01
Objects & State
Represent people, equipment, projects, orders, contracts, metrics and documents — including their current operational state.
Asset AContract v3Pending order
Business Ontology × Enterprise AI Model the business world
Model the business world
before AI enters the workflow
A business ontology organizes enterprise objects, relationships, rules, actions and permissions into a shared context. DesireCore uses this method to connect AgentFS, Skills, multi-agent teams, Workflows and governance — moving AI from finding information to completing work within explicit boundaries.
This page presents an ontology-driven implementation method. Data connections, automated actions and enterprise capabilities depend on the current release, authorized scope and solution design.
An ontology is not a glossary. It is a shared model of business objects and action rules.
Knowledge without action leaves AI in Q&A. Action without semantics leaves automation blind to business state. A business ontology brings together what something is, how it relates, which rules apply and what may happen next.
BUSINESS ONTOLOGY
A business context that people and agents can operate on together
From orders, equipment, contracts and documents to ownership, expert rules, approval actions and access boundaries, every concept can point to its source, current state and permitted operations.
02
Links & Evidence
Capture ownership, dependencies, lineage, versions, citations and sources so conclusions remain tied to business context.
OwnerStandard citedSource page
03
Rules & Logic
Carry expert rules, decision trees, SOPs, algorithms, models and constraints that define judgment and handling.
Review ruleApproval thresholdCalculation
04
Actions & Governance
Define retrieval, generation, approval, notification, execution and write-back for people or agents, with policy and audit.
Submit reviewGenerate reportWrite back
Semantics enables understanding, logic enables judgment, actions enable execution, and policy plus evidence enables trust. Together they form an operational business ontology for AI.
Ontology, knowledge graphs and RAG solve different layers of the problem.
They work well together, but they are not interchangeable. RAG retrieves relevant material, a knowledge graph organizes entities and links, and a business ontology adds meaning, rules, executable actions and governance.
| Capability | Core question | Typical output | Boundary when used alone |
|---|---|---|---|
| RAG / Semantic Retrieval | Which material is relevant now? | Chunks, citations, summaries | Finds information but does not inherently model full business state or permitted action |
| Knowledge Graph | Which entities exist and how are they linked? | Entities, relationships, paths, sources | Expresses relationships but may not include process, policy or action semantics |
| Business Ontology | How is the business world defined, judged and changed? | Objects, links, rules, actions, permissions | Must be maintained with real data, systems and frontline workflows |
| Agent Workflow | How is a goal completed through tasks? | Plans, tool calls, deliverables, receipts | Without ontology constraints, cross-system meaning and governance fragment easily |
Combined pattern: RAG supplies material, the graph supplies relationships, the ontology supplies business meaning and action boundaries, and agents plan, collaborate and execute toward a goal.
Do not create another conceptual silo. Ground the ontology method in manageable assets.
Current DesireCore capabilities cover the assets, knowledge, logic, execution, collaboration and governance needed by an ontology-driven implementation. This describes a composition method, not a claim of a separate closed ontology engine.
AgentFS
Keep business assets inspectable
Organize identities, rules, memory, sources, Skills, Workflows, versions and receipts for inspection, migration and maintenance.
Memory / Knowledge Graph
Connect durable knowledge and relationships
Store terminology, object relationships, corrections and task experience with explicit scope and provenance.
Skills / Decision Trees
Carry expert rules and judgment
Turn rules, examples, decision paths and acceptance criteria into reusable, versioned capabilities.
SOP / Workflow
Compose business actions into stable loops
Combine deterministic steps, agent judgment, tools, exception handling and human confirmation.
Multi-Agent Runtime
Share meaning across specialized roles
Research, drafting, review and delivery roles collaborate around the same objects and evidence boundaries.
Hook / Policy / Receipts
Constrain action and preserve accountability
Use permissions, policy, approvals, blocks, activity records and receipts around real operations.
Start with one real business chain — not a complete conceptual universe.
An FDE (Forward Deployed Engineer) approach works at the operational edge with domain experts to model, integrate and validate. Every stage produces reviewable evidence, not just a conceptual document.
- 01Core question
Work at the operational edge
Who makes which decision under what conditions?
Shadow real tasks and map roles, sources, systems, exception paths, risk boundaries and delivery objectives.
- Stage output
- Workflow map, ownership, inputs, outputs and baseline metrics
- Acceptance evidence
- Sample tasks, source material, system screens, current time and error types
- 02Core question
Model the minimum viable ontology
Which concepts are essential to complete this chain?
Define critical objects, links, state, rules, actions, permissions and evidence requirements around high-value decisions.
- Stage output
- Object dictionary, relationship map, rule inventory, action and policy table
- Acceptance evidence
- Expert sign-off, terminology conflicts, rule boundaries and missing items
- 03Core question
Assemble the agent work loop
How does meaning enter real execution?
Connect AgentFS, Skills, tools, Workflows and Human Gates, then define exception handling and write-back boundaries.
- Stage output
- Runnable scenario, role split, approval gates and delivery templates
- Acceptance evidence
- Execution traces, tool calls, approvals, deliverables and receipts
- 04Core question
Evaluate, release and iterate
How do we prove value and expand safely?
Build test sets, business metrics and failure categories; use replay, human review and version comparison to widen automation gradually.
- Stage output
- Acceptance report, evaluation baseline, operations dashboard and iteration plan
- Acceptance evidence
- Pass rate, exceptions, human takeover, cost, latency and business outcome
The same method produces a different ontology in every business domain.
An ontology must serve concrete decisions and actions. These examples move from objects and rules to reviewable delivery without treating AI output as professional sign-off.
01
AI Proposal Writing
Turn tender requirements and corporate evidence into a traceable, reviewable bid.
- Key objects
- Requirements, qualifications, scoring criteria, corporate evidence, chapters, risks
- Rules & logic
- Coverage, qualification fit, scoring map, format and prohibited claims
- Business actions
- Decompose, match evidence, draft in parallel, review risk and deliver Word
Commitments, pricing, qualifications and the final submission require accountable human confirmation.
Read the related practice02
Engineering Drawing Review
Convert multi-page drawings into structured facts with location, version and provenance.
- Key objects
- Equipment, lines, instruments, loops, sheets, coordinates, revisions, review rules
- Rules & logic
- Topology, identifier consistency, rule checks and frozen baselines
- Business actions
- Recognize, link, compare and produce issue lists with evidence locations
Baselines must be frozen first; qualified engineers review and sign off findings.
Read the related practice03
Contract Review & Obligation Tracking
Keep clause risk, accountable parties and fulfillment actions aligned.
- Key objects
- Contracts, clauses, parties, obligations, deadlines, amounts, risks, approvals
- Rules & logic
- Corporate templates, legal requirements, thresholds, missing clauses and conflicts
- Business actions
- Review clauses, cite evidence, grade risk, route approval and produce reports
Legal judgment, material risk acceptance and signature remain with authorized people.
Read the related practiceTrustworthy deployment evaluates context, action and accountability — not just the answer.
Production acceptance spans semantic correctness, task quality, operational safety and durable operation. Model output is only one part.
Semantics & Evidence
Verify object recognition, complete links and state, and whether conclusions trace to source, version and location.
- Terminology consistency
- Source coverage
- Evidence location
Task & Quality
Use real test sets and boundary cases to validate rules, delivery format, domain requirements and human review.
- Test pass rate
- Reviewer variance
- Boundary cases
Action & Permission
Check least privilege, approval gates, dangerous-operation blocks, write-back and sensitive-data handling.
- Policy decisions
- Human Gates
- Blocks & masking
Operations & Improvement
Track cost, latency, exceptions, human takeover and business results; govern change through versions.
- Execution receipts
- Failure taxonomy
- Version comparison
Business ontology FAQ
Concise answers from concept boundaries to delivery
01Is a business ontology the same as a knowledge graph?
No. A knowledge graph primarily expresses entities, properties and links. A business ontology also defines business meaning, state, rules, executable actions and policy boundaries. A graph can be an important part of the ontology.
02Why do we need an ontology if we already have RAG?
RAG retrieves relevant material but does not inherently know the complete business state, accountability relationships, rule priority or permitted actions. An ontology gives retrieved content a durable business context and constrains agent judgment and execution.
03Does ontology work require a large data-governance program first?
It should not. A more practical route starts with one valuable workflow, builds a minimum viable ontology, validates it on real tasks, and expands objects, rules and actions incrementally.
04Does DesireCore provide a standalone ontology engine?
This page describes an ontology-driven implementation method. DesireCore uses AgentFS, memory and knowledge graphs, Skills, Workflows, multi-agent teams and governance to carry the related assets and execution. Scope depends on the current release and solution design.
05What does an FDE do in an ontology and AI project?
The FDE connects frontline operations to engineering: identifying real decisions and boundaries, modeling the minimum ontology, integrating data and tools, assembling the workflow and iterating from business tests and operational evidence.
From formal semantics to an operational ontology for business action
This page uses common principles from public industry references and maps them independently to current DesireCore capabilities. References explain the method and do not imply product affiliation.
W3C OWL 2 Overview
Standards foundation for ontology languages, classes, properties, individuals and relationships.
Palantir Ontology Overview
Public explanation of connecting objects, links, actions, functions and governance to operations.
Palantir AI FDE Overview
Public workflow for using natural language across integration, ontology editing, functions, governance and validation.
ONTOLOGY × AGENT OS
Build the first minimum viable ontology around one real business chain.
Define objects, rules, actions, permissions and acceptance evidence before choosing how to connect agents, tools and business systems.
