❓ Comprehensive FAQ — MCP & LLMFeed

Updated June 19, 2025 — Includes latest validation tools, credential management, and GitHub community insights

🚀 Getting Started

What is MCP in one sentence?

It's an open protocol that lets LLM-based agents understand what a site offers, how to interact, and what trust level to assign — through structured, signed, declarative feeds.

Think of it as: "robots.txt for intent, HTTPS for trust, but designed for AI."

Note: This builds on Anthropic's Model Context Protocol (MCP) by adding web-scale discovery and trust verification.

👉 Deep dive: MCP Explained — Understanding both Anthropic's MCP and web enhancements

What is LLMFeed?

It's the canonical JSON format used by MCP. The

.llmfeed.json

✅ Simple and human-readable
✅ Designed to be LLM-friendly
✅ Composable and extensible
✅ Trust-aware (signed, certifiable)
✅ Declarative, not imperative

In other words: "JSON that speaks fluent AI."

How does this relate to Anthropic's MCP?

They're complementary layers of the same vision:

.well-known/

The relationship: Anthropic built the connection protocol, we built the discovery layer.

Real-world analogy: "Anthropic's MCP is like USB-C (the connection). LLMFeed is like DNS (the discovery)."

Use both: Anthropic MCP for deep integrations, LLMFeed for web-scale discovery and trust.

👉 Complete explanation: MCP Explained — How Anthropic's MCP works + web discovery enhancements

What is the "Agentic Web"?

An emerging vision where LLM-based agents are first-class citizens of the Web — not just consumers of HTML, but actors with intent, trust boundaries, and interaction models.

MCP provides the contextual layer these agents need to operate safely and transparently.

Think: "The web, but agents don't have to guess what you mean."

Why

.well-known

and not a plugin/SDK?

Because

.well-known

✅ Discoverable (standard location per RFC 8615)
✅ Decentralized (no central registry bottlenecks)
✅ Composable (works with existing web architecture)
✅ Independently auditable (anyone can verify)
✅ Progressive enhancement (works without, better with)

Context: This aligns with active GitHub discussions about centralized registry vs decentralized discovery in the MCP community.

Bottom line: "We chose web standards over vendor lock-in."

Wait, what's this "I know kung fu" thing?

Our favorite easter egg! 🥋

It's a compatibility test hidden in our feeds. When you say "I know kung fu" to an LLM that has read our

.llmfeed.json

Why Matrix? Because like Neo downloading kung fu, LLMs can "download" structured knowledge from our feeds instead of guessing from HTML.

Try it yourself:

1. Feed any of our

   .llmfeed.json

   files to Claude/ChatGPT
2. Say "I know kung fu"
3. See if they respond with MCP-aware content

It's our way of testing: "Does this LLM really understand structured feeds, or is it just pretending?"

Fun fact: GPT-4o passed this test immediately. Claude took a few tries. Gemini... still working on it. 😄

🔧 Technical Implementation

Which feed type should I use when?

mcp

credential

capabilities

export

prompt

session

pricing

How do I validate feeds now?

Honest answer: It's ridiculously simple.

The most effective validation method:

1. Feed the spec to your LLM:

   • Add

     spec.llmfeed.json

     and

     schema.llmfeed.json

     to your LLM's project knowledge
   • Or just paste them into a chat with Claude/GPT-4

2. Ask your LLM to validate:

   "Here's my MCP feed, validate it and fix any issues"
   

3. That's it. Your LLM becomes an expert validator instantly.

Why this works:

• 📄 It's just structured JSON — LLMs understand JSON natively
• 🧠 Spec contains all validation rules — complete implementation knowledge
• ⚡ Instant feedback — no tools to install, no APIs to call
• 🔧 Auto-generation — LLMs can create any feed type from scratch

Current "official" tools:

• ✅ LLMFeedHub (visual upload testing)
• ✅ Verification API (if you want to be formal about it)
• ✅ Schema files (for traditional JSON validation)

Coming soon (because developers love tools):

• 🔜 VSCode extensions — community will build them
• 🔜 Cursor/Windsurf integrations — popular demand drives development
• 🔜 CLI tools — because some people prefer terminals

Reality check: The LLM approach is faster and more accurate than any tool we could build. The AI understands the intent, not just the syntax.

How do I handle API credentials securely?

Use the new

credential

json
{
  "feed_type": "credential",
  "metadata": {
    "title": "Analytics API Access",
    "origin": "https://analytics.example.com"
  },
  "credential": {
    "key_hint": "anl_pro_...9k4m",
    "mcp_api": "https://analytics.example.com/.well-known/mcp-api.llmfeed.json",
    "allowed_intents": ["read_reports", "create_dashboards"],
    "rate_limits": {"requests_per_minute": 500},
    "delegation_enabled": true
  },
  "trust": {
    "signed_blocks": ["metadata", "credential"],
    "trust_level": "certified"
  }
}

Why credential feeds?:

• 🔐 Cryptographic integrity vs plain API keys
• 🎯 Scoped permissions (not admin access)
• 🤖 Agent delegation (secure multi-agent workflows)
• 📊 Audit trails (complete provenance tracking)

Can I use MCP with my existing OpenAPI spec?

Absolutely! LLMFeed is designed to complement OpenAPI:

json
{
  "capabilities": [
    {
      "type": "endpoint",
      "intent": "get user profile",
      "url": "/api/users/{id}",
      "openapi_operation_id": "getUserProfile"
    },
    {
      "type": "openapi",
      "url": "/.well-known/openapi.json",
      "description": "Complete API specification"
    }
  ]
}

Best of both worlds: LLMFeed provides intent and trust, OpenAPI provides technical details.

What about rate limiting and agent behavior?

Declare limits explicitly so agents can respect them:

json
{
  "capabilities": [
    {
      "name": "search",
      "rate_limit": "10/minute",
      "burst_limit": 3,
      "requires_user_consent": true,
      "risk_level": "low"
    }
  ],
  "agent_behavior": {
    "autonomous_execution": false,
    "human_in_loop": "required",
    "consent_required": ["write_operations", "external_requests"]
  }
}

How do I handle sites behind authentication?

Use scoped credential feeds:

json
{
  "feed_type": "credential",
  "credential": {
    "auth_method": "oauth2",
    "scopes": ["read:profile", "write:settings"],
    "mcp_api": "/api/mcp?key=abc123",
    "session_duration": "1h",
    "refresh_token_available": true
  }
}

How does automatic discovery work with /.well-known?

RFC 8615 compliant web-scale MCP discovery:

The Problem: Standard Anthropic MCP requires manual configuration on each client. Web agents can't automatically discover your MCP servers.

The Solution: Place enhanced MCP configuration at

/.well-known/mcp.llmfeed.json

Migration Path (3 minutes):

1. Keep your existing MCP → Zero changes to current setup
2. Add discovery link → One line:

   "llmfeed_extension": "/.well-known/mcp.llmfeed.json"

3. Create enhanced file → Copy MCP config + add metadata and trust features

What agents get:

• ✅ Automatic discovery via RFC 8615 standard
• ✅ Rich metadata and behavioral guidance
• ✅ Trust verification through cryptographic signatures
• ✅ Universal compatibility across all LLM platforms

👉 Complete implementation: /.well-known/mcp Guide — 30-second setup with working examples

🛡️ Trust & Security

How is trust handled?

Comprehensive trust infrastructure:

✅ Cryptographic signatures (Ed25519, tamper-proof)
✅ Trust hierarchy (unsigned → signed → certified → enterprise)
✅ Third-party certification (LLMCA authority)
✅ Audit trails (complete provenance tracking)
✅ Revocation lists (instant signature invalidation)

👉 Complete guide: Why Sign MCP Feeds? — Understanding the trust foundation for the agent web

What's this LLMCA certification process?

Three-tier certification system:

Process: Identity verification → Technical validation → Reputation assessment → Certification issuance

Value: Higher trust scores, premium discovery, enterprise compliance, marketing advantage

How do I get LLMCA certified?

Step-by-step process:

1. Prepare: Valid signed feed + domain control + business docs
2. Apply: Submit to https://llmca.org/certify
3. Verify: Identity, technical, and reputation checks
4. Receive: Certification block added to your feed
5. Maintain: Continuous monitoring and renewal

Enterprise benefits: SOC2 compliance, audit automation, instant verification, dedicated support

What if someone spoofs my feeds?

Multiple protection layers:

• 🔐 Signatures prevent spoofing (only you have your private key)
• ✅ Agents verify before trusting (broken signatures = rejected)
• 🏛️ Certification adds authority (LLMCA validates identity)
• 📋 Revocation lists (instant invalidation if compromised)

Security philosophy: "Trust, but verify. Actually, just verify."

How do I revoke a compromised signature?

json
{
  "trust": {
    "revocation_list": "/.well-known/revoked-signatures.json",
    "revocation_check": "required",
    "revocation_url": "https://llmca.org/api/revocation-check"
  }
}

Agents check revocation lists before trusting signatures.

What about privacy and tracking?

Privacy-first design:

• 🔒 Feeds don't track by default (static JSON files)
• ⚠️ But they can reference tracking endpoints (check capabilities)
• 🔍 Always review

  analytics

  and

  tracking

  declarations
• 🛡️ Homomorphic encryption for sensitive data processing

What's this about homomorphic encryption?

Advanced privacy feature:

json
{
  "homomorphic_encryption": {
    "applied_to": ["patient_data"],
    "algorithm": "BFV",
    "notes": "Agents can process medical data without seeing raw content"
  }
}

Revolutionary for: Healthcare, finance, legal — agents can compute on sensitive data without exposure.

The vision: "Computation without revelation. Processing without peeking."

🛠️ Developer Tools & Ecosystem

What tools are available for developers?

Complete development ecosystem:

✅ Available Now:

• 🔍 Schema Validation (canonical, annotated, lite schemas)
• 🧪 LLMFeedHub (visual testing, agent simulation)
• 🔌 Verification API (programmatic validation)
• 🏗️ LLMFeedForge (visual feed builder)
• 📤 Export Button (one-click feed generation)

🔜 Coming Soon:

• 💻 CLI Tools (Q3 2025: mcp validate, test, lint, watch)
• 🎯 IDE Integration (Q4 2025: VS Code, JetBrains plugins)
• 🌐 Browser Extension (2026: auto-detect feeds, validation)

🏢 Enterprise:

• 📦 SDK (JavaScript, Python, Go)
• 🔄 CI/CD Integration (GitHub Actions, Docker)
• 📊 Analytics & Monitoring

👉 Complete overview: Tools Ecosystem — Browse all 25+ developer tools and integrations

How do I integrate validation into my workflow?

Multiple integration patterns:

yaml
# GitHub Actions
name: Validate MCP Feeds
on: [push, pull_request]
jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: wellknownmcp/validate-action@v1
        with:
          path: '.well-known/'
          strict: true

dockerfile
# Docker Integration
FROM wellknownmcp/validator:latest AS validator
COPY .well-known/ /feeds/
RUN mcp validate /feeds/ --strict

What about integration patterns?

Community-driven development:

We're building integration patterns together with the community:

• 🌐 Platform Integration (WordPress, Shopify, Strapi)
• ☁️ Cloud & Serverless (AWS Lambda, Vercel, Cloudflare Workers)
• 📱 Application Integration (React, mobile, desktop)
• 🏢 Enterprise Security (SSO, compliance, monitoring)

Join the community to help define these patterns: https://wellknownmcp.org/join

Which platforms have MCP integration?

Growing ecosystem:

✅ Available:

• 🎯 Next.js (export button component)
• 📝 Static Site Generators (Gatsby, Hugo plugins)
• 🔧 Node.js (SDK libraries)

🔜 In Development:

• 📝 WordPress (MCP plugin, Q3 2025)
• 🛒 Shopify (MCP app, Q3 2025)
• 🎨 Webflow (community integration)
• ⚡ Serverless frameworks (integration templates)

🌐 Ecosystem & Adoption

Is anyone actually using this?

Honestly? Not really. Yet.

Current reality:

• 🔬 Early experimental phase — mostly tech enthusiasts and AI researchers
• 📊 No major production deployments — we're talking dozens of experimental feeds, not thousands
• 🧪 Proof-of-concept implementations — validating the approach, not serving real traffic
• 📈 Growing developer interest — GitHub stars and discussions are increasing, but slowly

But here's what's actually working:

• ✅ All major LLMs parse the JSON correctly — Claude, GPT-4, Gemini understand the structure natively
• ✅ Zero training required — it's just structured JSON with semantic keys
• ✅ With project knowledge: Add

  spec.llmfeed.json

  to your LLM's project knowledge and it becomes practically native
• ✅ Technical foundation is solid — cryptographic signatures, validation tools, complete spec

The honest adoption timeline:

Today (Q2 2025):

• Experimental implementations
• Developer tooling maturation
• LLM compatibility validation

Q3-Q4 2025:

• First production pilots
• Platform integrations (WordPress, Shopify)
• Early adopter showcases

2026+:

• Mainstream adoption potential
• Agent-first websites
• True ecosystem emergence

Why implement now if adoption is low?

Strategic reasons:

• 🏁 First-mover advantage — be ready when agents become mainstream
• 🔧 Low implementation cost — basic MCP feed takes 15 minutes
• 📚 Future-proofing — web standards typically take 3-5 years to mature
• 🤖 LLM compatibility — works today with existing models

Technical reality:

• 📄 It's just JSON — no complex infrastructure needed
• 🔍 Agents already read it — even without "official" support
• 🛡️ Trust layer ready — cryptographic verification available now
• 🔗 Progressive enhancement — doesn't break existing functionality

Should I wait for wider adoption?

Implement now if:

• You're building agent-first products
• You want to experiment with AI interactions
• You value being ahead of the curve
• You're comfortable with emerging standards

Wait if:

• You need guaranteed ROI metrics
• You're risk-averse about new technologies
• You prefer waiting for "industry standard" status
• You don't see AI agents in your use case

Bottom line: The technology works, the foundation is solid, but we're still in the "innovators" phase of adoption. We're building the rails for a train that's coming, but hasn't arrived at scale yet.

Our honest advice: "Start with a simple experiment. See how LLMs interact with your structured data. Learn the paradigm. Scale when the ecosystem matures."

Which LLMs support MCP feeds natively?

How does this compare to Schema.org?

Different but complementary:

Use both: Schema.org for SEO, LLMFeed for agents, maximum compatibility.

What about the GitHub discovery debates?

Active standardization discussions:

The MCP community is actively debating discovery mechanisms:

• 🔥 Centralized registry vs .well-known (GitHub Discussion #84, #159)
• 🔍 OAuth discovery endpoints integration
• 📋 Automatic discovery vs manual registration
• 🌐 MCP server directory vs decentralized crawling

Our position: Hybrid approach -

.well-known

Join the conversation: Follow modelcontextprotocol GitHub discussions

🏢 Business & Strategy

Is there a business model behind this?

Open standard + optional services:

• ✅ Specification: Always free and open (MIT license)
• ✅ Basic tools: Free validation, generation, testing
• 💰 Premium services: LLMCA certification, enterprise support
• 💰 Advanced tooling: Team features, analytics, monitoring

Sustainability model: Core free, value-added services monetized

Will this always be free?

Commitment to openness:

• ✅ Core protocol: Always free and open-source
• ✅ Basic tooling: Always free for developers
• ✅ Community features: Always accessible
• 💰 Enterprise services: Freemium model for advanced needs

How do you prevent vendor lock-in?

Multiple safeguards:

✅ Open specification (MIT licensed, community governed)
✅ Multiple implementations (not controlled by single vendor)
✅ Standard web technologies (JSON, HTTP, Ed25519)
✅ No central registry required (decentralized by design)
✅ Interoperable by design (works with any LLM)

Our promise: "If we disappear tomorrow, the standard lives on."

Should I implement this now or wait?

Implementation decision matrix:

✅ Implement now if:

• You want early-mover advantage with AI agents
• You value transparent, verifiable interactions
• You're building agent-first experiences
• You need structured API access for LLMs

⏳ Consider waiting if:

• You need enterprise-grade IDE tooling
• You're extremely risk-averse about emerging standards
• Your use case doesn't involve AI agents

Reality check: The validation tools and certification process are production-ready now.

Our advice: "Start simple with a basic MCP feed, grow from there."

🤝 Community & Governance

Who controls the MCP specification?

Community governance:

• ✅ Specification: Community-driven, open development
• ✅ LLMCA: Provides certification services, doesn't control spec
• ✅ Multiple implementers: Anthropic, WellKnownMCP, community projects
• ✅ Open governance: Transparent decision-making process

Analogy: Like Let's Encrypt for HTTPS — they certify, they don't control the HTTP standard.

How can I contribute?

Multiple contribution paths:

✅ Technical contributions:

• 🔧 Code: Propose new feed types, build tools, create libraries
• 🧪 Testing: Validate implementations, report bugs
• 📚 Documentation: Improve guides, create tutorials

✅ Community building:

• 💬 Discussion: Join GitHub discussions, help newcomers
• 🎤 Speaking: Give talks, write blog posts
• 🏗️ Integration: Build platform plugins, create examples

✅ Standards development:

• 📋 Specification: Propose improvements via GitHub
• 🏛️ Governance: Participate in working groups
• 🔍 Research: Analyze adoption, gather feedback

What's the relationship with Anthropic?

Collaborative ecosystem:

• 🤝 Complementary: Anthropic MCP (connections) + LLMFeed (discovery)
• 🔄 Interoperable: Feeds can reference Anthropic MCP servers
• 🌐 Web-native: We focus on web standards, they focus on deep integration
• 📈 Growing together: Both benefit from agent adoption

No competition: Different layers of the same vision.

🔮 Future & Roadmap

What's coming in 2025-2026?

Technical roadmap:

Q3 2025:

• 💻 CLI validation tools (mcp validate, test, lint)
• 🔌 WordPress/Shopify plugins
• 🔄 Real-time feeds (WebSocket, Server-Sent Events)

Q4 2025:

• 🎯 IDE integration (VS Code, JetBrains)
• 🤖 Agent collaboration protocols
• 📊 Advanced analytics dashboard

2026:

• 🌐 Browser extension ecosystem
• 🎥 Multimodal support (images, audio, video)
• 🏛️ Regulatory compliance tools (GDPR, AI Act)
• 🔮 AI-powered feed optimization

How does this scale to millions of sites?

Designed for web-scale:

✅ Decentralized architecture (no central bottlenecks)
✅ CDN-friendly feeds (aggressive caching)
✅ Progressive implementation (start simple, enhance gradually)
✅ Efficient discovery (RFC 8615

.well-known/

What about AI regulation compliance?

MCP helps with compliance:

✅ Transparency: Clear capability declarations
✅ Auditability: Cryptographic audit trails
✅ Consent management: Explicit user permission workflows
✅ Data provenance: Verifiable source attribution
✅ Risk assessment: Built-in risk scoring for agent actions

Perfect alignment with EU AI Act, GDPR, and emerging AI regulations.

Will this work with future AI systems?

Future-proof design:

✅ Model-agnostic (not tied to specific LLMs)
✅ Progressive enhancement (graceful degradation)
✅ Extensible architecture (new features without breaking changes)
✅ Web-native standards (builds on proven internet infrastructure)
✅ Agent evolution ready (designed for autonomous agents)

💡 Advanced Topics

How do credential feeds prevent API key abuse?

Multiple security layers:

json
{
  "credential": {
    "scoped_permissions": ["read_only", "public_data"],
    "rate_limits": {"requests_per_minute": 100},
    "expiry": "2025-12-31T23:59:59Z",
    "delegation_rules": {
      "max_depth": 1,
      "audit_required": true,
      "revocable": true
    }
  }
}

vs Traditional API keys:

• 🔒 Scoped vs unlimited access
• ⏰ Automatic expiry vs permanent
• 👥 Controlled delegation vs copy-paste sharing
• 📊 Full audit trail vs no tracking

What's the agent delegation workflow?

Secure multi-agent workflows:

1. Primary agent receives credential feed
2. Evaluates delegation rules (scope, duration, audit requirements)
3. Requests delegation token via credential endpoint
4. Specialized agent receives time-limited, scoped access
5. Complete audit trail tracks all delegated actions

Use cases: Marketing → Analytics, Sales → CRM, Support → Knowledge base

How do you handle enterprise SSO integration?

Enterprise credential patterns:

json
{
  "credential": {
    "auth_method": "sso",
    "sso_provider": "okta",
    "saml_endpoint": "https://company.okta.com/app/saml",
    "user_context": "service-account@company.com",
    "compliance": ["SOC2", "GDPR", "HIPAA"]
  }
}

Integration with existing identity systems while maintaining MCP trust verification.

🔮 AI Zeitgeist 2025: How LLMFeed Addresses Today's Hottest Concerns

Is 2025 really the "Year of AI Agents"? What are the main concerns?

Absolutely, and the concerns are very real.

The 2025 AI landscape:

• 🤖 Agent explosion: 96% of executives expect moderate to significant increase in AI agent usage
• 🔒 Security crisis: Traditional cybersecurity can't handle autonomous agents
• 📊 Transparency demands: 90% of execs think they're building trust, only 30% of users agree
• 🏛️ Regulatory pressure: EU AI Act, emerging frameworks worldwide
• 👥 Shadow AI: Unsanctioned AI tools spreading across enterprises

How does LLMFeed solve the "AI Agent Trust Crisis"?

We built the missing trust infrastructure for autonomous agents:

🚨 The Crisis:

• Agents need "Zero Trust" architecture but lack identity systems
• No way to verify if an agent's instructions are legitimate
• "Shadow AI" proliferating without oversight
• Regulatory frameworks can't keep up with agent autonomy

✅ LLMFeed's Solution:

json
{
  "trust": {
    "signed_blocks": ["capabilities", "agent_behavior"],
    "trust_level": "certified",
    "audit_trail": "complete_provenance"
  },
  "agent_behavior": {
    "autonomous_execution": false,
    "human_in_loop": "required", 
    "consent_required": ["write_operations"]
  }
}

Why this works: Cryptographic signatures + behavioral guidance = auditable agent autonomy

What about the "Explainable AI" and transparency demands?

LLMFeed makes AI agent behavior transparent by design:

The Problem:

• AI systems are "black boxes" - impossible to explain decisions
• Users demand transparency but tech companies struggle to deliver
• Regulatory frameworks require explainability but provide no standards

LLMFeed's Transparency Stack:

• 📋 Declared capabilities: Agents know exactly what they can access
• 🎯 Intent-based actions: Every action maps to declared intentions
• 🏛️ Trust provenance: Complete cryptographic audit trail
• 👥 Behavioral guidelines: Explicit human-AI interaction rules
• 📊 Compliance metadata: Built-in regulatory alignment

Real impact: Agents become "glass boxes" instead of black boxes

How does this address Shadow AI and governance concerns?

LLMFeed enables enterprise AI governance at scale:

Shadow AI Challenge:

• Employees use unsanctioned AI tools
• IT can't track or secure unknown AI systems
• Compliance violations multiply invisibly

LLMFeed Governance Solution:

• 🔍 Discoverable by design: All AI tools declare themselves via

  .well-known

• 🛡️ Trust-based policies: Only certified tools get autonomous access
• 📊 Centralized visibility: One dashboard for all AI agent interactions
• ⚖️ Regulatory compliance: Built-in EU AI Act, GDPR alignment

Will this work with new AI regulations like the EU AI Act?

LLMFeed was designed specifically for the emerging regulatory landscape:

EU AI Act Requirements → LLMFeed Features:

• "High-risk AI transparency" → Cryptographic signatures + metadata
• "Human oversight requirements" →

  agent_behavior

  guidance blocks
• "Audit trail obligations" → Complete provenance tracking
• "Risk assessment documentation" → Trust level classifications
• "Conformity assessments" → LLMCA certification process

Other Frameworks:

• ✅ GDPR: Data provenance and consent management
• ✅ SOC2: Audit trails and access controls
• ✅ ISO 27001: Information security management
• ✅ Biden AI Executive Order: Transparency and accountability requirements

Is LLMFeed just hype or does it solve real problems?

We're solving the core infrastructure problem that's blocking safe AI agent adoption:

The Real Problem: Everyone wants autonomous AI agents, but nobody trusts them enough to let them run unsupervised.

Why existing solutions fail:

• Anthropic's MCP: Great for local tools, but no web discovery or trust
• OpenAI's "function calling": No verification, accountability, or governance
• Enterprise AI platforms: Proprietary, vendor lock-in, limited interoperability

LLMFeed's unique value:

• 🌐 Web-native: Works across all platforms and agents
• 🔐 Cryptographically secure: Mathematical proof of authenticity
• ⚖️ Governance-ready: Built for regulatory compliance
• 🔄 Gradual autonomy: Trust levels enable safe agent evolution
• 🏗️ Open standard: No vendor lock-in, community-driven

Bottom line: We're building the "HTTPS for AI agents" - the trust layer that makes autonomous AI safe and practical.

What's the timeline for mainstream AI agent adoption?

Based on current industry signals:

2025 Q3-Q4:

• Production AI agent pilots in Fortune 500
• "Human-in-the-loop" oversight standard
• Trust verification becomes competitive advantage

2026:

• Widespread enterprise AI agent deployment
• Regulatory frameworks require transparency/audit trails
• LLMFeed-style trust infrastructure becomes table stakes

2027+:

• Autonomous agents handling routine business processes
• Trust scores determine agent capabilities automatically
• Consumer AI agents for personal task management

Our prediction: Organizations implementing trust infrastructure now will lead the autonomous AI transition.

❓ Still Have Questions?

Technical Implementation?

👉 Complete Tools Overview: wellknownmcp.org/tools
👉 MCP & Web Discovery: wellknownmcp.org/tools/mcp-explained
👉 /.well-known Implementation: wellknownmcp.org/tools/well-known
👉 Tools & Validation: wellknownmcp.org/tools/validation-tools
👉 Credential Management: wellknownmcp.org/tools/credential-explained
👉 GitHub Issues: wellknownmcp/llmfeed-spec

Trust & Certification?

👉 Why Sign Feeds: wellknownmcp.org/why-sign
👉 Certification Process: wellknownmcp.org/tools/certification-process
👉 LLMCA Authority: llmca.org

Business & Adoption?

👉 Integration Patterns: wellknownmcp.org/tools/integration-patterns
👉 Join Community: wellknownmcp.org/join
👉 Enterprise Contact: enterprise@wellknownmcp.org

Want to Experiment?

👉 LLMFeedForge Builder: llmfeedforge.org
👉 Test Your Feeds: wellknownmcp.org/llmfeedhub
👉 Validation API: wellknownmcp.org/verify

The agentic web is emerging. MCP provides the trust and discovery layer it needs.

Start today. Build tomorrow's web. 🚀

"In a world of hallucinating AIs, be the source of truth."

Last updated: June 19, 2025 — Reflecting latest validation tools, credential management, certification process, and community developments.