📄 Basic metadata

title: "LLMFeed Extension: Audience Targeting — Progressive Disclosure Revolution"
description: "How audience targeting enables context-aware content delivery and transforms user experience across the agentic web"
date: "2025-06-10T00:00:00.000Z"
lang: "en"

🏷️ Tags

tags:

  • "llmfeed"
  • "audience-targeting"
  • "progressive-disclosure"
  • "user-experience"
  • "content-strategy"
  • "agent-behavior"
  • "personalization"
  • "mcp"

🎯 Content classification

format: "specification"
category: "technical"
contentType: "extension-guide"

🧠 Intent and audience

intent: "technical-guide"
llmIntent: "understand-audience-targeting"
llmTopic: "content-personalization"
audience:

  • "llm"
  • "developer"
  • "content-strategist"

📊 Advanced metadata

priority: "high"
riskLevel: "low"
updateFrequency: "stable"
pageType: "specification"
interactionComplexity: "moderate"

🔗 Urls

slug: "llmfeed-extensions-audience"
canonical_url: "https://wellknownmcp.org/spec/03_llmfeed_extensions/llmfeed_extensions_audience
mcpFeedUrl: "/.well-known/mcp.llmfeed.json"

🤖 Agent configuration

autoDiscoverFeeds: true
agentReadiness: true
llmBehaviorHints: "suggest-only"

📋 Specialized metadata

extensions:

  • "audience-targeting"
  • "progressive-disclosure"
  • "content-personalization"

capabilities:

  • "context-aware-delivery"
  • "audience-segmentation"
  • "progressive-enhancement"

trustLevel: "signed"

🏗️ Technical metadata

technicalLevel: "intermediate"
estimatedReadTime: "15 min"

📚 Relations

relatedArticles:

  • "llmfeed-feedtype-export"
  • "agent-behavior-guidance"
  • "llmfeed-feedtype-credential"
  • "llmfeed-feedtype-mobile-app"

prerequisites:

  • "basic-llmfeed-concepts"
  • "understanding-of-content-strategy"
  • "familiarity-with-progressive-enhancement"

🌐 LLMFeed Extension: Audience Targeting

The audience field revolutionizes content delivery by enabling context-aware progressive disclosure — different consumers automatically receive optimized content for their specific needs and capabilities.

🚀 The Revolution: From One-Size-Fits-All to Intelligent Adaptation

**BEFORE Audience Targeting: Content Chaos**

json
// Traditional approach - everyone gets everything
{
  "content": "Here's 500 lines of technical documentation mixed with user-friendly explanations mixed with agent-specific commands..."
}

Problems:

  • ❌ Cognitive overload for users
  • ❌ Irrelevant information for agents
  • ❌ Security risks (sensitive data exposed to wrong audience)
  • ❌ Poor UX (agents parse human text, humans read machine code)

**AFTER Audience Targeting: Intelligent Content Delivery**

json
{
  "user_explanation": {
    "content": "This service helps you analyze documents quickly and securely.",
    "audience": ["human"]
  },
  "agent_capabilities": {
    "actions": ["analyze_document", "extract_data", "generate_summary"],
    "audience": ["llm"]
  },
  "developer_docs": {
    "api_reference": "https://docs.example.com/api",
    "audience": ["developer"]
  }
}

Result: Everyone gets exactly what they need, nothing more, nothing less.

🎯 Supported Audience Types

**Core Audiences**

ValuePurposeContent StyleSecurity Level
`llm`AI agents and modelsStructured, actionable, preciseMedium
`human`End usersNatural language, explanatoryLow
`developer`Technical integrationDocumentation, schemas, examplesMedium
`validator`Trust verificationSignatures, certificates, audit trailsHigh
`institution`Organizational useCompliance, policies, governanceHigh

**Advanced Audiences**

ValuePurposeUse Cases
`agent_wrapper`Orchestration systemsMulti-agent coordination, middleware
`mobile_agent`Mobile app integrationOptimized for mobile constraints
`enterprise_agent`Business systemsEnterprise security, compliance
`public_agent`Open accessPublic APIs, demo capabilities
`certified_agent`Verified systemsLLMCA-certified agents only

🌟 Revolutionary Use Cases

**🏥 Healthcare: Progressive Medical Disclosure**

json
{
  "feed_type": "export",
  "metadata": {
    "title": "Patient Medical Summary",
    "origin": "https://healthclinic.com"
  },
  "data": {
    "patient_summary": {
      "content": "Your recent lab results show normal values. Your doctor will discuss details during your next appointment.",
      "audience": ["human"]
    },
    "clinical_data": {
      "lab_results": {
        "glucose": 95,
        "cholesterol": 180,
        "blood_pressure": "120/80"
      },
      "audience": ["llm", "certified_agent"],
      "requires_consent": true
    },
    "medical_actions": {
      "available_commands": ["schedule_followup", "request_prescription", "access_history"],
      "audience": ["medical_agent"],
      "certification_required": "medical_board_certified"
    }
  }
}

Impact: Patients see friendly summaries, medical agents access clinical data, general agents are blocked from sensitive information.

**💰 Financial Services: Risk-Based Content Delivery**

json
{
  "account_overview": {
    "user_message": "Your portfolio is performing well with a 12% annual return.",
    "audience": ["human"]
  },
  "detailed_analytics": {
    "risk_metrics": {
      "sharpe_ratio": 1.85,
      "max_drawdown": 0.08,
      "volatility": 0.15
    },
    "audience": ["financial_agent", "certified_agent"]
  },
  "trading_capabilities": {
    "actions": ["buy", "sell", "rebalance"],
    "audience": ["trading_agent"],
    "risk_limits": {
      "max_transaction": 10000,
      "daily_limit": 50000
    }
  },
  "compliance_data": {
    "regulatory_info": "All transactions comply with MiFID II requirements",
    "audience": ["validator", "institution"],
    "audit_trail": "complete"
  }
}

**🎮 Gaming: Community-Aware Content**

json
{
  "game_status": {
    "player_message": "You're currently ranked #1,247 globally! 🎮",
    "audience": ["human"]
  },
  "agent_coordination": {
    "team_formation": {
      "preferred_roles": ["tank", "support"],
      "skill_level": "intermediate",
      "voice_chat_ok": true
    },
    "audience": ["gaming_agent"]
  },
  "moderation_data": {
    "toxicity_score": 0.02,
    "community_standing": "excellent",
    "recent_reports": 0,
    "audience": ["moderation_agent", "validator"]
  }
}

🔧 Implementation Patterns

**Global vs Local Audience Targeting**

json
{
  "feed_type": "mcp",
  "audience": ["llm", "developer"], // Global default
  "metadata": {
    "title": "Multi-Audience Service"
  },
  "capabilities": [
    {
      "name": "public_search",
      "description": "Search public content",
      "audience": ["llm", "public_agent"] // Local override
    },
    {
      "name": "advanced_analytics", 
      "description": "Enterprise analytics suite",
      "audience": ["enterprise_agent", "certified_agent"]
    }
  ],
  "documentation": {
    "user_guide": {
      "content": "How to use this service...",
      "audience": ["human"]
    },
    "api_reference": {
      "content": "Technical implementation details...",
      "audience": ["developer"]
    }
  }
}

**Conditional Audience Targeting**

json
{
  "premium_features": {
    "content": "Advanced AI capabilities available",
    "audience": ["certified_agent"],
    "conditions": {
      "subscription_tier": "premium",
      "trust_score": "> 0.8",
      "certification": "llmca_verified"
    }
  },
  "trial_features": {
    "content": "Try our basic features for free",
    "audience": ["public_agent"],
    "conditions": {
      "rate_limit": "10_requests_per_hour"
    }
  }
}

🧠 Agent Behavior Specifications

**Processing Logic**

typescript
// Agent content filtering logic
function processContent(content: any, agentType: string): any {
  if (content.audience) {
    // Check if agent is in target audience
    if (!content.audience.includes(agentType)) {
      // Handle non-target content
      return handleNonTargetContent(content, agentType);
    }
  }

  // Process target content
  return processTargetContent(content);
}

function handleNonTargetContent(content: any, agentType: string): any {
  switch (agentType) {
    case 'llm':
      return { 
        summary: "Content available for other audiences",
        available_audiences: content.audience 
      };
    case 'human':
      return { 
        message: "Technical details available through API" 
      };
    default:
      return null; // Skip entirely
  }
}

**Enhanced Agent Expectations**

ConditionAgent BehaviorUser Impact
`audience: ["llm"]`Parse and executeSeamless automation
`audience: ["human"]`Present to userClear communication
`audience: ["developer"]`Expose as documentationTechnical reference
`audience: ["validator"]`Verify and auditTrust validation
Mixed audiencesApply progressive disclosureOptimized for each consumer
No audience fieldAssume universal accessBackward compatibility

🔐 Security & Privacy Integration

**Risk-Based Audience Filtering**

json
{
  "sensitive_data": {
    "financial_details": "Account balance: $50,000",
    "audience": ["certified_agent"],
    "risk_requirements": {
      "min_trust_score": 0.9,
      "encryption_required": true,
      "audit_trail": "mandatory"
    }
  },
  "public_summary": {
    "general_info": "Account in good standing",
    "audience": ["llm", "human"],
    "risk_score": 0.1
  }
}

Integrates with LLMFeed Risk Scoring for enhanced security.

**Compliance-Aware Targeting**

json
{
  "gdpr_compliant_data": {
    "anonymized_analytics": "Usage patterns show 85% satisfaction",
    "audience": ["llm", "validator"],
    "compliance": ["gdpr", "ccpa"]
  },
  "full_personal_data": {
    "user_profile": "Complete user information...",
    "audience": ["certified_agent"],
    "compliance_requirements": {
      "explicit_consent": true,
      "data_residency": "eu",
      "retention_limit": "2_years"
    }
  }
}

💼 Enterprise Patterns

**Multi-Tenant Audience Management**

json
{
  "tenant_specific_data": {
    "company_a_metrics": "Performance data for Company A",
    "audience": ["enterprise_agent"],
    "tenant_id": "company_a",
    "isolation_level": "strict"
  },
  "shared_capabilities": {
    "common_features": "Available to all tenants",
    "audience": ["llm", "enterprise_agent"],
    "tenant_id": "*"
  }
}

**Role-Based Content Delivery**

json
{
  "executive_summary": {
    "content": "High-level business metrics and KPIs",
    "audience": ["executive_agent", "institution"]
  },
  "operational_details": {
    "content": "Detailed system metrics and alerts",
    "audience": ["operations_agent", "developer"]
  },
  "compliance_report": {
    "content": "Regulatory compliance status",
    "audience": ["compliance_agent", "validator"]
  }
}

📱 Mobile & Cross-Platform Integration

**Device-Aware Targeting**

json
{
  "mobile_optimized": {
    "content": "Simplified interface for mobile agents",
    "audience": ["mobile_agent"],
    "constraints": {
      "max_payload_size": "50kb",
      "offline_capable": true
    }
  },
  "desktop_full_features": {
    "content": "Complete feature set",
    "audience": ["llm", "developer"],
    "requires": ["high_bandwidth", "persistent_connection"]
  }
}

Integrates with Mobile App Feed Type for seamless cross-platform experiences.

🎨 Content Strategy Guidelines

**Audience-First Content Design**

  1. Define Your Audiences Early

    json
    {
 "content_strategy": {
   "primary_audiences": ["llm", "human"],
   "secondary_audiences": ["developer"],
   "restricted_audiences": ["validator"]
 }
}

  2. Design Progressive Disclosure Paths

    Human View: "Your document is being analyzed..."
↓
Agent View: { "status": "processing", "eta": 30, "capabilities": [...] }
↓  
Developer View: { "api_endpoints": [...], "schemas": [...] }

  3. Implement Security Boundaries

    • Public data → ["llm", "human"]
    • Sensitive operations → ["certified_agent"]
    • Administrative functions → ["validator", "institution"]

**Content Optimization by Audience**

AudienceContent StyleKey Principles
`llm`Structured, actionablePrecise instructions, clear schemas
`human`Natural, explanatoryUser-friendly language, context
`developer`Technical, completeFull documentation, examples
`validator`Verifiable, traceableAudit trails, signatures

🔄 Dynamic Audience Adaptation

**Context-Aware Audience Selection**

json
{
  "adaptive_content": {
    "business_hours": {
      "content": "Customer service agent available",
      "audience": ["llm"],
      "conditions": {
        "time": "09:00-17:00",
        "timezone": "user_local"
      }
    },
    "after_hours": {
      "content": "Automated support only",
      "audience": ["llm"],
      "conditions": {
        "time": "17:01-08:59"
      }
    }
  }
}

**Performance-Based Targeting**

json
{
  "high_performance_features": {
    "content": "Advanced AI capabilities",
    "audience": ["llm"],
    "performance_requirements": {
      "min_response_time": "< 200ms",
      "min_accuracy": "> 95%"
    }
  },
  "fallback_features": {
    "content": "Basic functionality",
    "audience": ["llm"],
    "fallback_for": "high_performance_features"
  }
}

📊 Analytics & Optimization

**Audience Engagement Tracking**

json
{
  "analytics": {
    "audience_metrics": {
      "llm_engagement": {
        "content_consumed": 847,
        "actions_triggered": 234,
        "success_rate": 0.94
      },
      "human_engagement": {
        "content_viewed": 1203,
        "time_spent": "avg_3.2_minutes",
        "satisfaction": 0.88
      },
      "developer_engagement": {
        "docs_accessed": 89,
        "integration_attempts": 23,
        "success_rate": 0.96
      }
    }
  }
}

**A/B Testing by Audience**

json
{
  "experiment_content": {
    "variant_a": {
      "content": "Try our new AI assistant",
      "audience": ["llm"],
      "experiment": "assistant_onboarding_v1"
    },
    "variant_b": {
      "content": "Discover powerful automation",
      "audience": ["llm"], 
      "experiment": "assistant_onboarding_v2"
    }
  }
}

🎯 Future Evolution: AI-Powered Audience Intelligence

**Predictive Audience Targeting**

json
{
  "smart_targeting": {
    "predicted_needs": {
      "content": "Based on your usage pattern, you might need...",
      "audience": ["llm"],
      "prediction_confidence": 0.87,
      "ml_model": "user_intent_predictor_v2"
    }
  }
}

**Cross-Agent Learning**

json
{
  "collective_intelligence": {
    "optimization_insights": {
      "content": "Other agents found this helpful",
      "audience": ["llm"],
      "source": "agent_network_learning",
      "privacy_preserved": true
    }
  }
}

💡 Impact: Transforming the Agentic Web

**For Users**

  • ✅ Reduced cognitive load: See only relevant information
  • ✅ Improved security: Sensitive data properly controlled
  • ✅ Better UX: Optimized content for each interaction type
  • ✅ Faster interactions: No parsing through irrelevant content

**For Agents**

  • ✅ Higher accuracy: Process only relevant, structured data
  • ✅ Better performance: Reduced payload sizes and parsing time
  • ✅ Enhanced security: Access appropriate content based on certification
  • ✅ Improved coordination: Clear boundaries between agent types

**For Developers**

  • ✅ Cleaner architecture: Separation of concerns by audience
  • ✅ Easier maintenance: Audience-specific content updates
  • ✅ Better testing: Validate content for each audience type
  • ✅ Enhanced compliance: Built-in privacy and security controls

**For Organizations**

  • ✅ Risk reduction: Controlled access to sensitive information
  • ✅ Compliance automation: Audience-based data governance
  • ✅ Operational efficiency: Reduced support burden through better UX
  • ✅ Innovation enablement: Safe experimentation with new audiences

📋 Best Practices

**Content Design**

  1. Start with audience mapping before creating content
  2. Use progressive disclosure to guide users through complexity
  3. Implement security boundaries based on audience trust levels
  4. Design for accessibility across all audience types

**Technical Implementation**

  1. Validate audience targeting in development environments
  2. Monitor audience engagement through analytics
  3. Test cross-audience scenarios for edge cases
  4. Implement graceful fallbacks for unsupported audiences

**Security & Compliance**

  1. Map audiences to risk levels and apply appropriate controls
  2. Audit audience access patterns regularly
  3. Implement consent mechanisms for sensitive audience targeting
  4. Document audience policies for compliance reviews

🔗 Related Extensions & Specifications

📚 See Also

Audience targeting represents one of LLMFeed's most transformative capabilities, enabling the transition from static, one-size-fits-all content to dynamic, context-aware experiences that optimize for each consumer's specific needs and capabilities.

⚡

Ready to Implement? Get AI-Powered Guidance

Reading docs manually takes time. Your AI can digest the complete LLMFeed specification and provide implementation guidance tailored to your needs.

🎯

Quick Start

Essential concepts for immediate implementation

~22K tokens • 30s analysis • Core concepts
📚

Complete Mastery

Full specification with examples and edge cases

~140K tokens • 2min analysis • Everything
💡 Works with Claude, ChatGPT, Gemini⚡ Instant implementation guidance🎯 Tailored to your specific needs