Selected concept: Agents and Tool Use
AI Systems · Software and Coding
Agents and Tool Use An AI agent is a software system that pursues a scoped objective through a sequence of steps, sometimes using models, retrieval, tools, rules, and human input. A tool extends what the system can observe or do. Tool availability does not provide intent, permission, or proof that the result is correct.
Scope note This explains an architectural boundary. It does not assess a particular agent's autonomy, reliability, security, safety, or authority to act.
Connections
2 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Agents and Tool Use
Connects to Retrieval and Evaluation Connected from Computing and AI Systems Relationship details
Connects to Retrieval and Evaluation An agent may use retrieval to obtain material and evaluation to compare behavior or outcomes with a stated goal, policy, or expectation.
Connection boundary Retrieving material or passing an evaluation does not grant permission to act, validate every tool result, or replace human review.
Open Retrieval and Evaluation Connected from Computing and AI Systems An agent is a wider software-system role that may coordinate models, retrieval, tools, rules, and human input within a computing environment.
Connection boundary This relationship does not make every AI system an agent or give an agent authority merely because a capability is available.
Open Computing and AI Systems Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details AI Agent Standards Initiative National Institute of Standards and Technology
Used here for NIST describes agent systems capable of autonomous actions on behalf of users and identifies interoperability, security, authentication, identity, and human-agent interaction as active concerns.
Source use Paraphrased from source
Source type official emerging standards and research initiative overview Lessons Learned from the Consortium: Tool Use in Agent Systems National Institute of Standards and Technology and AI Safety Institute Consortium
Used here for Current agent systems may embed general-purpose models in wider software scaffolding that uses tools; tool use can be examined by function, permissions, environment, risk, reliability, monitoring, and autonomy.
Source use Paraphrased from source
Source type official workshop findings and preliminary taxonomy discussion Tools — Model Context Protocol Specification 2025-11-25 Model Context Protocol project
Used here for MCP provides one concrete interface in which servers expose named tools with schemas, clients invoke them, and human denial and untrusted metadata remain explicit.
Source use Paraphrased from source
Source type official stable protocol specification Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology
Used here for Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
Source use Paraphrased from source
Source type official government risk-management framework; version 1.0; published 2023 Open full topic AI Systems · Systems and Thinking
Claims, Evidence, and Review A claim is a statement that can be inspected. Evidence is material considered in relation to a claim. A review decision records what a named reviewer accepted, returned for changes, did not accept, or left unresolved for a stated purpose. These roles should remain separate.
Scope note A citation or review does not create universal truth. Evidence may conflict, and one decision does not automatically apply to every version, audience, or use.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Claims, Evidence, and Review
Connected from Knowledge Graphs and Relationships Connected from Parsers and Structured Transformation Connected from Provenance and Sources Relationship details
Connected from Knowledge Graphs and Relationships A graph may record claims and connect them with evidence or review state, but the encoded node or edge remains a statement to inspect rather than proof by itself.
Connection boundary This relationship does not turn graph structure, connectivity, or consistency into truth, consensus, or evidential sufficiency.
Open Knowledge Graphs and Relationships Connected from Parsers and Structured Transformation A parser result can become material for later evaluation or review while remaining distinguishable from a reviewed claim or accepted interpretation.
Connection boundary Passing parser rules or tests does not turn generated structure into truth, evidence sufficiency, linguistic acceptance, or approval.
Open Parsers and Structured Transformation Connected from Provenance and Sources Provenance helps distinguish a source claim, evidence considered for or against it, transformations applied to it, and the later review decision.
Connection boundary Knowing the origin of material does not determine whether the evidence is sufficient or which conclusion a reviewer must reach.
Open Provenance and Sources Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details RDF 1.1 Concepts and Abstract Syntax World Wide Web Consortium
Used here for RDF provides one formal graph data model for representing information as subject-predicate-object triples.
Source use Paraphrased from source
Source type W3C Recommendation PROV-DM: The PROV Data Model World Wide Web Consortium
Used here for Provenance can describe entities, activities, agents, and relations such as generation, usage, derivation, attribution, association, and delegation.
Source use Paraphrased from source
Source type W3C Recommendation ECO: the Evidence and Conclusion Ontology, an update for 2022 Suvarna Nadendla et al.
Used here for Within biomedical biocuration, ECO distinguishes evidence types from assertion methods and supports capturing evidence for assertions with provenance and quality-control roles.
Source use Paraphrased from source
Source type peer-reviewed primary project report Open full topic AI Systems · Software and Coding
Computing and AI Systems AI systems depend on computing components, but computing alone does not make a system AI. There are different kinds of AI methods; no single method defines the whole field.
Scope note This explains a general system boundary. It does not assess a specific AI product, capability, safety claim, or accessibility outcome.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Computing and AI Systems
Connects to Agents and Tool Use Connects to Data, Datasets, and Algorithms Connects to Model Training and Inference Connected from Deterministic and Probabilistic Systems Relationship details
Connects to Agents and Tool Use An agent is a wider software-system role that may coordinate models, retrieval, tools, rules, and human input within a computing environment.
Connection boundary This relationship does not make every AI system an agent or give an agent authority merely because a capability is available.
Open Agents and Tool Use Connects to Data, Datasets, and Algorithms AI systems use computing components alongside data and algorithms. These elements can participate in one system, but none of them alone defines the complete system.
Connection boundary This relationship does not claim that every computing system uses AI or that every AI system uses the same data or algorithmic design.
Open Data, Datasets, and Algorithms Connects to Model Training and Inference Models, training, and inference can operate as components within a wider AI system built on computing infrastructure.
Connection boundary This relationship does not make a model equivalent to the complete AI system or imply one universal model lifecycle.
Open Model Training and Inference Connected from Deterministic and Probabilistic Systems A computing or AI system may combine deterministic validation, routing, or transformation with probabilistic models or decision components.
Connection boundary This relationship does not classify every AI component as probabilistic, every software component as deterministic, or one mixture as universally preferable.
Open Deterministic and Probabilistic Systems Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-16 Source details Explanatory memorandum on the updated OECD definition of an AI system Organisation for Economic Co-operation and Development
Used here for The scoped distinction between an AI system and a model component; machine-learning and knowledge-based technique families; and the roles of training in model development and inference in producing outputs.
Source use Paraphrased from source
Source type official intergovernmental explanatory material; published 2024 Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology
Used here for Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
Source use Paraphrased from source
Source type official government risk-management framework; version 1.0; published 2023 Open full topic AI Systems · Software and Coding
Data, Datasets, and Algorithms Data is represented material that a computing system can store, communicate, or process. A dataset is an identified collection of data. An algorithm is a defined procedure for transforming input into output. These roles can work together, but they are not interchangeable.
Scope note This explains a general computing boundary. It does not assess data quality, dataset suitability, algorithm performance, privacy, bias, or fitness for a particular AI system.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Data, Datasets, and Algorithms
Connected from Computing and AI Systems Connects to Model Training and Inference Connects to Retrieval and Evaluation Connected from Parsers and Structured Transformation Relationship details
Connected from Computing and AI Systems AI systems use computing components alongside data and algorithms. These elements can participate in one system, but none of them alone defines the complete system.
Connection boundary This relationship does not claim that every computing system uses AI or that every AI system uses the same data or algorithmic design.
Open Computing and AI Systems Connects to Model Training and Inference Training can use data, an objective, and a learning procedure to develop or adjust a model. Inference then uses a model and input in a different role.
Connection boundary This relationship does not assess whether a dataset is suitable, representative, lawful, private, or sufficient for a particular model.
Open Model Training and Inference Connects to Retrieval and Evaluation Retrieval looks through an identified collection, while evaluation can use recorded cases, expectations, and observations to check bounded behavior.
Connection boundary This relationship does not prove that a collection is complete, that retrieved material is relevant, or that an evaluation method fits every use.
Open Retrieval and Evaluation Connected from Parsers and Structured Transformation Parsing uses algorithms to recognize or organize input, while schemas and data structures can define the forms that the parser accepts and produces.
Connection boundary This relationship does not make every algorithm a parser or establish that accepted input is complete, safe, meaningful, or correct.
Open Parsers and Structured Transformation Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details Data — Glossary National Institute of Standards and Technology, Computer Security Resource Center
Used here for Data can be represented for communication, interpretation, processing, storage, or transmission; the exact term remains scoped to its identified source context.
Source use Paraphrased from source
Source type official terminology index pointing to named source publications Algorithm, in Dictionary of Algorithms and Data Structures National Institute of Standards and Technology
Used here for An algorithm can be described as a computable set of steps intended to achieve a result, separately from one concrete source-code file or implementation.
Source use Paraphrased from source
Source type official technical dictionary entry; entry modified 2020 Data Catalog Vocabulary (DCAT) — Version 3 World Wide Web Consortium
Used here for A dataset is a conceptual collection of data that can be distinguished from its distributions and data services and described with identifiers, versions, provenance, and access metadata.
Source use Paraphrased from source
Source type W3C Recommendation Open full topic AI Systems · Mathematics and Logic · Systems and Thinking
Deterministic and Probabilistic Systems A deterministic process is expected to reproduce the same result when its declared input, prior state, rules, versions, dependencies, and execution conditions are the same. A probabilistic process represents or produces outcomes using probabilities and may express variation or a distribution of possibilities. One larger system can contain both deterministic and probabilistic parts.
Scope note Repeatability does not prove correctness, and probabilistic does not mean uncontrolled or unreliable. The exact behavior and useful interpretation depend on the stated contract, context, and evaluation method.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Deterministic and Probabilistic Systems
Connects to Computing and AI Systems Connects to Model Training and Inference Connects to State Machines and Transitions Relationship details
Connects to Computing and AI Systems A computing or AI system may combine deterministic validation, routing, or transformation with probabilistic models or decision components.
Connection boundary This relationship does not classify every AI component as probabilistic, every software component as deterministic, or one mixture as universally preferable.
Open Computing and AI Systems Connects to Model Training and Inference Model training and inference may use probabilistic methods while operating inside deterministic software contracts, data pipelines, validation rules, and presentation boundaries.
Connection boundary Deterministic surrounding code does not make a model output automatically correct, and probabilistic model behavior does not describe the entire system.
Open Model Training and Inference Connects to State Machines and Transitions A state-machine contract can define deterministic transition selection under stated conditions, while other systems may attach probabilities to states, events, or possible transitions.
Connection boundary This relationship does not make every state machine deterministic or define one universal probabilistic state model.
Open State Machines and Transitions Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details State Chart XML (SCXML): State Machine Notation for Control Abstraction World Wide Web Consortium
Used here for One concrete state-machine execution model with active-state configurations, transitions, ordered processing, and deterministic behavior under its declared conditions.
Source use Paraphrased from source
Source type W3C Recommendation Probability Distribution — Glossary National Institute of Standards and Technology
Used here for A probability distribution assigns probabilities to possible outcomes of a random variable within the terminology scope of its named NIST sources.
Source use Paraphrased from source
Source type official terminology index pointing to named NIST source publications Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology
Used here for Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
Source use Paraphrased from source
Source type official government risk-management framework; version 1.0; published 2023 Open full topic Software and Coding · Systems and Thinking
Intermediate Representations An intermediate representation is a structured form used between an input and a later output. It lets separate stages exchange selected meaning, constraints, state, or instructions without requiring every stage to understand the complete original input or final presentation. Because the representation selects what to preserve, its schema, version, and missing-value behavior matter.
Scope note An intermediate representation is not neutral or complete. A stable structure can make transformation inspectable, but it does not guarantee that the selected information or downstream result is correct.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Intermediate Representations
Connects to Kinematics and Motion Connects to Knowledge Representation Connects to State Machines and Transitions Connected from Parsers and Structured Transformation Relationship details
Connects to Kinematics and Motion A renderer-independent intermediate representation can preserve selected motion intent, spatial references, constraints, or timing before a later component converts them into body- or renderer-specific output.
Connection boundary This relationship does not claim that a generic structure contains accepted motion, that every renderer can use it, or that the rendered result is physically or linguistically correct.
Open Kinematics and Motion Connects to Knowledge Representation An intermediate representation deliberately expresses selected information so another stage can inspect or use it. That makes it one scoped form of knowledge or program representation.
Connection boundary Not every knowledge representation is an intermediate program form, and a useful intermediate structure does not represent everything known about its subject.
Open Knowledge Representation Connects to State Machines and Transitions An intermediate representation can carry the named states, events, constraints, or instructions that a later stateful stage uses to evaluate allowed transitions.
Connection boundary Storing state-related fields does not create an executable state machine or prove that the transition model is complete or correct.
Open State Machines and Transitions Connected from Parsers and Structured Transformation A parser can turn accepted input into an intermediate representation that later stages can inspect and transform without repeatedly interpreting the original form.
Connection boundary This relationship does not require every parser to produce a separate intermediate representation or guarantee that the produced structure preserves every relevant meaning.
Open Parsers and Structured Transformation Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details Python Glossary — bytecode Python Software Foundation
Used here for In CPython, Python source is compiled into bytecode as an internal intermediate representation, and that bytecode is not guaranteed to remain stable across Python releases or virtual machines.
Source use Paraphrased from source
Source type official implementation documentation; versioned Python 3 documentation Open full topic Mathematics and Logic · Software and Coding · Systems and Thinking
Kinematics and Motion Kinematics describes how position, orientation, and motion change over time without by itself explaining the forces or intent behind that motion. In software and motion planning, a kinematic representation may describe joints, connected segments, coordinate frames, constraints, and timed changes so that movement can be inspected before rendering or execution.
Scope note A mathematically consistent motion description does not by itself prove physical feasibility, linguistic or cultural correctness, accessibility, safety, naturalness, or approval for a person or product.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Kinematics and Motion
Connected from Intermediate Representations Connects to Units, Frames, and Uncertainty Connected from State Machines and Transitions Connected from Vectors and Coordinate Frames Relationship details
Connected from Intermediate Representations A renderer-independent intermediate representation can preserve selected motion intent, spatial references, constraints, or timing before a later component converts them into body- or renderer-specific output.
Connection boundary This relationship does not claim that a generic structure contains accepted motion, that every renderer can use it, or that the rendered result is physically or linguistically correct.
Open Intermediate Representations Connects to Units, Frames, and Uncertainty A motion description needs units, frames, timing, conventions, and limitations so positions, orientations, velocities, and constraints can be interpreted consistently.
Connection boundary Complete context can make a motion record inspectable, but it does not prove accuracy, physical realism, safety, accessibility, or approval.
Open Units, Frames, and Uncertainty Connected from State Machines and Transitions A motion-planning system can use named states and transitions to organize phases, constraints, timing, or explicit failures while keeping the geometric motion representation separate.
Connection boundary A transition sequence does not supply missing spatial values, prove smooth or feasible movement, or establish linguistic, cultural, or product correctness.
Open State Machines and Transitions Connected from Vectors and Coordinate Frames Kinematic descriptions can use vectors and coordinate frames to express position, direction, orientation, velocity, or other motion-related quantities.
Connection boundary A coordinate description does not by itself provide dynamics, physical feasibility, intent, meaning, or an accepted motion plan.
Open Vectors and Coordinate Frames Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details University Physics Volume 1 — 5.1 Forces OpenStax
Used here for Within introductory Newtonian-physics scope, kinematics describes how objects move through quantities such as velocity and acceleration, while dynamics addresses forces and causes of motion.
Source use Paraphrased from source
Source type open educational university-physics textbook section The International System of Units (SI Brochure), 9th edition, version 4.01 Bureau International des Poids et Mesures
Used here for The scoped roles of quantity values, numbers, units, dimensions, and angle-unit context when expressing and interpreting numerical values.
Source use Paraphrased from source
Source type official BIPM publication and authoritative SI reference; ninth edition published 2019; version 4.01 International Vocabulary of Metrology — Basic and General Concepts and Associated Terms (VIM), 3rd edition, JCGM 200:2012 Joint Committee for Guides in Metrology
Used here for Scoped distinctions among quantity, quantity value, numerical value, measurement unit, dimension, quantity-value scale, conversion, and measurement-uncertainty roles.
Source use Paraphrased from source
Source type official international metrology vocabulary; third edition; corrected 2012 publication OGC Abstract Specification Topic 2: Referencing by coordinates (Including corrigendum 1 and corrigendum 2), OGC 18-005r8, version 6.0.8 Open Geospatial Consortium
Used here for Within its OGC and ISO 19111 scope, distinctions among coordinate systems, coordinate reference systems, datums and reference frames, coordinate operations, coordinate conversions, coordinate transformations, dynamic reference frames, coordinate epochs, and relevant frame-reference, datum-anchor, transformation-reference, and parameter-reference epoch roles.
Source use Paraphrased from source
Source type official OGC publicly available standard and Abstract Specification; exact document 18-005r8, version 6.0.8 Open full topic Mathematics and Logic · Systems and Thinking
Knowledge Graphs and Relationships A knowledge graph uses identified nodes and explicit relationships to represent selected knowledge or context. Its structure can make connections inspectable and navigable. A node or edge records a statement within a declared scope; it does not become true merely because it appears in a graph.
Scope note This explains a general graph boundary. It does not establish completeness, causation, consensus, formal reasoning, or database quality.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Knowledge Graphs and Relationships
Connects to Claims, Evidence, and Review Connects to Provenance and Sources Connected from Knowledge Representation Relationship details
Connects to Claims, Evidence, and Review A graph may record claims and connect them with evidence or review state, but the encoded node or edge remains a statement to inspect rather than proof by itself.
Connection boundary This relationship does not turn graph structure, connectivity, or consistency into truth, consensus, or evidential sufficiency.
Open Claims, Evidence, and Review Connects to Provenance and Sources Provenance can show where a graph statement came from, which activity created or changed it, and which review or version applies.
Connection boundary A complete trace does not automatically make the graph statement correct, accepted, current, or public.
Open Provenance and Sources Connected from Knowledge Representation A knowledge graph is one way to represent selected concepts or claims through identified nodes and explicit relationships.
Connection boundary Not every knowledge representation is a graph, and a graph does not by itself provide a complete ontology or verified truth.
Open Knowledge Representation Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details Knowledge Graphs Aidan Hogan et al.
Used here for Knowledge graph has multiple definitions; a useful broad scope treats entities as nodes and relationships as edges, and different graph models may be used.
Source use Paraphrased from source
Source type peer-reviewed survey research; published March 2021 RDF 1.1 Concepts and Abstract Syntax World Wide Web Consortium
Used here for RDF provides one formal graph data model for representing information as subject-predicate-object triples.
Source use Paraphrased from source
Source type W3C Recommendation Open full topic AI Systems · Systems and Thinking
Knowledge Representation Knowledge representation is the deliberate expression of selected concepts, relationships, constraints, sources, uncertainty, and review state in a form that people or tools can inspect. A representation helps organize meaning for a purpose; it is not the represented thing or proof that every recorded claim is true.
Scope note Every representation selects and simplifies. This does not claim that one representation is complete, universally correct, or suitable for every person, community, or use.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Knowledge Representation
Connected from Intermediate Representations Connects to Knowledge Graphs and Relationships Connects to Retrieval and Evaluation Relationship details
Connected from Intermediate Representations An intermediate representation deliberately expresses selected information so another stage can inspect or use it. That makes it one scoped form of knowledge or program representation.
Connection boundary Not every knowledge representation is an intermediate program form, and a useful intermediate structure does not represent everything known about its subject.
Open Intermediate Representations Connects to Knowledge Graphs and Relationships A knowledge graph is one way to represent selected concepts or claims through identified nodes and explicit relationships.
Connection boundary Not every knowledge representation is a graph, and a graph does not by itself provide a complete ontology or verified truth.
Open Knowledge Graphs and Relationships Connects to Retrieval and Evaluation Retrieval can select inspectable material from a represented collection, and evaluation can compare the observed selection with a stated expectation.
Connection boundary Retrieval does not train a model, prove the selected material true, or show that the representation is complete.
Open Retrieval and Evaluation Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details What Is a Knowledge Representation? Randall Davis, Howard Shrobe, and Peter Szolovits
Used here for Knowledge representation can serve several roles, including acting as a surrogate for selected aspects of the world, expressing ontological commitments, supporting inference, and providing a medium for human expression.
Source use Paraphrased from source
Source type peer-reviewed primary research article Open full topic AI Systems · Systems and Thinking
Model Training and Inference Training can develop or adjust a model. Inference can use a model to produce an output. These are different roles. A model can be one part of an AI system. An output is not automatically correct.
Scope note Not every AI model is trained in the same way. This does not assess a particular model, dataset, or output.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Model Training and Inference
Connected from Computing and AI Systems Connected from Data, Datasets, and Algorithms Connected from Deterministic and Probabilistic Systems Connects to Retrieval and Evaluation Relationship details
Connected from Computing and AI Systems Models, training, and inference can operate as components within a wider AI system built on computing infrastructure.
Connection boundary This relationship does not make a model equivalent to the complete AI system or imply one universal model lifecycle.
Open Computing and AI Systems Connected from Data, Datasets, and Algorithms Training can use data, an objective, and a learning procedure to develop or adjust a model. Inference then uses a model and input in a different role.
Connection boundary This relationship does not assess whether a dataset is suitable, representative, lawful, private, or sufficient for a particular model.
Open Data, Datasets, and Algorithms Connected from Deterministic and Probabilistic Systems Model training and inference may use probabilistic methods while operating inside deterministic software contracts, data pipelines, validation rules, and presentation boundaries.
Connection boundary Deterministic surrounding code does not make a model output automatically correct, and probabilistic model behavior does not describe the entire system.
Open Deterministic and Probabilistic Systems Connects to Retrieval and Evaluation Training, inference, retrieval, and evaluation are different processes that can interact inside an AI-assisted system. Evaluation may inspect behavior at more than one of those layers.
Connection boundary Passing a retrieval or model evaluation does not automatically prove answer truth, safety, accessibility, or general capability.
Open Retrieval and Evaluation Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-16 Source details Explanatory memorandum on the updated OECD definition of an AI system Organisation for Economic Co-operation and Development
Used here for The scoped distinction between an AI system and a model component; machine-learning and knowledge-based technique families; and the roles of training in model development and inference in producing outputs.
Source use Paraphrased from source
Source type official intergovernmental explanatory material; published 2024 Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology
Used here for Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
Source use Paraphrased from source
Source type official government risk-management framework; version 1.0; published 2023 Open full topic Software and Coding · Systems and Thinking
Parsers and Structured Transformation A parser examines input according to a declared grammar, format, or set of rules and produces a structured result that later stages can inspect. A structured transformation then uses explicit mappings or rules to carry selected information from one representation into another. A successful parse shows that the input matched the parser's accepted structure; it does not by itself prove that the input was understood correctly or that the result is true.
Scope note This describes a general software boundary. It does not define one universal parser or approve a linguistic interpretation, translation, motion plan, or product output.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Parsers and Structured Transformation
Connects to Claims, Evidence, and Review Connects to Data, Datasets, and Algorithms Connects to Intermediate Representations Relationship details
Connects to Claims, Evidence, and Review A parser result can become material for later evaluation or review while remaining distinguishable from a reviewed claim or accepted interpretation.
Connection boundary Passing parser rules or tests does not turn generated structure into truth, evidence sufficiency, linguistic acceptance, or approval.
Open Claims, Evidence, and Review Connects to Data, Datasets, and Algorithms Parsing uses algorithms to recognize or organize input, while schemas and data structures can define the forms that the parser accepts and produces.
Connection boundary This relationship does not make every algorithm a parser or establish that accepted input is complete, safe, meaningful, or correct.
Open Data, Datasets, and Algorithms Connects to Intermediate Representations A parser can turn accepted input into an intermediate representation that later stages can inspect and transform without repeatedly interpreting the original form.
Connection boundary This relationship does not require every parser to produce a separate intermediate representation or guarantee that the produced structure preserves every relevant meaning.
Open Intermediate Representations Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details The Python Language Reference — Introduction and Lexical analysis Python Software Foundation
Used here for One concrete language-reference example in which lexical analysis produces tokens for syntactic parsing under declared grammar rules, while language rules and implementation details remain distinguishable.
Source use Paraphrased from source
Source type official programming-language documentation; versioned Python 3 documentation Algorithm, in Dictionary of Algorithms and Data Structures National Institute of Standards and Technology
Used here for An algorithm can be described as a computable set of steps intended to achieve a result, separately from one concrete source-code file or implementation.
Source use Paraphrased from source
Source type official technical dictionary entry; entry modified 2020 Open full topic AI Systems · Systems and Thinking
Provenance and Sources Provenance records where a claim, artifact, input, or output came from and how it was created, selected, changed, or reviewed. It helps someone trace a result back through its sources and the activities that shaped it.
Scope note Provenance can explain origin and process. It does not by itself prove correctness, reliability, consent, authority, or permission for reuse.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Provenance and Sources
Connected from Knowledge Graphs and Relationships Connects to Claims, Evidence, and Review Connects to Units, Frames, and Uncertainty Relationship details
Connected from Knowledge Graphs and Relationships Provenance can show where a graph statement came from, which activity created or changed it, and which review or version applies.
Connection boundary A complete trace does not automatically make the graph statement correct, accepted, current, or public.
Open Knowledge Graphs and Relationships Connects to Claims, Evidence, and Review Provenance helps distinguish a source claim, evidence considered for or against it, transformations applied to it, and the later review decision.
Connection boundary Knowing the origin of material does not determine whether the evidence is sufficient or which conclusion a reviewer must reach.
Open Claims, Evidence, and Review Connects to Units, Frames, and Uncertainty Provenance can preserve which quantities, units, coordinate and time context, methods, and limitations accompanied a numerical result.
Connection boundary Recording that context explains how to interpret the result; it does not prove the result accurate, valid, observed, or approved.
Open Units, Frames, and Uncertainty Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details PROV-DM: The PROV Data Model World Wide Web Consortium
Used here for Provenance can describe entities, activities, agents, and relations such as generation, usage, derivation, attribution, association, and delegation.
Source use Paraphrased from source
Source type W3C Recommendation Open full topic AI Systems · Systems and Thinking
Retrieval and Evaluation Information retrieval means looking in a stored collection for material that may be relevant to what someone wants to know.
A Kinesema Atlas evaluation can compare retrieved results with a recorded expectation. Passing that check does not prove the knowledge collection is complete, an answer is true, or the method fits every use.
Scope note This describes one limited kind of retrieval evaluation. It does not prove completeness, truth, safety, accessibility, or product readiness.
Connections
4 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Retrieval and Evaluation
Connected from Agents and Tool Use Connected from Data, Datasets, and Algorithms Connected from Knowledge Representation Connected from Model Training and Inference Relationship details
Connected from Agents and Tool Use An agent may use retrieval to obtain material and evaluation to compare behavior or outcomes with a stated goal, policy, or expectation.
Connection boundary Retrieving material or passing an evaluation does not grant permission to act, validate every tool result, or replace human review.
Open Agents and Tool Use Connected from Data, Datasets, and Algorithms Retrieval looks through an identified collection, while evaluation can use recorded cases, expectations, and observations to check bounded behavior.
Connection boundary This relationship does not prove that a collection is complete, that retrieved material is relevant, or that an evaluation method fits every use.
Open Data, Datasets, and Algorithms Connected from Knowledge Representation Retrieval can select inspectable material from a represented collection, and evaluation can compare the observed selection with a stated expectation.
Connection boundary Retrieval does not train a model, prove the selected material true, or show that the representation is complete.
Open Knowledge Representation Connected from Model Training and Inference Training, inference, retrieval, and evaluation are different processes that can interact inside an AI-assisted system. Evaluation may inspect behavior at more than one of those layers.
Connection boundary Passing a retrieval or model evaluation does not automatically prove answer truth, safety, accessibility, or general capability.
Open Model Training and Inference Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-19 Source details Introduction to Information Retrieval — Chapter 1: Boolean retrieval Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze
Used here for The general meaning of information retrieval, stored collections, and an information need.
Source use Paraphrased from source
Source type academic textbook section; Cambridge University Press; book published 2008 Introduction to Information Retrieval — Chapter 8: Evaluation in information retrieval Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze
Used here for Relevance is judged against an information need, and retrieved results can include relevant and nonrelevant material.
Source use Paraphrased from source
Source type academic textbook section; Cambridge University Press; book published 2008 AI measurement and evaluation National Institute of Standards and Technology
Used here for Evaluation depends on named measurements, methods, tasks, tools, data, and operating context.
Source use Paraphrased from source
Source type official government program overview Open full topic Mathematics and Logic · Software and Coding · Systems and Thinking
State Machines and Transitions A state machine represents selected behavior through named states and allowed transitions. A state identifies the active configuration within the chosen model; a transition records how an event, condition, or rule can move that configuration to another state or an explicit failure. The model becomes inspectable when its inputs, transition rules, order, and outputs are declared.
Scope note A state machine is one representation of behavior, not a complete description of a real system. It does not establish why an event occurred, whether every state was modelled, or whether an implementation is correct.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept State Machines and Transitions
Connected from Deterministic and Probabilistic Systems Connected from Intermediate Representations Connects to Kinematics and Motion Relationship details
Connected from Deterministic and Probabilistic Systems A state-machine contract can define deterministic transition selection under stated conditions, while other systems may attach probabilities to states, events, or possible transitions.
Connection boundary This relationship does not make every state machine deterministic or define one universal probabilistic state model.
Open Deterministic and Probabilistic Systems Connected from Intermediate Representations An intermediate representation can carry the named states, events, constraints, or instructions that a later stateful stage uses to evaluate allowed transitions.
Connection boundary Storing state-related fields does not create an executable state machine or prove that the transition model is complete or correct.
Open Intermediate Representations Connects to Kinematics and Motion A motion-planning system can use named states and transitions to organize phases, constraints, timing, or explicit failures while keeping the geometric motion representation separate.
Connection boundary A transition sequence does not supply missing spatial values, prove smooth or feasible movement, or establish linguistic, cultural, or product correctness.
Open Kinematics and Motion Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details State Chart XML (SCXML): State Machine Notation for Control Abstraction World Wide Web Consortium
Used here for One concrete state-machine execution model with active-state configurations, transitions, ordered processing, and deterministic behavior under its declared conditions.
Source use Paraphrased from source
Source type W3C Recommendation Open full topic Mathematics and Logic · Systems and Thinking
Units, Frames, and Uncertainty A numerical value needs context before it can be interpreted. That context includes what the number represents, how it is expressed, any coordinate and time information that applies, and the uncertainty or limitation that qualifies it. Naming this context does not establish how the result was obtained. It also does not establish that the result is accurate, historically correct, scientifically valid, or approved.
Scope note This identifies context needed for interpretation. It does not validate the result.
Connections
3 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Units, Frames, and Uncertainty
Connected from Kinematics and Motion Connected from Provenance and Sources Connected from Vectors and Coordinate Frames Relationship details
Connected from Kinematics and Motion A motion description needs units, frames, timing, conventions, and limitations so positions, orientations, velocities, and constraints can be interpreted consistently.
Connection boundary Complete context can make a motion record inspectable, but it does not prove accuracy, physical realism, safety, accessibility, or approval.
Open Kinematics and Motion Connected from Provenance and Sources Provenance can preserve which quantities, units, coordinate and time context, methods, and limitations accompanied a numerical result.
Connection boundary Recording that context explains how to interpret the result; it does not prove the result accurate, valid, observed, or approved.
Open Provenance and Sources Connected from Vectors and Coordinate Frames Vector components need units, coordinate representation, frame, and applicable time where relevant before their values can be interpreted or compared.
Connection boundary Naming that context does not validate the vector, select the correct transformation, or establish measurement accuracy.
Open Vectors and Coordinate Frames Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-17 Source details The International System of Units (SI Brochure), 9th edition, version 4.01 Bureau International des Poids et Mesures
Used here for The scoped roles of quantity values, numbers, units, dimensions, and angle-unit context when expressing and interpreting numerical values.
Source use Paraphrased from source
Source type official BIPM publication and authoritative SI reference; ninth edition published 2019; version 4.01 International Vocabulary of Metrology — Basic and General Concepts and Associated Terms (VIM), 3rd edition, JCGM 200:2012 Joint Committee for Guides in Metrology
Used here for Scoped distinctions among quantity, quantity value, numerical value, measurement unit, dimension, quantity-value scale, conversion, and measurement-uncertainty roles.
Source use Paraphrased from source
Source type official international metrology vocabulary; third edition; corrected 2012 publication OGC Abstract Specification Topic 2: Referencing by coordinates (Including corrigendum 1 and corrigendum 2), OGC 18-005r8, version 6.0.8 Open Geospatial Consortium
Used here for Within its OGC and ISO 19111 scope, distinctions among coordinate systems, coordinate reference systems, datums and reference frames, coordinate operations, coordinate conversions, coordinate transformations, dynamic reference frames, coordinate epochs, and relevant frame-reference, datum-anchor, transformation-reference, and parameter-reference epoch roles.
Source use Paraphrased from source
Source type official OGC publicly available standard and Abstract Specification; exact document 18-005r8, version 6.0.8 Open full topic Mathematics and Logic · Systems and Thinking
Vectors and Coordinate Frames A vector can be represented by ordered components that describe a quantity such as position, direction, or velocity. Those components become interpretable only with the coordinate frame and representation that give them an origin, axes, orientation, units, and applicable time where needed. The same components can describe different physical or geometric meanings in different frames.
Scope note This explains a representation boundary. It does not choose the correct frame, perform a transformation, validate a measurement, or prove that a vector describes the real world accurately.
Connections
2 reviewed connections
Follow an immediate reviewed neighbour, or open the explanation
underneath to see why the link exists and where it stops.
Selected concept Vectors and Coordinate Frames
Connects to Kinematics and Motion Connects to Units, Frames, and Uncertainty Relationship details
Connects to Kinematics and Motion Kinematic descriptions can use vectors and coordinate frames to express position, direction, orientation, velocity, or other motion-related quantities.
Connection boundary A coordinate description does not by itself provide dynamics, physical feasibility, intent, meaning, or an accepted motion plan.
Open Kinematics and Motion Connects to Units, Frames, and Uncertainty Vector components need units, coordinate representation, frame, and applicable time where relevant before their values can be interpreted or compared.
Connection boundary Naming that context does not validate the vector, select the correct transformation, or establish measurement accuracy.
Open Units, Frames, and Uncertainty Sources & review
Review state Human reviewed
AI assistance Yes—human reviewed
Last reviewed 2026-08-23 Source details The International System of Units (SI Brochure), 9th edition, version 4.01 Bureau International des Poids et Mesures
Used here for The scoped roles of quantity values, numbers, units, dimensions, and angle-unit context when expressing and interpreting numerical values.
Source use Paraphrased from source
Source type official BIPM publication and authoritative SI reference; ninth edition published 2019; version 4.01 International Vocabulary of Metrology — Basic and General Concepts and Associated Terms (VIM), 3rd edition, JCGM 200:2012 Joint Committee for Guides in Metrology
Used here for Scoped distinctions among quantity, quantity value, numerical value, measurement unit, dimension, quantity-value scale, conversion, and measurement-uncertainty roles.
Source use Paraphrased from source
Source type official international metrology vocabulary; third edition; corrected 2012 publication OGC Abstract Specification Topic 2: Referencing by coordinates (Including corrigendum 1 and corrigendum 2), OGC 18-005r8, version 6.0.8 Open Geospatial Consortium
Used here for Within its OGC and ISO 19111 scope, distinctions among coordinate systems, coordinate reference systems, datums and reference frames, coordinate operations, coordinate conversions, coordinate transformations, dynamic reference frames, coordinate epochs, and relevant frame-reference, datum-anchor, transformation-reference, and parameter-reference epoch roles.
Source use Paraphrased from source
Source type official OGC publicly available standard and Abstract Specification; exact document 18-005r8, version 6.0.8 Open full topic