Domain Glossary¶
Status: Canonical
Date Established: 2026-03-14
1. Purpose¶
This glossary defines the key terminology used within the Agile AI domain.
The purpose of this glossary is to:
- establish consistent terminology
- support conceptual clarity
- enable shared understanding across the ecosystem
These definitions apply across the Agile AI ecosystem, including:
- conceptual frameworks
- academic models
- capability architectures
- assessments and reports
- knowledge systems
2. Core Domain Terms¶
Agile AI¶
Agile AI refers to the integration of adaptive execution, machine intelligence, and accountable human judgment within organizational systems.
Core expression:
Agile AI = Adaptive Execution + Machine Intelligence + Accountable Human Judgment
This capability enables organizations to continuously adapt and make informed decisions in complex and rapidly changing environments.
Adaptive Execution¶
Adaptive execution refers to the ability of an organization to modify plans, workflows, and priorities in response to emerging information.
Key characteristics include:
- iterative delivery
- continuous feedback
- rapid learning cycles
- cross-functional collaboration
Adaptive execution ensures responsiveness to change.
Machine Intelligence¶
Machine intelligence refers to computational systems capable of processing information and generating insights using algorithms and data-driven models.
Examples include:
- machine learning systems
- predictive analytics
- automated decision support systems
- pattern recognition models
Machine intelligence extends human analytical capability.
Accountable Human Judgment¶
Accountable human judgment refers to the responsibility of human decision-makers to interpret AI-generated insights and make final decisions.
Human accountability includes responsibility for:
- ethical evaluation
- contextual reasoning
- strategic interpretation
- governance oversight
Artificial intelligence augments human capability but does not replace responsibility.
3. Capability Architecture Terms¶
Capability Architecture¶
A capability architecture is a structured model describing the skills, systems, and processes required to achieve a specific organizational capability.
Within the Agile AI domain, capability architectures define how organizations integrate:
- execution frameworks
- intelligent systems
- human decision processes
Capability Dimension¶
A capability dimension represents a major category within a capability architecture.
Each dimension reflects a distinct area of organizational capability development.
Examples include:
- strategic alignment
- intelligent decision systems
- adaptive execution
- governance and oversight
Capability Assessment¶
A capability assessment is a structured evaluation used to measure an organization's maturity across defined capability dimensions.
Assessments may include:
- diagnostic questionnaires
- scoring models
- insight reports
The purpose is to identify strengths, gaps, and opportunities for capability development.
4. Human–AI Collaboration Terms¶
Human–AI Collaboration¶
Human–AI collaboration refers to the coordinated interaction between human decision-makers and intelligent systems.
In effective collaboration:
- AI systems generate insights
- humans interpret context
- decisions integrate analytical and experiential knowledge
AI-Augmented Decision Making¶
AI-augmented decision making refers to decision processes that incorporate machine-generated insights to support human judgment.
AI systems provide analytical inputs, while humans retain responsibility for final decisions.
5. Organizational Terms¶
Intelligent Organization¶
An intelligent organization is one that systematically integrates data, machine intelligence, and human expertise into operational decision processes.
Such organizations continuously learn and adapt.
Capability Maturity¶
Capability maturity refers to the degree to which an organization has developed structured and repeatable capabilities within a specific domain.
Higher maturity typically reflects:
- defined processes
- established governance
- integrated systems
- consistent outcomes
6. Ecosystem Institutional Terms¶
Agile AI Foundation¶
The Agile AI Foundation is the canonical standards authority responsible for defining the conceptual architecture of the Agile AI domain.
Responsibilities include:
- conceptual frameworks
- governance principles
- domain standards
The Foundation does not provide training or credentials.
Agile AI University¶
Agile AI University operationalizes Agile AI domain standards through structured academic capability systems.
The University provides:
- capability frameworks
- assessment systems
- professional recognition models
- knowledge interfaces
The University operates as an academic and professional body and is not positioned as a commercial training provider.
7. Digital Ecosystem Terms¶
Knowledge Surface¶
A knowledge surface is a digital interface designed to present structured knowledge resources related to the Agile AI domain.
Example:
edu.agileai.university
Credential Infrastructure¶
Credential infrastructure refers to the systems used to issue, manage, and verify professional credentials.
This infrastructure may include:
- credential issuance platforms
- verification services
- credential registries
Institutional Portal¶
An institutional portal provides interactive access to ecosystem services.
Portals may enable:
- user interaction
- capability insights
- institutional services
Example:
portal.agileai.university
8. Governance Terms¶
Design Authority¶
Design Authority refers to the governance mechanism responsible for maintaining architectural consistency across the digital ecosystem.
Responsibilities include:
- architecture governance
- design system stability
- cross-surface consistency
Design System¶
A design system is a structured set of standards, components, and visual rules used to ensure consistent digital interfaces.
Within the Agile AI ecosystem:
- site.css — canonical design system
- refinement.css — controlled refinement layer
9. Terminology Governance¶
This glossary serves as the canonical reference for Agile AI domain terminology.
Updates must follow ecosystem governance procedures and be recorded in the governance log.
Maintaining consistent terminology is essential for long-term clarity and domain integrity.