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La mesure de la profondeur de la file d'attente est une valeur opérationnelle mesurée utilisée pour observer la profondeur de la file d'attente dans l'infrastructure PlatPhorm News. Il aide les opérateurs à vérifier que les listes d'articles, les flux, les routes API et les services de graphique réseau sont disponibles, frais et sains.

La sonde de profondeur de la file d'attente est un contrôle de santé automatisé utilisé pour observer la profondeur de la file d'attente sur l'infrastructure PlatPhorm News. Il aide les opérateurs à vérifier que les listes d'articles, les flux, les routes API et les services de graphique réseau sont disponibles, frais et sains.

Brouillon de traduction automatique (French) for "Queue Image Hardening": Queue Image Hardening is a compute security practice that reduces risk inside packaged runtime images for asynchronous work buffer. It uses minimal bases, patching, and vulnerability checks so teams can ship safer workloads while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The platform engineering team used Queue Image Hardening when the queue depth increased, so the team could ship safer workloads before the workload scaled up.”

Brouillon de traduction automatique (French) for "Queue Isolation Boundary": Queue Isolation Boundary is a compute security boundary that separates workloads so one cannot affect another unexpectedly for asynchronous work buffer. It uses namespaces, sandboxes, and access controls so teams can reduce cross-workload risk while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The platform engineering team used Queue Isolation Boundary when the queue depth increased, so the team could reduce cross-workload risk before the workload scaled up.”

Brouillon de traduction automatique (French) for "Queue Placement Strategy": Queue Placement Strategy is a compute scheduling rule that chooses where workloads should run for asynchronous work buffer. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The platform engineering team used Queue Placement Strategy when the queue depth increased, so the team could improve reliability and efficiency before the workload scaled up.”

Brouillon de traduction automatique (French) for "Queue Resource Quota": Queue Resource Quota is a compute limit that sets how much compute a workload may consume for asynchronous work buffer. It uses policy, reservations, and usage tracking so teams can protect shared capacity while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The platform engineering team used Queue Resource Quota when the queue depth increased, so the team could protect shared capacity before the workload scaled up.”

Brouillon de traduction automatique (French) for "Queue Workload Priority": Queue Workload Priority is a compute scheduling signal that tells the platform which work matters most when capacity is constrained for asynchronous work buffer. It uses priority classes, preemption rules, and fairness limits so teams can protect critical paths while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The platform engineering team used Queue Workload Priority when the queue depth increased, so the team could protect critical paths before the workload scaled up.”

Brouillon de traduction automatique (French) for "RAG Agent Trace": RAG Agent Trace is a ai observability record that captures the steps an AI workflow took for retrieval-augmented generation pipelines. It uses trace identifiers, tool events, and redacted metadata so teams can debug agent behavior without exposing secrets while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Agent Trace when the retriever mixed old and new documents, so the team could debug agent behavior without exposing secrets before the agent workflow reached production.”

Brouillon de traduction automatique (French) for "RAG Citation Builder": RAG Citation Builder is a ai attribution helper that formats source links and evidence for an AI answer for retrieval-augmented generation pipelines. It uses canonical URLs, source titles, and quote limits so teams can make generated answers citeable while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Citation Builder when the retriever mixed old and new documents, so the team could make generated answers citeable before the agent workflow reached production.”

La capacité contextuelle du RAG est une fonction d'agent déclarée utilisée par les agents d'IA et les clients du MCP lorsqu'ils travaillent avec PlatPhorm News. Il fournit un contexte structuré pour qu'un agent puisse découvrir des outils, inspecter des listes d'articles, lire des ressources ou effectuer des actions de plateforme sûres.

Brouillon de traduction automatique (French) for "RAG Context Contract": RAG Context Contract is a ai interface contract that defines what context may be passed into a model call for retrieval-augmented generation pipelines. It uses schemas, redaction rules, source labels, and token budgets so teams can keep model inputs relevant and safe while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Context Contract when the retriever mixed old and new documents, so the team could keep model inputs relevant and safe before the agent workflow reached production.”

Le RAG Context Prompt est un modèle d'instruction utilisé par les agents d'IA et les clients de MCP lorsqu'ils travaillent avec PlatPhorm News. Il fournit un contexte structuré pour qu'un agent puisse découvrir des outils, inspecter des listes d'articles, lire des ressources ou effectuer des actions de plateforme sûres.

La ressource contextuelle du RAG est une ressource lisible du MCP utilisée par les agents d'IA et les clients du MCP lorsqu'ils travaillent avec PlatPhorm News. Il fournit un contexte structuré pour qu'un agent puisse découvrir des outils, inspecter des listes d'articles, lire des ressources ou effectuer des actions de plateforme sûres.

Le RAG Context Run est une instance d'exécution utilisée par les agents d'IA et les clients MCP lorsqu'ils travaillent avec PlatPhorm News. Il fournit un contexte structuré pour qu'un agent puisse découvrir des outils, inspecter des listes d'articles, lire des ressources ou effectuer des actions de plateforme sûres.

L'outil contextuel RAG est une fonction d'agent callable utilisée par les agents d'IA et les clients de MCP lorsqu'ils travaillent avec PlatPhorm News. Il fournit un contexte structuré pour qu'un agent puisse découvrir des outils, inspecter des listes d'articles, lire des ressources ou effectuer des actions de plateforme sûres.

Brouillon de traduction automatique (French) for "RAG Fallback Path": RAG Fallback Path is a ai resilience pattern that keeps an AI feature useful when a provider or tool is unavailable for retrieval-augmented generation pipelines. It uses degraded states, deterministic responses, and operator notices so teams can avoid fake AI success while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Fallback Path when the retriever mixed old and new documents, so the team could avoid fake AI success before the agent workflow reached production.”

Brouillon de traduction automatique (French) for "RAG Grounding Check": RAG Grounding Check is a ai quality control that verifies that generated answers are backed by available sources for retrieval-augmented generation pipelines. It uses citation checks, retrieval evidence, and contradiction detection so teams can reduce unsupported claims while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Grounding Check when the retriever mixed old and new documents, so the team could reduce unsupported claims before the agent workflow reached production.”

Brouillon de traduction automatique (French) for "RAG Human Approval": RAG Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for retrieval-augmented generation pipelines. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Human Approval when the retriever mixed old and new documents, so the team could keep protected decisions accountable before the agent workflow reached production.”

Brouillon de traduction automatique (French) for "RAG Instruction Boundary": RAG Instruction Boundary is a ai policy boundary that separates durable system instructions from user-provided content for retrieval-augmented generation pipelines. It uses role labels, precedence rules, and prompt assembly checks so teams can avoid instruction confusion while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Instruction Boundary when the retriever mixed old and new documents, so the team could avoid instruction confusion before the agent workflow reached production.”

Brouillon de traduction automatique (French) for "RAG Memory Scope": RAG Memory Scope is a ai state boundary that limits what an assistant may remember or reuse for retrieval-augmented generation pipelines. It uses retention policies, consent checks, and namespace separation so teams can prevent accidental cross-context leakage while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemple en brouillon: The AI platform team used RAG Memory Scope when the retriever mixed old and new documents, so the team could prevent accidental cross-context leakage before the agent workflow reached production.”