of-thought prompting. 

9. Data agents support multiple use cases including conversational queries, synthetic data generation, compliance auditing, and custom workflows. 

10. Successful implementation follows agentic AI best practices including entity-centric design, automated governance, and continuous monitoring. 

Artificial intelligence is changing how businesses work. Companies everywhere are building AI agents that can reason, make decisions, and take actions on their own. These AI agents promise to transform customer service, speed up operations, and help businesses make better decisions.

But there's a problem that many companies face when they move from testing to real production. Their AI agents struggle to work with the actual data they need from enterprise systems. The agents might be smart, but they can't reach the right information at the right time.

This is where data agents come in. Data agents act as the bridge between AI agents and your company's operational data. They make sure AI agents get exactly the data for AI agents they need, when they need it, in a form they can use.

Think of it this way: An AI agent is like a skilled doctor who can diagnose problems and recommend treatments. But without access to a patient's medical records, test results, and current medications, even the best doctor can't help. Data agents are like medical assistants who gather all the right information and present it clearly, so the doctor can do their best work. 

The promise of agentic AI is powerful. Unlike simple chatbots that just answer questions, AI agents can plan multiple steps, work with different tools, and complete complex tasks without constant human help. According to a study by MIT Sloan, Microsoft Research, and GitHub, generative AI coding tools can reduce programming time by 56%, allowing developers to focus on higher-level problem-solving.

However, most agentic AI projects hit a wall when moving to production. The problem isn't usually the AI model itself. The real issue is that AI agents can't easily access the operational data they need from enterprise systems.

Consider a customer service AI agent. To help a customer with a billing question, it needs to know about the customer's account status, recent payments, active services, and billing history. This information sits in different systems: CRM platforms, billing databases, payment processors, and customer data warehouses. Traditional approaches make it hard for AI agents to quickly get a complete picture.

The 4 core challenges 

When AI agents can't get the right data for AI agents, 4 major problems emerge: 

1. Accuracy issues 

   When agents receive incomplete, outdated, or conflicting information, they make mistakes. They might tell a customer their payment was processed when it wasn't, or suggest a service upgrade that the customer already has. 

2. Slow responses 

   If an agent needs to call multiple systems and piece together information, it takes too long. With conversational AI, delays of even a few seconds feel awkward and frustrating. 

3. Rising costs 

   A Large Language Model (LLM) charges based on how much data it process. When LLM agents receive huge amounts of unnecessary information, costs multiply quickly, especially when handling thousands of requests per day. 

4. Security and compliance risks 

   Giving AI agents access to entire databases exposes sensitive information that isn't needed for the task. This creates privacy risks and makes it harder to follow regulations like CPRA, HIPAA, GDPR, and DORA European regulations which is why agentic AI data protection has become critical. 

 

Data agents are specialized AI agents that focus on one critical job: connecting other AI agents to enterprise data. While an AI agent handles reasoning and decision-making, a data agent handles data access and operational actions.

Data agents work inside your company's agentic AI framework. On one side, they interact with AI agents, understanding what data is needed for each task. On the other side, they connect to data products and enterprise systems, retrieving information and executing actions. 

How data agents are different 

Data agents are not the same as traditional data integration tools or APIs. They use AI to reason about data needs and plan how to fulfill them. A data agent can: 

• Understand a natural language request and figure out what data is needed 

• Determine which systems or data sources contain that information 

• Generate queries or API calls to retrieve the data 

• Apply AI data governance and security rules automatically 

• Transform and assemble the data into a useful format 

• Execute updates or actions on systems of record when needed 

For example, when a customer service AI agent asks, "Why is this customer's bill higher this month?", the data agent: 

1. Identifies the customer being discussed 
2. Determines it needs billing information (current and previous bills, usage data, plan changes) 
3. Retrieves this information from the relevant data products 
4. Applies any necessary data masking for privacy 
5. Formats the response so the AI agent can understand it clearly 
6. Returns only the essential information needed to answer the question 

This all happens in seconds, without requiring someone to write custom code or create new API endpoints.

A key principle that guides data agents is called Minimum Viable Data, or MVD. This means providing only the smallest, most relevant dataset needed for a specific task, which is one of the fundamental agentic AI best practices.

Many people think giving AI agents more data leads to better results. Actually, the opposite is true. Too much data creates noise, slows down processing, increases costs, and raises security risks.

MVD isn't about providing less data. It's about providing the right data for AI agents with the right context at the right time. This includes 3 elements: 

1. Task relevance: Only information directly needed for the current workflow or question 

2. Context richness: Metadata and semantic structure that help the agent understand what the data means 

3. Data minimization: Limiting exposure to reduce privacy and compliance risks, a core aspect of agentic AI data protection 

For instance, if a customer calls about upgrading their internet speed, the AI agent doesn't need the customer's entire 5-year history of support tickets. It needs to know their current plan, available upgrade options, contract terms, and billing information. That's the minimum viable data needed for this task. 

Why context matters as much as content 

Some companies make the mistake of thinking "minimum data" means "bare-bones data". But for AI agents to work well, context is just as important as content. 

A small dataset without context lacks meaning. But when that same dataset includes rich metadata, it becomes much more useful. This semantic layer provides: 

• What each data field means 

• How different pieces of information relate to each other 

• Valid values and data types 

• Business rules and constraints 

• Data quality indicators 

MVD calls for minimum size but maximum understanding. The data agent ensures that even a small response carries all the meaning an AI agent needs to reason accurately. 

For data agents to work well, they need access to data organized in a specific way. This is where the entity-centric data product comes in, another crucial element of agentic AI best practices.

Traditional data systems organize information around applications or databases. An entity-centric approach organizes data around business entities such as a customer, order, invoice, or device.

What is an entity-centric data product? 

A data product is a self-contained unit that manages all the data for a specific business entity. For example, a customer data product brings together everything about a customer: their profile information, account details, purchase history, support interactions, preferences, and more.

Each data product: 

• Pulls data from all relevant source systems 

• Keeps information synchronized in real-time 

• Includes semantic metadata and business rules 

• Enforces governance and security policies 

• Provides a unified view of that entity 

This creates a live, reliable operational view that data agents can depend on. 

Why traditional approaches fall short 

Many companies try to support AI agents using data lakes or APIs. Both have significant limitations: 

Data lakes store huge amounts of information, which is great for analytics. But for agentic AI, they fall short because: 

• Data is often stale, updated only in batches 

• Information about one entity is scattered across many tables 

• There's too much noise and irrelevant data 

• Semantic meaning and relationships are lost 

• Accessing the entire lake creates major security risks 

APIs are essential for system integration, but they can't serve as the primary data foundation for AI agents because: 

• They're designed around how systems store data, not how businesses think about entities 

• Getting a complete picture requires calling many different APIs, which is slow 

• Teams can never build enough API endpoints to answer every possible question 

• APIs expose fields without the semantic meaning agents need 

• Creating new APIs for each new agent question becomes unsustainable 

Entity-centric data products solve these problems by providing unified, real-time, semantically rich views of business entities. 

Let's walk through a real example to see how data agents operate. Imagine a telco with an AI chatbot for customer service. 

The customer service scenario 

A customer calls and says, "My bill is $50 higher than usual this month. Why is that?"

The customer service AI agent fielding this question needs to provide the right answer quickly. The step-by-step workflow might look somehting like this: 

1. Understand the request 

   The AI agent understands this is a billing question that requires comparing the current month's charges with previous months. 

2. Invoke the data agent 

   The AI agent asks the data agent for the necessary billing information for this specific customer. 

3. Decide which data is most relevant 

   The data agent applies chain-of-thought reasoning to determine what information is required, for example: 

   Current month's bill with line-item details

   Previous month's bill for comparison 

   Any recent plan changes or add-ons 

   Usage information if relevant 

   Active services and features 

4. Retrieve the data 

   The data agent accesses the customer's billing data product, which unifies information from multiple systems, such as the billing platform, usage databases, plan management, and payment systems. It then generates the appropriate queries and retrieves the data. 

5. Apply governance 

   The data agent automatically applies any necessary data masking. For instance, it might hide payment card details while keeping transaction amounts visible, ensuring proper agentic AI data protection. 

6. Assemble the response 

   The data agent packages the information in a structured format that includes: 

   * Previous month: $85 total 

   * Current month: $135 total 

   * Difference: $50 increase 

   * Cause: International roaming charges ($45) + one-time activation fee ($5) 

   * Context: Customer traveled abroad, roaming was active 

7. AI agent response 

   The AI agent receives this clear, contextual information and can now explain to the customer: "I see your bill increased by $50 this month. That’s because you used international roaming while traveling, which added $45, plus a one-time activation fee of $5." 

The bottom line: The customer receives a clear, accurate, and satisfying answer in a matter of seconds. 

Security and compliance are critical concerns when AI agents access enterprise data. Data agents play a key role in maintaining security, making them essential for agentic AI data protection. 

Built-in governance 

Data agents enforce governance rules automatically. They apply: 

• Access controls based on who is requesting the data 

• Data masking to hide sensitive fields 

• Audit logging to track what data was accessed and why

• Privacy rules to ensure CPRA, HIPAA, GDPR, or DORA compliance. 

For example, if a junior customer service representative asks about a particular customer's account, the data agent might mask certain financial details that only senior staff can see. All of this happens transparently, without requiring the AI agent or the user to understand the complexity behind the security rules. 

Limiting data exposure 

By implementing minimum viable data principles, data agents significantly reduce risk. Instead of giving AI agents access to entire databases, data agents provide only what's needed for each specific task.

Leading consulting firms emphasize treating agents like new employees, giving them access only to what they need. Data agents make this practical by automatically determining and enforcing data minimization.

Monitoring and auditing 

Data agents create clear audit trails showing: 

• What data was requested 

• Why it was requested 

• Which systems provided it 

• What transformations were applied 

• Who accessed it 

• When it was accessed 

This visibility is essential for compliance and for investigating any issues that may arise.

Organizations can take different approaches to implementing an LLM agent architecture, depending on their needs and technical capabilities. 

Pre-built data agents 

For common use cases, pre-built data agents offer the fastest path to value. These agents handle standard scenarios like: 

• Answering operational questions 
• Generating synthetic test data 
• Auditing for compliance 
• Basic data retrieval and transformation 

Pre-built agents can be configured to work with your specific data products and systems without custom coding. 

Custom data agents 

For specialized needs, organizations can build custom data agents. Modern platforms provide low-code or no-code builders that make this accessible to business-oriented teams, not just developers. 

Creating a custom data agent typically involves: 

1. Defining the purpose 

   What business problem will this agent solve? What questions should it answer or actions should it perform? 

2. Identifying data sources 

   Which data products or systems contain the relevant information? 

3. Configuring the logic 

   How should the agent reason out data requests? What are the rules for data access and transformation? 

4. Setting governance rules 

   What security and compliance policies must the agent enforce? 

5. Testing and refining 

   Which scenarios should the agent be run through to ensure it works correctly and securely? 

Integration with agentic frameworks 

Data agents operate inside your organization's broader agentic AI framework. This might be built on platforms like Microsoft Copilot Studio, Salesforce Agentforce, AWS Bedrock, or open-source frameworks.

The key is that data agents work alongside other agents, including LLM agents, each specializing in different aspects of the workflow. While some agents handle conversation, others make decisions, and data agents ensure everyone has access to the right information.