Dynamic Agent Ontology
Give AI the context of the work.
Why business context matters
Enterprise AI does not fail only because a model lacks information. It often fails because the information is not organized for the work.
Business meaning is spread across systems, catalogs, documents, workflows, policies, playbooks, tickets, conversations, and people. Data catalogs may describe tables. Documents may describe processes. Teams may know which source is authoritative. But that knowledge often does not exist in a governed runtime form that AI workflows can use.
The Agentic Ontology makes that operating knowledge explicit, connected, permission-aware, and reusable.
It gives enterprise AI a governed way to understand the business context around a workflow before it acts.
What the Agentic Ontology does
Maps the language of the business
The Agentic Ontology connects business terms, concepts, tools, skills, data sources, tables, documents, scripts, conversations, tickets, and relationships into a typed graph of work.
Grounds AI in approved context
Agents, models, and applications can retrieve context from the ontology instead of relying only on prompts, generic model knowledge, or disconnected search results.
Scopes context by role, domain, and policy
Access controls are enforced as context is retrieved. The same question can return different context depending on the user, workflow, namespace, and permissions involved.
Ranks what matters
The ontology helps surface the most relevant surrounding context for a task: related terms, tools, source systems, documents, skills, and prior work patterns.
Supports review before reuse
Agents and teams can propose updates, but changes can remain in draft until reviewed and promoted. This prevents unreviewed context from silently influencing future workflows.
Preserves context over time
The ontology can support time-aware views of what was known when a decision was made, helping teams understand, audit, and improve AI-enabled work.
Built for real enterprise workflows
The Agentic Ontology is not a static glossary or a slide-deck version of the business.
It is designed to be used by AI workflows at runtime.
It can be hydrated from structured data sources, existing metadata catalogs, customer ontology endpoints, documents, transcripts, SME input, and skill libraries. That means teams can begin with the systems and knowledge sources they already have, then improve coverage over time through workflow use and human review.
The ontology also works with the Data Room, where durable workflow artifacts can be created, versioned, reviewed, and reused. The ontology defines what the enterprise knows. The Data Room preserves what agents and people produce against that knowledge.
Together, they create a governed foundation for long-running, multi-step, and multi-agent work.
