Enterprise AI agents need current business meaning, applicable policies, operational state and permissions to make or carry out decisions, according to a September 28, 2026, Constellation Research ShortList video hosted by Michael Ni and Hannah Mason. The video argues that traditional enterprise data management questions—what data exists, whether it is trustworthy and what it means—are no longer sufficient as agents move from recommendations to actions.

A sales pipeline shrinking by 12% does not by itself tell an executive what to do. A decision also depends on which deals are at risk, how “pipeline” is defined, which costs matter, who can approve action and what has changed with customers. Catalogs help govern data, semantics establish meaning, and context adds current conditions, applicable policies and permitted actions.

The video illustrates the difference between finding a policy and applying it: orders above $100,000 require financial approval, restricted customers require compliance review, and margins below 15% require commercial approval. To act, an agent needs the current order value, customer status, projected margin, required approvals and its own permissions. This is described as a shift from document context to served context, delivering relevant information for a decision under the right constraints.

The proposed context infrastructure has three layers: trusted business meaning through consistent definitions and relationships; consistent consumption of that meaning across applications and workflows; and active, current context that carries policy to the point of decision. Related capabilities include semantic management, connected semantics, AI grounding, policy, explainability, lineage, ecosystem integration, feedback and learning, and domain governance. The video says context is becoming active infrastructure rather than documentation.

Because enterprise data and knowledge are spread across systems, customer records may sit in CRM, definitions in a data platform, process state in workflow software, and policies and permissions elsewhere. Actian is pursuing a federated knowledge graph connecting metadata, lineage, business definitions, quality signals, policies and relationships across distributed systems without moving the underlying data.

Platforms will need to automate metadata and relationship discovery, knowledge graph enrichment, quality monitoring, sensitive-data classification and governance workflows. They must also activate context: machine-readable data contracts, APIs, MCP interfaces and other integrations can make governed context available to analytics, applications and agents at runtime. Enterprise AI leaders are urged to ask whether business meaning stays current, agents can access context at runtime, policies apply to specific decisions, operational state is included and systems learn from outcomes.

Enterprise AI agents need current business meaning, applicable policies, operational state and permissions to make or carry out decisions, according to a September 28, 2026, Constellation Research ShortList video hosted by Michael Ni and Hannah Mason. The video argues that traditional enterprise data management questions—what data exists, whether it is trustworthy and what it means—are no longer sufficient as agents move from recommendations to actions.

A sales pipeline shrinking by 12% does not by itself tell an executive what to do. A decision also depends on which deals are at risk, how “pipeline” is defined, which costs matter, who can approve action and what has changed with customers. Catalogs help govern data, semantics establish meaning, and context adds current conditions, applicable policies and permitted actions.

The video illustrates the difference between finding a policy and applying it: orders above $100,000 require financial approval, restricted customers require compliance review, and margins below 15% require commercial approval. To act, an agent needs the current order value, customer status, projected margin, required approvals and its own permissions. This is described as a shift from document context to served context, delivering relevant information for a decision under the right constraints.

The proposed context infrastructure has three layers: trusted business meaning through consistent definitions and relationships; consistent consumption of that meaning across applications and workflows; and active, current context that carries policy to the point of decision. Related capabilities include semantic management, connected semantics, AI grounding, policy, explainability, lineage, ecosystem integration, feedback and learning, and domain governance. The video says context is becoming active infrastructure rather than documentation.

Because enterprise data and knowledge are spread across systems, customer records may sit in CRM, definitions in a data platform, process state in workflow software, and policies and permissions elsewhere. Actian is pursuing a federated knowledge graph connecting metadata, lineage, business definitions, quality signals, policies and relationships across distributed systems without moving the underlying data.

Platforms will need to automate metadata and relationship discovery, knowledge graph enrichment, quality monitoring, sensitive-data classification and governance workflows. They must also activate context: machine-readable data contracts, APIs, MCP interfaces and other integrations can make governed context available to analytics, applications and agents at runtime. Enterprise AI leaders are urged to ask whether business meaning stays current, agents can access context at runtime, policies apply to specific decisions, operational state is included and systems learn from outcomes.