Background

Tim Berners-Lee's Charlie Works describes a vision for trustworthy, user-centric AI assistants that leverage personal data stored in Solid Pods. Rather than giving generic responses, "Charlie" accesses an individual's structured data (with consent) to provide highly personalized answers.

Key insight: "the pod full of semantic web data is extremely powerful in driving the LLM."

How it maps to JSS

JSS already has most of the building blocks:

• Solid Pods with LDP containers and JSON-LD resources
• WAC auth for fine-grained access control
• /db/ route backed by MongoDB for structured data storage
• IdP with credentials endpoint for token-based auth
• Pay-as-you-go token model — agent interactions can be metered

Proposed implementation

1. /agent/ endpoint — accepts a natural language query + consent scope
2. Pod data retrieval — agent reads user's pod data (fitness, finances, preferences, etc.) based on granted permissions
3. LLM context injection — pod data is serialized as JSON-LD context and sent to an LLM (e.g. Claude API)
4. Response — personalized answer returned to the user
5. Consent flow — user explicitly grants/revokes data access per query or per session
6. Token metering — each agent query costs tokens, fits the pay-as-you-go model

Data flow

User query + consent → /agent/ endpoint
  → Read pod data (WAC-scoped)
  → Build JSON-LD context
  → Send to LLM API
  → Return personalized response

Reference

• https://www.w3.org/DesignIssues/Works.html
• The AI populates data into the graph AND uses the graph as context — bidirectional