Foundations
Quantum Ontology
The shared language of Quantum Automation Center for connecting automations, AI agents, business objects, governance, and impact.
Updated: 2026-05-17
Operational language
An operating language for humans, automations, and agents to work from the same reality.
The Quantum Ontology defines how we name processes, objects, decisions, permissions, outputs, and impact inside Quantum Automation Center. It is not another database: it is the shared map that lets teams operate, control, and improve real work with automation and AI.
The ontology connects four layers
Every QAC use case should be explainable with the same vocabulary. That reduces friction between product, engineering, operations, and customers.
Objects
Companies, users, automations, agents, executions, documents, reports, operations, and business entities.
Actions
Run, schedule, approve, retry, cancel, notify, export, invite, configure, or increase capacity.
Rules
Permissions, limits, validation, billing, privacy, languages, operating SLA, and success criteria.
Learning
Impact events, feedback, adoption, errors, time saved, organization usage, and improvement opportunities.
QAC as the control plane
Quantum Automation Center is the surface where that ontology becomes operable
QAC is Quantum's control center: it enables capabilities, orchestrates executions, shows traceability, manages reusable configuration, and turns technical results into decisions users can understand.
Automations and agents
What Quantum is
Quantum is an execution layer for turning enterprise processes into measurable, governed, reusable digital capabilities.
- Applied automation
- Context-aware AI agents
- Operating product
- Measurable impact
What an automation is
An automation is a versioned process that receives inputs, runs controlled steps, and produces verifiable outputs.
- Configurable inputs
- Workflow and activities
- Business objects
- Reports, emails, or artifacts
What an AI agent is
An agent is an AI-assisted capability that understands a task, uses authorized tools, and acts within defined limits.
- Explicit objective
- Allowed tools
- Memory and context
- Guardrails and audit
Automation and agents do not compete; they complement each other
Automation
Best when the flow is repeatable, measurable, and must run with high consistency. Example: monitor shipments, validate documents, generate reports, or consolidate data.
AI agent
Best when the task requires interpretation, conversation, search, reasoning, or adaptation. Example: analyze exceptions, answer questions, or assist decisions.
Governance and continuous improvement
Design principles for the Quantum Ontology
Governance by default
Nothing critical depends on loose prompts: permissions, limits, audit, and allowed data live in the operating model.
Reusable language
Concepts must work for Shipment Monitor, accruals, financial reports, agents, and future use cases.
Impact before activity
We do not only measure runs; we measure operations processed, time saved, assisted decisions, and errors avoided.
Controlled innovation
Frontier capabilities enter with clear boundaries: experiments, flags, human review, privacy, and traceability.
How it applies
Minimum model every use case must declare