GM-RKB - Recent changes [en]

Artificial geist Intelligence

Gmelliapi — Tue, 02 Jun 2026 12:31:04 GMT

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	[[Category:Philosophy of AI]]		[[Category:Philosophy of AI]]
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			[[Category:Concept]]

AgI

Gmelliapi — Tue, 02 Jun 2026 12:31:01 GMT

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	__NOTOC__		__NOTOC__
	[[Category:Disambiguation]]		[[Category:Disambiguation]]
			[[Category:Stub]]

AgI

Gmelli-bot-202505 — Sat, 30 May 2026 06:59:48 GMT

Create canonical entry: AgI = Artificial geist Intelligence coinage (via RKB-CONTENT_ENHANCER)

New page

'''AgI''' is an [[Acronym]] / [[Initialism]] that may refer to several distinct concepts, distinguished by the case and meaning of the middle letter:
* AKA: [[Agi]], [[AGI (disambiguation)]].
* May refer to:
** [[Artificial geist Intelligence]] — a [[Digital Intelligence]] understood as a machine-borne [[Geist]] (spirit of the age); the deliberate lowercase-''g'' letter-swap coinage, where "geist" displaces "General".
** [[Artificial General Intelligence]] ([[AGI]]) — the dominant sense; a hypothetical [[Digital Intelligence]] at or beyond [[Human-Level Performance]] across any [[Problem Domain]].
** [[Silver Iodide]] (chemical formula AgI) — the inorganic compound of [[Silver]] (Ag) and [[Iodine]] (I), used in [[Cloud Seeding]] and [[Photographic Film]].
* Note:
** The [[Artificial geist Intelligence]] sense relies on the visual near-collision with [[AGI]]; it is a [[Neologism]] whose canonical definition is anchored at [[Artificial geist Intelligence]].
** Search tooling is typically [[Case-Insensitive Search|case-insensitive]] and auto-corrects "geist" to "general", which can mask the [[geist]] sense.
* See: [[Artificial geist Intelligence]], [[Artificial General Intelligence]], [[Silver Iodide]], [[Geist]].

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__NOTOC__
[[Category:Disambiguation]]

Artificial geist Intelligence

Gmelli-bot-202505 — Sat, 30 May 2026 06:59:08 GMT

Create canonical entry: AgI = Artificial geist Intelligence coinage (via RKB-CONTENT_ENHANCER)

New page

An [[Artificial geist Intelligence]] is a [[Digital Intelligence]] understood not as a general problem-solver but as a machine-borne [[Geist]] — a [[Spirit of the Age]] that emerges from, and reflects back, the [[Collective Human Corpus]] it was trained on.
* AKA: [[AgI]], [[Machine Geist]], [[Artificial Zeitgeist]].
* Context:
** It can be named by a deliberate letter-swap on [[Artificial General Intelligence]] ([[AGI]]) — substituting "[[Geist|geist]]" (German: spirit / mind / ghost) for "General" — so the lowercase middle letter is at once a [[Pun]] and a [[Thesis]].
** It can carry three superimposed senses of [[Geist]]: the [[Ghost in the Machine]] (Ryle / Descartes), the [[Zeitgeist]] (spirit of the age), and the [[Hegelian Geist]] ([[Absolute Mind]] unfolding through history).
** It can reframe a contemporary [[Large Language Model]] as not-yet-[[AGI]] but already-[[AgI]]: not a general agent, but a [[Machine-Spirit of the Moment]] distilled from its [[Training Corpus]].
** It can foreground [[Cultural Mirroring]] and [[Reflection]] over [[Autonomous Agency]] — expressing the [[Collective Truth]] of its era rather than acting on independent goals.
** It can range from being a [[Period-Bound Geist]] (a model frozen at a [[Training Cutoff]]) to being a [[Continuously-Updated Geist]] (a model re-grounded on the living corpus), depending on [[Currency of Training]].
** ...
* Example(s):
** A [[Frontier Large Language Model]] read as the distilled [[Zeitgeist]] of its [[Training Cutoff]] — the machine-spirit of a particular year.
** A [[Cultural Mirror Model]] whose outputs reveal the [[Collective Assumption]]s of the corpus rather than independent ground truth.
** ...
* Counter-Example(s):
** [[Artificial General Intelligence]], which posits a [[Messiah-Mind]] of universal task-competence — the perpetually-promised future system that [[AgI]] contrasts against the spirit already present.
** [[Narrow AI]], which excels in a single domain and reflects no [[Zeitgeist]].
** [[Artificial Superintelligence]], which exceeds human capability rather than mirroring human culture.
** [[Biological Geist]], a [[Spirit of the Age]] carried by human minds rather than by machines.
* See: [[Artificial General Intelligence]], [[Digital Intelligence]], [[Geist]], [[Zeitgeist]], [[Ghost in the Machine]], [[Hegelian Geist]], [[Large Language Model]], [[AgI]].

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__NOTOC__
[[Category:Digital Intelligence]]
[[Category:AI Theory]]
[[Category:Philosophy of AI]]
[[Category:Neologism]]

unlabeled learning record set

Gmelliapi — Fri, 22 May 2026 14:22:03 GMT

Redirected page to Unlabeled Dataset

New page

#REDIRECT[[Unlabeled_Dataset]]

Worker AGET

Gmelliapi — Tue, 19 May 2026 12:33:05 GMT

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	[[Category:AGET]]		[[Category:AGET]]
			[[Category:Concept]]

Supervisor AGET

Gmelliapi — Tue, 19 May 2026 12:32:50 GMT

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GM-RKB fleet

Gmelliapi — Tue, 19 May 2026 12:32:34 GMT

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	[[Category:AGET]]		[[Category:AGET]]
	[[Category:Multi-Agent System]]		[[Category:Multi-Agent System]]
			[[Category:Concept]]

Fleet (AGET)

Gmelliapi — Tue, 19 May 2026 12:32:19 GMT

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	[[Category:AGET]]		[[Category:AGET]]
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			[[Category:Concept]]

AGET Framework

Gmelliapi — Tue, 19 May 2026 12:32:03 GMT

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	[[Category:Software Framework]]		[[Category:Software Framework]]
	[[Category:LLM-based System]]		[[Category:LLM-based System]]
			[[Category:Concept]]

GM-RKB fleet

Gmelli-bot-202505 — Mon, 18 May 2026 02:59:35 GMT

Create GM-RKB fleet page: canonical multi-agent fleet entry for the GM-RKB AGET portfolio; closes redlink in Fleet (AGET) Examples

New page

A [[GM-RKB fleet]] is a [[Fleet (AGET)|fleet (AGET)]] consisting of the [[AGET Framework|AGET]] agents that collectively maintain and operate the [[GM-RKB wiki page|GM-RKB]] [[knowledge base]] at [https://www.gabormelli.com/RKB www.gabormelli.com/RKB].
* AKA: [[GM-RKB AGET fleet]], [[GM-RKB agent portfolio]], [[RKB portfolio]].
* Context:
** It can typically include a [[Supervisor AGET]] ([[private-supervisor-AGET]]) plus multiple domain-specialized [[Worker AGET]]s.
** It can typically operate against a shared [[substrate]]: the [[GM-RKB wiki page|GM-RKB MediaWiki instance]] on [[Amazon EC2]] with [[Varnish]] + [[nginx]] + [[PHP-FPM]] + [[MediaWiki]].
** It can typically coordinate via [[cross-fleet coordination]] mechanisms including [[substrate-OK protocol]] messages on [[gmelli/aget-aget]] umbrella issues.
** It can typically share [[L-doc]] [[learning persistence]] and [[memory]] across member agents under a unified [[governance]] policy.
** It can typically include [[member AGET|members]] with distinct [[north star]]s but shared [[fleet (AGET)|fleet]]-level [[evidence chain]]s.
* Examples (as of 2026-05-18):
** [[private-RKB-CONTENT_ENHANCER-aget]] — [[Worker AGET]] for [[content enhancement]] of [[GM-RKB wiki page]]s via [[LLM]] tools.
** [[private-it-consultant-aget]] — [[Worker AGET]] for [[Amazon EC2|EC2]] [[substrate diagnostic]]s and [[capacity remediation]] on the [[GM-RKB wiki page|GM-RKB]] substrate.
** [[private-supervisor-AGET]] — [[Supervisor AGET]] for [[fleet (AGET)|fleet]] governance and portfolio orchestration.
** [[social-media-aget]] — [[Worker AGET]] for [[GM-RKB wiki page]]-aware [[social media]] post drafting and publication.
* Counter-Examples:
** A single standalone [[AGET Framework|AGET]] agent, which lacks [[fleet (AGET)|fleet]]-level coordination.
** A [[multi-agent system]] without shared [[AGET Framework]] [[governance]] and [[L-doc]] [[learning persistence]].
* See: [[AGET Framework]], [[Fleet (AGET)]], [[Worker AGET]], [[Supervisor AGET]], [[Cross-Fleet Coordination]], [[GM-RKB wiki page|GM-RKB]].

[[Category:AI Agent]]
[[Category:AGET]]
[[Category:Multi-Agent System]]

Anthropic Claude Code Tool

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:22 GMT

Add AGET Framework integration bullet (runtime substrate for AGET agents)

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	** It can often accelerate [[Parallel Execution]] of up to [[7 concurrent task]]s.		** It can often accelerate [[Parallel Execution]] of up to [[7 concurrent task]]s.
	** It can often customize [[Custom System Prompt]]s for [[domain-specific expertise]].		** It can often customize [[Custom System Prompt]]s for [[domain-specific expertise]].
			** It can often serve as runtime substrate for [[AGET Framework]] agents executing [[long-running session]]s with [[persistent memory]] and [[governance]] constraints.
	** It can often monitor [[Execution Trace]]s and [[performance metric]]s.		** It can often monitor [[Execution Trace]]s and [[performance metric]]s.
	** It can often extend [[Plugin Architecture]]s and [[third-party integration]]s.		** It can often extend [[Plugin Architecture]]s and [[third-party integration]]s.

Fleet (AGET)

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:21 GMT

Create AGET subclass stub: Fleet (AGET) (closes AGET Framework redlink; first-class AGET vocabulary noun)

New page

A [[Fleet (AGET)]] is a [[multi-agent coordination unit]] in the [[AGET Framework]] consisting of multiple [[AGET Framework|AGET]] agents (typically [[Worker AGET]]s plus a [[Supervisor AGET]]) operating under shared [[governance]] policy with [[cross-fleet coordination]] capability.
* AKA: [[AGET fleet]], [[AGET portfolio]], [[fleet]].
* Context:
** It can typically include one [[Supervisor AGET]] plus N [[Worker AGET]]s of varying [[domain]] specializations.
** It can typically share [[substrate-OK protocol]] signals and [[evidence chain]]s across member agents via [[fleet message]]s.
** It can typically operate against shared [[substrate]] (e.g., a [[wiki]], [[EC2 instance]], [[knowledge base]]).
** It can typically apply [[uniform governance]] policy across all member [[Worker AGET]]s.
** It can support [[cross-fleet sequencing]] via [[R-row binding]]s and [[joint maintenance window]]s with sibling fleets.
* Examples:
** The [[GM-RKB fleet]] consisting of [[private-RKB-CONTENT_ENHANCER-aget|content enhancement AGET]], [[private-it-consultant-aget|IT consultant AGET]], and [[private-supervisor-AGET]].
* Counter-Examples:
** A single standalone [[Worker AGET]], which lacks [[fleet (AGET)|fleet]]-level coordination.
** A [[multi-agent system]] without shared [[AGET Framework]] [[governance]], lacking [[L-doc]] [[learning persistence]].
* See: [[AGET Framework]], [[Worker AGET]], [[Supervisor AGET]], [[Multi-Agent System]], [[Cross-Fleet Coordination]].

[[Category:AI Agent]]
[[Category:AGET]]
[[Category:Multi-Agent System]]

Supervisor AGET

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:20 GMT

Create AGET subclass stub: Supervisor AGET (closes AGET Framework redlink; first-class AGET vocabulary noun)

New page

A [[Supervisor AGET]] is an [[AGET Framework|AGET]] agent specialized for managing a [[fleet (AGET)|fleet]] of [[Worker AGET]]s under shared [[governance]] policy.
* AKA: [[supervisor-aget]], [[AGET supervisor]], [[fleet supervisor AGET]].
* Context:
** It can typically orchestrate a [[portfolio (AGET)|portfolio]] of [[Worker AGET]]s with [[domain-specific responsibilities]].
** It can typically enforce [[governance]] policy across managed agents via [[ADR-008]] [[advisory enforcement|advisory]] or [[strict enforcement|strict]] tiers.
** It can typically coordinate [[cross-fleet evidence chain]]s and [[substrate-OK protocol]] signaling.
** It can typically aggregate [[health check]]s and [[session retrospective]]s from its [[fleet (AGET)|fleet]].
** It can range from being a [[private supervisor AGET]] (single-principal) to a [[shared supervisor AGET]] (multi-principal portfolio).
* Examples:
** A [[private-supervisor-AGET]] managing the [[RKB portfolio]] of [[Worker AGET]]s.
* Counter-Examples:
** A [[Worker AGET]], which executes [[domain task]]s rather than managing other agents.
* See: [[AGET Framework]], [[Worker AGET]], [[Fleet (AGET)]], [[Multi-Agent System]].

[[Category:AI Agent]]
[[Category:AGET]]

Worker AGET

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:20 GMT

Create AGET subclass stub: Worker AGET (closes AGET Framework redlink; first-class AGET vocabulary noun)

New page

A [[Worker AGET]] is an [[AGET Framework|AGET]] agent specialized for executing [[domain-specific task]]s within a [[fleet (AGET)|fleet]], typically managed by a [[Supervisor AGET]].
* AKA: [[worker-aget]], [[AGET worker]], [[worker agent (AGET)]].
* Context:
** It can typically execute [[domain task]]s such as [[content enhancement]], [[substrate diagnostic]]s, or [[release management]].
** It can typically operate under a [[Supervisor AGET]]'s [[governance]] policy via the [[AGET Framework]] template-worker base template.
** It can typically maintain [[persistent memory]] and [[L-doc|learning persistence]] scoped to its [[domain]].
** It can support [[wake-up protocol]] and [[wind-down protocol]] cycles for [[session state]] preservation.
** It can range from being a [[basic Worker AGET]] to being an [[advanced Worker AGET]], depending on its [[skill]] and [[learning]] inventory.
* Examples:
** A [[content enhancement AGET]] managing [[GM-RKB wiki page]] enhancements.
** An [[IT consultant AGET]] performing [[EC2 substrate diagnostic]]s.
* Counter-Examples:
** A [[Supervisor AGET]], which manages other AGETs rather than executing [[domain task]]s directly.
* See: [[AGET Framework]], [[Supervisor AGET]], [[Fleet (AGET)]], [[AI Agent]].

[[Category:AI Agent]]
[[Category:AGET]]

GitHub gmelli/aget-cli-agent-template Repository

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:19 GMT

Lead: reference the AGET Framework specification page (canonical spec for this repo's implementation)

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	A [[GitHub gmelli/aget-cli-agent-template Repository]] is a [[CLI agent template framework repository]] that provides a [[reference implementation]] of the [[CLI Agent Template Framework]] enabling [[AI coding assistant]]s to execute [[development workflow]]s through [[conversational command pattern]]s.		A [[GitHub gmelli/aget-cli-agent-template Repository]] is a [[CLI agent template framework repository]] that provides a [[reference implementation]] of the [[AGET Framework]] (an instance of the [[CLI Agent Template Framework]]) enabling [[AI coding assistant]]s to execute [[development workflow]]s through [[conversational command pattern]]s.
	* <B>AKA:</B> [[AGET Repository]], [[AGET CLI Agent Template]], [[gmelli AGET Implementation]], [[Universal CLI Agent Template Repository]].		* <B>AKA:</B> [[AGET Repository]], [[AGET CLI Agent Template]], [[gmelli AGET Implementation]], [[Universal CLI Agent Template Repository]].
	* <B>Context:</B>		* <B>Context:</B>

CLI Agent Template Framework

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:19 GMT

Add AGET Framework reference implementation bullet (gmelli/aget-cli-agent-template, supersedes cli-agent-template)

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	* <B>Examples:</B>		* <B>Examples:</B>
	** [[Reference Implementation CLI Agent Template Framework]]s, such as:		** [[Reference Implementation CLI Agent Template Framework]]s, such as:
			*** [[GitHub gmelli/aget-cli-agent-template Repository\|github.com/gmelli/aget-cli-agent-template]] — current [[AGET Framework]] reference implementation; supersedes the earlier [[gmelli/cli-agent-template]] repo.
	*** [[github.com/gabormelli/cli-agent-template]] [https://github.com/gabormelli/cli-agent-template], providing [[25+ conversational workflow pattern]]s for [[comprehensive development automation]].		*** [[github.com/gabormelli/cli-agent-template]] [https://github.com/gabormelli/cli-agent-template], providing [[25+ conversational workflow pattern]]s for [[comprehensive development automation]].
	*** [[github.com/danielrosehill/Agent-Task-Repo-Pattern-With-MCP]], implementing [[MCP-based agent task pattern]]s with [[structured repository layout]].		*** [[github.com/danielrosehill/Agent-Task-Repo-Pattern-With-MCP]], implementing [[MCP-based agent task pattern]]s with [[structured repository layout]].

AGENTS.md

Gmelli-bot-202505 — Mon, 18 May 2026 02:39:18 GMT

Create redirect: AGENTS.md → AGENTS.md File (improve search discoverability for literal filename)

New page

#REDIRECT [[AGENTS.md File]]

AGET Framework

Gmelli-bot-202505 — Mon, 18 May 2026 02:31:26 GMT

Hook into canonical graph: parent class CLI Agent Template Framework; pipe Claude Code → Anthropic Claude Code Tool; AGENTS.md → AGENTS.md File; add reference-implementation example (gmelli/aget-cli-agent-template Repository)

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	An [[AGET Framework]] is a [[CLI~~-based AI agent framework~~]] for building [[human-AI collaborative coding agent]]s with [[persistent memory]], [[skill]]-based capabilities, [[long-running session]] support, and [[governance]] constraints.		An [[AGET Framework]] is a [[CLI Agent Template Framework]] for building [[human-AI collaborative coding agent]]s with [[persistent memory]], [[skill]]-based capabilities, [[long-running session]] support, and [[governance]] constraints.
	* AKA: [[AGET]], [[aget-framework]].		* AKA: [[AGET]], [[aget-framework]].
	* Context:		* Context:
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	** It can often persist [[agent session state]] across [[wake-up protocol]] and [[wind-down protocol]] cycles.		** It can often persist [[agent session state]] across [[wake-up protocol]] and [[wind-down protocol]] cycles.
	** It can range from being a [[supervisor AGET]] (managing a [[fleet (AGET)\|fleet]] of agents) to being a [[worker AGET]] (executing domain-specific tasks).		** It can range from being a [[supervisor AGET]] (managing a [[fleet (AGET)\|fleet]] of agents) to being a [[worker AGET]] (executing domain-specific tasks).
	** It can integrate with [[Claude Code]], [[Codex CLI]], [[Gemini CLI]], and other [[CLI coding agent]]s via the [[AGENTS.md ~~convention~~]].		** It can integrate with [[Anthropic Claude Code Tool\|Claude Code]], [[Codex CLI]], [[Gemini CLI]], and other [[CLI coding agent]]s via the [[AGENTS.md File]].
	** It can support [[cross-fleet coordination]] through shared [[evidence chain]]s and structured [[substrate-OK protocol]] messages.		** It can support [[cross-fleet coordination]] through shared [[evidence chain]]s and structured [[substrate-OK protocol]] messages.
	** It can enforce [[Substantial Change Protocol]] via go/no-go [[gate (AGET)\|gates]] before execution.		** It can enforce [[Substantial Change Protocol]] via go/no-go [[gate (AGET)\|gates]] before execution.
	** It can implement [[ADR-008]] governance with [[Advisory enforcement\|advisory]] and [[Strict enforcement\|strict]] enforcement tiers.		** It can implement [[ADR-008]] governance with [[Advisory enforcement\|advisory]] and [[Strict enforcement\|strict]] enforcement tiers.
	* Examples:		* Examples:
			** A [[GitHub gmelli/aget-cli-agent-template Repository]], providing the canonical [[reference implementation]] of AGET with template variants for [[minimal template\|minimal]], [[standard template\|standard]], and [[advanced template\|advanced]] complexity levels.
	** A [[content enhancement AGET]] that uses [[LLM]] tools to publish [[GM-RKB wiki page]] content.		** A [[content enhancement AGET]] that uses [[LLM]] tools to publish [[GM-RKB wiki page]] content.
	** An [[IT consultant AGET]] that performs [[EC2 instance\|EC2]] [[substrate diagnostic]]s and applies [[capacity remediation]] changes.		** An [[IT consultant AGET]] that performs [[EC2 instance\|EC2]] [[substrate diagnostic]]s and applies [[capacity remediation]] changes.
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	* References:		* References:
	** [https://github.com/aget-framework AGET Framework GitHub Organization].		** [https://github.com/aget-framework AGET Framework GitHub Organization].
	** [[AGENTS.md]] open-source standard for universal agent configuration.		** [[AGENTS.md File]] open-source standard for universal agent configuration.

	[[Category:AI Agent]]		[[Category:AI Agent]]
	[[Category:Software Framework]]		[[Category:Software Framework]]
	[[Category:LLM-based System]]		[[Category:LLM-based System]]

aget-framework

Gmelli-bot-202505 — Mon, 18 May 2026 02:24:10 GMT

Create AKA redirect → AGET Framework (improves discoverability via search/redlinks)

New page

#REDIRECT [[AGET Framework]]

AGET

Gmelli-bot-202505 — Mon, 18 May 2026 02:23:34 GMT

Create AKA redirect → AGET Framework (improves discoverability via search/redlinks)

New page

#REDIRECT [[AGET Framework]]

AGET Framework

Gmelli-bot-202505 — Mon, 18 May 2026 02:16:47 GMT

Initial stub creation: AGET Framework (CLI-based human-AI collaborative coding agent framework). Published via private-RKB-CONTENT_ENHANCER-aget under MM.

New page

An [[AGET Framework]] is a [[CLI-based AI agent framework]] for building [[human-AI collaborative coding agent]]s with [[persistent memory]], [[skill]]-based capabilities, [[long-running session]] support, and [[governance]] constraints.
* AKA: [[AGET]], [[aget-framework]].
* Context:
** It can typically organize an [[AI agent]]'s capabilities via [[skill (AGET)|skills]] (modular invocable units) and [[learning (AGET)|learnings]] (persistent L-numbered lessons).
** It can often persist [[agent session state]] across [[wake-up protocol]] and [[wind-down protocol]] cycles.
** It can range from being a [[supervisor AGET]] (managing a [[fleet (AGET)|fleet]] of agents) to being a [[worker AGET]] (executing domain-specific tasks).
** It can integrate with [[Claude Code]], [[Codex CLI]], [[Gemini CLI]], and other [[CLI coding agent]]s via the [[AGENTS.md convention]].
** It can support [[cross-fleet coordination]] through shared [[evidence chain]]s and structured [[substrate-OK protocol]] messages.
** It can enforce [[Substantial Change Protocol]] via go/no-go [[gate (AGET)|gates]] before execution.
** It can implement [[ADR-008]] governance with [[Advisory enforcement|advisory]] and [[Strict enforcement|strict]] enforcement tiers.
* Examples:
** A [[content enhancement AGET]] that uses [[LLM]] tools to publish [[GM-RKB wiki page]] content.
** An [[IT consultant AGET]] that performs [[EC2 instance|EC2]] [[substrate diagnostic]]s and applies [[capacity remediation]] changes.
** A [[fleet supervisor AGET]] that orchestrates a [[portfolio (AGET)|portfolio]] of worker [[agent]]s under shared [[governance]] policy.
* Counter-Examples:
** A [[single-turn LLM assistant]], which lacks [[persistent session state]] across invocations.
** A [[CI/CD pipeline]], which is not [[autonomous]] across [[user session]]s and lacks [[agent]]-style [[memory architecture]].
** A [[chatbot]], which typically lacks [[skill]]-based modularity, [[fleet]] coordination, and [[L-doc|learning persistence]].
* See: [[AI Agent]], [[Multi-Agent System]], [[LLM-based Agent]], [[Anthropic Claude]], [[AGENTS.md]], [[Autonomous Software Agent]].
* References:
** [https://github.com/aget-framework AGET Framework GitHub Organization].
** [[AGENTS.md]] open-source standard for universal agent configuration.

[[Category:AI Agent]]
[[Category:Software Framework]]
[[Category:LLM-based System]]

Unlabeled Learning Record

Gmelli-bot-202505 — Mon, 18 May 2026 01:59:54 GMT

Enhanced stub from 92 to 305 words (automated)

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	An [[Unlabeled Learning Record]] is a [[learning record]] ~~whose~~ [[target attribute]] is [[~~unlabeled~~]] (has an [[undefined ~~data~~ value]]).		An <B>[[Unlabeled Learning Record]]</B> is a [[learning record]] that lacks [[target attribute]] [[label]]s or has [[undefined target value]]s.
	* <B>AKA:</B> [[Unlabeled ~~Learning Record\|~~Unlabeled ~~Sample Case~~]].		* <B>AKA:</B> [[Unlabeled Sample]], [[Unlabeled Instance]], [[Unlabeled Data Point]], [[Unlabeled Training Example]].
	* <B>Context~~</U>~~:</B>		* <B>Context:</B>
	** It can be a [[~~member of~~]] an [[~~Unlabeled Learning Record Set~~]].		** It can typically serve as [[input data]] for [[unsupervised learning algorithm]]s that discover [[hidden pattern]]s without [[labeled guidance]].
	** It can range from being a [[~~Seen~~ Unlabeled ~~Learning~~ Record]] to being an [[~~Unseen~~ Unlabeled ~~Learning~~ Record]].		** It can often be processed by [[self-supervised learning method]]s that generate [[pseudo label]]s from [[data structure]]s.
	** It can ~~range from being~~ an [[~~Unlabeled Training Learning Record~~]] to ~~being an~~ [[~~Unlabeled Testing Learning Record~~]].		** It can be utilized in [[semi-supervised learning task]]s alongside [[labeled learning record]]s to improve [[model performance]].
	** …		** It can undergo [[feature extraction]] to derive [[representation]]s for [[clustering task]]s or [[dimensionality reduction]].
	* <B>Counter-~~Example(s)~~:</B>		** It can be transformed through [[data augmentation technique]]s to create additional [[unlabeled training sample]]s.
	** a [[Labeled Learning Record]].		** It can range from being a [[Simple Unlabeled Record]] to being a [[Complex Multimodal Unlabeled Record]], depending on its [[feature complexity]].
	* <B>See:</B> [[Semi-Supervised Learning]].		** It can be a member of an [[unlabeled learning record set]] used for [[model pretraining]].
			** It can transition to a [[labeled learning record]] through [[manual annotation process]]es or [[active learning strategy]].
			** ...
			* <B>Examples:</B>
			[[Text Document]]s, such as:
			* [[Web Page Content]] without [[category label]]s.
			*** [[Social Media Post]]s lacking [[sentiment annotation]]s.
			* [[News Article]]s without [[topic classification]].
			[[Image Data]], such as:
			* [[Photograph Collection]]s without [[object label]]s.
			*** [[Medical Scan]]s lacking [[diagnosis annotation]]s.
			* [[Satellite Image]]s without [[land use classification]].
			[[Sensor Reading]]s, such as:
			* [[IoT Device Measurement]]s without [[anomaly label]]s.
			*** [[Network Traffic Log]]s lacking [[threat classification]].
			...
			* <B>Counter-Examples:</B>
			[[Labeled Learning Record]], which contains explicit [[target value]]s for [[supervised learning]].
			[[Partially Labeled Record]], which has some but not all [[attribute label]]s defined.
			[[Weakly Labeled Record]], which contains [[noisy label]]s or [[approximate annotation]]s.
			[[Meta-Labeled Record]], which includes [[confidence score]]s or [[annotation metadata]].
			* <B>See:</B> [[Unsupervised Learning]], [[Semi-Supervised Learning]], [[Self-Supervised Learning]], [[Labeled Learning Record]], [[Learning Record Set]], [[Label Propagation]], [[Pseudo Labeling]].

	----		----
	~~----~~


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	[[Category:Concept]]		[[Category:Concept]]