Solix Technologies Announces General Availability of Data Sense and Data Ask: The Bridge From AI-Ready to AI-Activated Data reflects a larger shift in enterprise AI: organizations are moving beyond preparing information for generative AI toward making that information usable by AI agents.This makes agent-ready data an increasingly important enterprise data strategy.AI agents need more than access to large amounts of information. They need data that is accurate, contextualized, governed, discoverable, and accessible within clearly defined permissions.An AI agent that can access trusted enterprise knowledge can potentially do much more than answer a question. It can analyze information, recommend actions, interact with applications, and support business workflows.Solix's current enterprise AI positioning describes its platform as combining enterprise data warehousing, semantic intelligence, natural-language interaction, and autonomous AI agents, with Data Sense and Data Ask helping organizations interact with trusted enterprise knowledge. The evolution is therefore:Enterprise Data → AI-Ready Data → Agent-Ready Data → AI Agents → AI-Activated Business Processes
Agent-ready data is enterprise information prepared for AI agents to discover, retrieve, understand, and use within defined governance and security boundaries.It combines several characteristics of AI-ready data with additional requirements for autonomous systems.
Agent-ready data should ideally be:
The objective is not to give AI agents access to more data.The objective is to give them access to the right data with the right context and permissions.
AI-ready data and agent-ready data are closely related, but they are not exactly the same.
AI-ready data is prepared for AI applications such as:
Agent-ready data goes a step further.It must support systems that may:
This creates an additional requirement:The AI must understand not only the information but also what it is allowed to do with it.
A traditional chatbot may simply answer a question.An AI agent can potentially take action.For example:Chatbot:"Which invoices are overdue?"AI Agent:"Which invoices are overdue?"Then:"Which customers have the highest outstanding balances?"Then:"Prepare a collection priority list."Then:"Create a draft notification for approved customers."The second workflow requires substantially more context and control.
Because the agent is moving from:Information retrievaltoDecision support and action.
Agentic AI refers to AI systems designed to pursue goals through multi-step reasoning, tool use, planning, and execution rather than simply generating a single response.An agent may be able to:
This makes data infrastructure extremely important.
An AI agent can only make useful decisions based on the information available to it.If the underlying information is:
the agent's behavior can become unreliable.This creates a simple enterprise AI principle:Better agents require better enterprise data.That is why AI-ready data assessment and preparation are increasingly positioned as foundations for agentic AI. Solix's 2026 SOLIXEmpower agenda, for example, explicitly includes AI-Ready Data Assessment as the strategic foundation for Agentic AI and autonomous enterprises.
AI agents may need access to multiple forms of enterprise information.
Examples include:
Examples include:
Examples include:
Examples include:
An enterprise agent may need information from several of these categories to complete a single task.
Business processes rarely exist inside one application.Consider a customer service agent.To resolve a customer issue, it might need:CRM data
Order history
Invoice information
Contract terms
Support documentsThe information may reside across several systems.An AI agent therefore needs a way to understand relationships across enterprise applications.
Semantic intelligence gives AI systems business context.Consider the term:CustomerIn different applications, it could appear as:
A semantic layer can connect these different representations.
Without semantic context, an AI agent may treat related concepts as unrelated data points.With semantic intelligence, it can understand:Business concept → Data entity → Application → RelationshipThis makes enterprise data more usable for AI.
An Application Knowledge Graph (AKG) provides a semantic representation of application data, relationships, business terminology, and structures.It can help AI understand complex enterprise applications.For example:Customer↓Orders↓Invoices↓Payments↓Outstanding BalanceInstead of seeing disconnected tables, AI can understand the business relationships between entities.Solix's Data Sense positioning describes its Application Knowledge Graph as a semantic layer that maps application schemas and relationships, providing the intelligence Data Ask can query.
An AI agent needs to understand more than table names.It needs to understand:
An Application Knowledge Graph can provide this context.
Raw Schema↓Application Understanding↓Semantic Relationships↓Business Context↓AI-Ready Knowledge↓Agent-Ready Data
Governance becomes even more important when AI can take action.Imagine an employee asks an AI agent:"Show me all employee compensation data."The agent should not simply retrieve everything it can find.It must understand:Who is asking?What are they authorized to access?What data is sensitive?What policies apply?The same principle applies when an agent attempts to take action.
AI agent governance is the framework used to control how AI agents access information, make decisions, interact with systems, and perform actions.
The objective is:Autonomy within defined boundaries.Solix's current enterprise messaging increasingly emphasizes governing autonomous agents and trusted enterprise knowledge as part of enterprise AI.
They can potentially access sensitive data when explicitly authorized and properly controlled.But unrestricted access is dangerous.Sensitive enterprise information can include:
Therefore, an enterprise AI agent should inherit appropriate data access policies.
Classification identifies the sensitivity and business value of information.For example:
| Data Type | Classification |
|---|---|
| Public product information | Public |
| Internal procedures | Internal |
| Customer contracts | Confidential |
| Patient information | Restricted |
| Employee compensation | Highly Restricted |
Once information is classified, organizations can apply appropriate access policies.This creates an important connection:Data Classification → Governance → AI Agent Access
Traditional applications typically operate according to predefined workflows.AI agents can dynamically determine what information to retrieve and which tools to use.That introduces additional risks.
This means AI agent security needs to cover both:The dataandThe agent's behavior.
Least privilege means an AI agent should receive only the permissions required to complete its assigned task.For example:A reporting agent may need:Read accessbut not:Delete accessA finance approval agent may need:Read invoicesand potentially:Create approval recommendationsbut not necessarily:Modify financial records.This reduces the potential impact of an incorrect or compromised agent.
It depends on the risk level of the workflow.
Examples:
These may be suitable for higher levels of automation.
Examples:
These may require:Human approvalbefore execution.The best enterprise architecture should therefore support different levels of autonomy.
Human-in-the-loop AI keeps people involved at important decision points.For example:AI Agent↓Analyzes invoices↓Identifies unusual transaction↓Creates recommendation↓Human Approval↓ActionThis approach allows enterprises to benefit from automation without handing unrestricted control to AI.
Suppose an AI agent recommends a business action.The organization may need to know:Where did the information come from?Data lineage can help trace:
This improves:
For high-impact AI workflows, traceability becomes particularly important.
Data Sense can serve as an intelligence layer between raw enterprise information and AI applications.Its current positioning includes:
These capabilities help organizations understand both structured application data and unstructured enterprise content.
Discover↓Understand↓Classify↓Enrich↓Govern↓Publish AI-Ready Intelligence↓Activate Through AIThis creates the foundation needed for downstream AI applications.
Data Ask provides a natural-language interaction layer for enterprise data.A user can ask questions using ordinary language rather than navigating complex schemas or manually constructing database queries.This is important because natural-language access can become one of the interfaces through which people interact with enterprise AI.
User:"Which customers have overdue invoices?"↓Data Ask↓Enterprise Data↓Trusted AnswerAn agentic workflow can potentially take this further by using the result as an input to a larger business process.
They serve different but complementary purposes.
Primarily focuses on:Asking questionsRetrieving informationNatural-language interaction
Can potentially focus on:ReasoningPlanningWorkflow executionActionThe underlying enterprise data foundation can support both.This creates a progression:Data Sense → Data Ask → AI AgentsUnderstand → Ask → Act
The relationship is straightforward.
Makes enterprise information:Discoverable + Governed + Contextualized + Accessible
Uses that information to:Reason + Decide + ExecuteTherefore:AI-ready data is the foundation.Agentic AI is one of the activation layers.
A practical roadmap can include the following steps.
Identify structured and unstructured information.
Determine which information matters most to AI use cases.
Identify PII, PHI, financial information, intellectual property, and other sensitive data.
Evaluate accuracy, completeness, consistency, and freshness.
Document ownership, meaning, source, sensitivity, and relationships.
Create mappings between business concepts and technical data.
Define access, retention, privacy, and compliance rules.
Specify exactly what each agent can access and what actions it can perform.
Evaluate responses, retrieval, permissions, and actions.
Track agent activity, data access, and outcomes.
A simplified architecture can look like this:Enterprise Data Sources↓Discovery & Classification↓Data Governance↓Metadata & Semantic Intelligence↓Application Knowledge Graph↓AI-Ready / Agent-Ready Data Layer↓Data Ask / RAG / AI Applications↓AI Agents↓Governed Actions↓Business OutcomesThis architecture separates:Data preparationfromAI executionwhile connecting them through governed intelligence.
Yes.Legacy systems often contain valuable business information.Examples include:
The challenge is making that information understandable and accessible to modern AI applications.
Historical information can provide:
Organizations should therefore consider legacy information as part of their AI data strategy rather than automatically treating it as irrelevant.
An agent may need historical context to make better decisions.For example:"Has this customer experienced similar issues before?"Answering that question may require years of historical information.If the organization only gives AI access to recent operational data, the agent may lack important context.This is why:Historical data + current datacan be more valuable than current data alone.
Yes.Documents can contain valuable business knowledge that agents may need.Examples include:
But the documents need appropriate:
Solix's current Data Sense positioning specifically includes Content Intelligence for indexing enterprise content such as contracts, policies, manuals, and reports.
Shadow AI occurs when employees use unauthorized AI tools because the approved enterprise AI path does not meet their needs.Solix's recent discussion of Shadow AI describes an access gap: employees need answers from enterprise information, but many organizations have not connected AI systems to trusted enterprise content. This creates an important lesson:Governance alone is not enough.If employees cannot easily access trusted enterprise information through approved AI tools, they may seek alternative tools.
Organizations can address the access gap by providing:Trusted enterprise data
Information is distributed across applications.
Incorrect information can produce incorrect actions.
AI may not understand enterprise terminology.
Sensitive information may be exposed.
Agents may have more access than necessary.
Older applications may be difficult to integrate.
Important knowledge may be hidden in documents.
Organizations may not know what agents are doing.
A strong agentic AI security strategy should combine:
Protect the underlying information.
Know which user or agent is making the request.
Control what the agent can access.
Control which applications and tools an agent can call.
Track activity.
Maintain records of important interactions.
Require approval for high-risk actions.
Test agent behavior regularly.The goal is:Trusted autonomy—not unrestricted autonomy.
Organizations can measure progress through metrics such as:Percentage of enterprise data discoveredPercentage of data classifiedPercentage of critical data with metadataPercentage of data mapped to business conceptsPercentage of AI datasets governedPercentage of agent-accessible data with defined permissionsPercentage of high-risk agent workflows with human approvalNumber of unauthorized AI access eventsAgent task success rateAgent policy violation rateThese KPIs can help connect data readiness with AI governance.
Enterprise agentic AI requires a trusted information foundation.The architecture should allow agents to:
This means agentic AI is not simply an AI model problem.It is also a:Data problemGovernance problemSecurity problemArchitecture problem
The next generation of enterprise AI will likely move from:ChatbotstoAI assistantstoAI agentstoMulti-agent enterprise workflows.As that happens, data architecture becomes increasingly important.An AI agent cannot operate effectively without access to reliable enterprise context.The organizations best positioned for this transition will be those that prepare their data before deploying large numbers of autonomous AI systems.
The Solix approach can be understood as a progression.
Understands the data.It helps discover, classify, contextualize, and build semantic intelligence around enterprise information.
Lets users interact with the data.Natural-language questions provide a simple interface for enterprise information.
Uses the data to support workflows and actions.That creates the broader transformation:Understand → Ask → Reason → ActThis is why the Data Sense and Data Ask announcement is relevant beyond a product launch. It represents a broader shift from preparing enterprise information for AI toward actually activating it. Solix's current enterprise AI messaging similarly positions trusted enterprise knowledge as the foundation for AI-powered applications and autonomous agents.
Agent-ready data is enterprise information prepared for AI agents to retrieve, understand, and use within defined governance, security, and permission boundaries.
AI-ready data is prepared for AI applications. Agent-ready data adds requirements for AI systems that may reason, use tools, interact with applications, and perform actions.
AI agents depend on enterprise information to make decisions and complete tasks. Poor-quality or poorly governed data can produce unreliable or risky outcomes.
Agentic AI refers to AI systems capable of pursuing goals through reasoning, planning, tool use, and potentially autonomous execution.
An AI agent is an AI system designed to perform tasks by interpreting goals, retrieving information, reasoning about it, and interacting with tools or systems.
Governance ensures agents only access appropriate information and perform actions within authorized boundaries.
AI agent governance establishes policies and controls for agent identity, permissions, data access, tool use, monitoring, auditing, and actions.
Yes. Enterprise AI agents may need both structured application data and unstructured documents to understand complex business questions.
An Application Knowledge Graph represents enterprise application structures, relationships, business terminology, and semantic context so AI can better understand application data.
Data Sense provides capabilities such as Application Knowledge Graph, Content Intelligence, and Intelligent Classification to help create an AI-ready intelligence layer.
Data Ask provides a natural-language interface for interacting with enterprise data, helping users access governed enterprise information without requiring traditional query-writing skills.
It can help address the access gap by providing employees with a governed way to interact with trusted enterprise information. Solix's recent Shadow AI analysis specifically identifies governed enterprise data access as part of closing that gap.
No. Agent permissions should be based on the agent's purpose, user authorization, data sensitivity, and business risk.
Agent-ready data is likely to become a foundational component of enterprise AI as organizations move from conversational assistants toward autonomous and multi-agent workflows.