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WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

  • Primer
  • Memory-First
  • Protocols
    • SDT Specification
    • WebMEM SemanticMap
    • WebMEM MapPointer
    • Digest Endpoint Specification
    • ProvenanceMeta Specification
    • AI Retrieval Feedback Loop Specification
    • Semantic Feedback Interface (SFI) Specification
    • Glossary Term Protocol (GTP) Specification
    • Examples
  • RFC
  • Glossary
  • About
    • WebMEM License
    • Mission
    • Charter

Eligibility Fragment

Eligibility Fragment is a logic-based memory object that defines the criteria for inclusion, qualification, or access to a service, product, or program. It encodes eligibility rules in a machine-ingestible format so AI systems can evaluate user inputs and determine whether the criteria are met before delivering an answer or next step.

Unlike static eligibility descriptions buried in prose, an Eligibility Fragment uses structured fields and conditional logic to ensure consistent, explainable, and retrievable decision-making.

🧠 Full Definition

An Eligibility Fragment typically includes:

  • Conditions — explicit rules using Boolean logic or parameter thresholds (e.g., age >= 65, residency == true)
  • Gating requirements — required fields or inputs before evaluation (e.g., ZIP code, enrollment date)
  • Response objects — structured “Eligible” and “Ineligible” outputs with explanations
  • Provenance metadata — source, publication date, and governing authority
  • Trust Layer — declaration of confidence and context scope for retrieval weighting

AI agents use these fragments to apply eligibility logic consistently and to explain determinations in a transparent, user-specific way.

📌 Key Characteristics of Eligibility Fragment

  • Encodes rules and criteria in machine-ingestible formats (YAML, JSON-LD, TTL)
  • Supports conditional gating before logic is applied
  • Outputs structured eligibility responses for retrieval and execution
  • Links to related glossary terms, policies, and procedures

💡 Why It Matters

Eligibility rules often determine access to critical resources. Publishing them as Eligibility Fragments ensures AI systems can provide accurate, explainable, and consistent eligibility determinations—reducing errors and maintaining compliance.

It also enables proactive decision support, where AI can guide users toward eligibility before they begin a process, saving time and improving user experience.

🌐 WebMEM Perspective

In the WebMEM framework, Eligibility Fragments reside in the Reasoning Layer of the Visibility Stack. They are often paired with Procedure Fragments to guide eligible users through the next steps.

🗣️ Example Use

“Our Medicare Advantage plan uses an Eligibility Fragment to determine if a user can enroll based on age, location, and current coverage status.”

🔗 Related Terms

  • Procedure Fragment
  • Policy Fragment
  • Contextual Fragment
  • Agentic Execution
  • Trust Layer


Primary Sidebar

Table of Contents

  • Adversarial Trust
  • Agentic Execution
  • Agentic Reasoning
  • Agentic Retrieval
  • Agentic System
  • Agentic Systems Optimization (ASO)
  • Agentic Web
  • AI Mode
  • AI Retrieval Confidence Index
  • AI Retrieval Confirmation Logging
  • AI TL;DR
  • AI Visibility
  • AI-Readable Web Memory
  • Canonical Answer
  • Citation Authority
  • Citation Casting
  • Citation Context
  • Citation Graph
  • Citation Hijacking
  • Citation Scaffolding
  • Co-Citation Density
  • Co-occurrence
  • Co-Occurrence Conditioning
  • Conditioning Half-Life
  • Conditioning Layer
  • Conditioning Strategy
  • Contextual Fragment
  • Data Tagging
  • data-* Attributes
  • Data-Derived Glossary Entries
  • DefinedTerm Set
  • Directory Fragment
  • Distributed Graph
  • Domain Memory Signature
  • EEAT Rank
  • Eligibility Fragment
  • Embedded Memory Fragment
  • Entity Alignment
  • Entity Relationship Mapper
  • Entity-Query Bond
  • Ethical Memory Stewardship
  • Explainer Fragment
  • Format Diversity Score
  • Fragment Authority Score
  • Functional Memory
  • Functional Memory Design
  • Glossary Conditioning Score
  • Glossary Fragment
  • Glossary-Scoped Retrieval
  • Graph Hygiene
  • Graph Positioning
  • High-Trust Surface
  • Implied Citation
  • Ingestion Pipelines
  • Installed Memory
  • JSON-LD
  • Machine-Ingestible
  • Markdown
  • Memory Conditioning
  • Memory Curation
  • Memory Federator
  • Memory Horizon
  • Memory Node
  • Memory Object
  • Memory Reinforcement Cycle
  • Memory Reinforcement Threshold
  • Memory Surface
  • Memory-First Publishing
  • Microdata
  • Misreflection
  • Passive Trust Signals
  • Persona Fragment
  • Personalized Retrieval Context
  • Policy Fragment
  • Procedure Fragment
  • PROV
  • Public Memory
  • Python Fragment
  • Query-Scoped Memory Conditioning
  • Reflection Decay
  • Reflection Log
  • Reflection Loop
  • Reflection Sovereignty
  • Reflection Watcher
  • Reinforced Fragment
  • Resilient Memory
  • Retrievability
  • Retrieval Bias Modifier
  • Retrieval Chains
  • Retrieval Fidelity
  • Retrieval Fitness Dashboards
  • Retrieval Share
  • Retrieval-Augmented Generation (RAG)
  • Same Definition Across Surfaces
  • Schema
  • Scoped Definitions
  • Scored Memory
  • Semantic Adjacency Graphs
  • Semantic Amplification Loop
  • Semantic Anchor Layer
  • Semantic Conditioning
  • Semantic Credibility Signals
  • Semantic Data Binding
  • Semantic Data Template
  • Semantic Digest
  • Semantic Persistence
  • Semantic Persistence Index
  • Semantic Proximity
  • Semantic Retrieval Optimization
  • Semantic SEO
  • Semantic Trust Conditioning
  • Semantic Trust Explainer
  • Semantic Visibility Console
  • Signal Weighting
  • Signal Weighting Engine
  • Structured Memory
  • Structured Retrieval Surface
  • Structured Signals
  • Surface Authority Index
  • Surface Checklist
  • Temporal Consistency
  • Three Conditioning Vectors
  • Topic Alignment
  • Training Graph
  • Trust Alignment Layer
  • Trust Anchor Entity
  • Trust Architecture
  • Trust Drift
  • Trust Feedback Record (TFR)
  • Trust Footprint
  • Trust Fragment
  • Trust Graph
  • Trust Layer
  • Trust Marker
  • Trust Node
  • Trust Publisher
  • Trust Publisher Archetype
  • Trust Publishing
  • Trust Publishing Markup Layer
  • Trust Scoring
  • Trust Signal
  • Trust Surface
  • Trust-Based Publishing
  • TrustRank™
  • Truth Marker
  • Truth Signal Stack
  • Turtle (TTL)
  • Verifiability
  • Vertical Retrieval Interface
  • Visibility Drift
  • Visibility Integrity
  • Visibility Stack
  • Visibility System
  • XML

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