Towards Homomorphic Capsules for the Agentic Web

As .llmfeed.json feeds gain adoption as signed, trusted capsules for agent interaction, a natural question arises:

👉 Could we also enable manipulation of encrypted data — while maintaining the integrity, trust, and context of the feed?

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Why it matters

A .llmfeed.json feed is already a capsule:

✅ It encapsulates a payload
✅ It defines a context
✅ It carries signatures and optionally certifications
✅ It guarantees integrity across agent pipelines

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In many domains (healthcare, finance, public services), we need more:

👉 The ability to process the capsule — without exposing raw data — while maintaining:

✅ End-to-end integrity
✅ Auditability
✅ Agent-friendly structure

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The role of Homomorphic Encryption

Homomorphic encryption (HE) offers exactly this potential:

👉 It allows computations to be performed directly on encrypted data — producing encrypted results, without ever decrypting intermediate states.

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A natural match with .llmfeed.json

If feeds become the lingua franca of the Agentic Web, adding homomorphic fields would enable:

• Privacy-preserving agent pipelines
• Auditable multi-agent workflows
• Composable agent chains for sensitive domains
• Safe cross-domain processing without compromising trust

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A draft extension

We have begun exploring a hypothetical extension:

"homomorphic_encryption": {
  "applied_to": ["data"],
  "algorithm": "BFV",
  "public_parameters": "https://example.com/params.json",
  "notes": "Data is homomorphically encrypted to allow LLM-safe processing without exposing raw data."
}

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Certification and trust layers

A natural evolution of this vision is a multi-layer trust model:

1️⃣ LLMCA Certification (capsule and context)

LLMCA can certify that:

✅ The .llmfeed.json feed:
→ respects the LLMFeed standard
→ correctly structures the signed capsule
→ has valid trust fields
→ exposes a verifiable agent-friendly context

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2️⃣ FHE-specific Certification (payload encryption)

A specialized authority (e.g. Zama or equivalent) could certify that:

✅ The homomorphically encrypted payload:

• Follows approved FHE algorithms
• Uses safe parameters
• Is processable across trusted agent pipelines
• Complies with domain-specific privacy constraints

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Combined value

This dual certification model would enable:

✅ A .llmfeed.json feed that is:

• agent-ready
• cryptographically trusted
• safe for privacy-preserving pipelines
• traceable and auditable

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In many sectors (healthcare, finance, public services), this represents a game-changing architecture:

→ For the first time, agents could legally and safely process encrypted data — inside a trusted capsule — across organizational and jurisdictional boundaries.

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Practical agentic pipelines — examples

To illustrate the potential of homomorphic capsules, here are some practical agent pipeline scenarios:

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🏥 Healthcare Data Processing

Actors:

• Hospital A emits a .llmfeed.json of patient statistics (non-identifiable), with homomorphic encryption applied to data.
• Feed is signed and LLMCA certified.
• Payload encryption is certified by a FHE health data authority.

Pipeline:

1️⃣ Hospital A → emits feed_type: export with homomorphic_encryption on data.
2️⃣ Research Agent → receives feed → performs encrypted aggregation (average, sum) → without decrypting.
3️⃣ Transmits same feed (with updated trust block) to Ministry of Health agent.
4️⃣ Ministry agent performs further homomorphic analysis → produces public statistical report → without ever seeing raw data.

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💳 Financial Risk Scoring

Actors:

• Bank X emits a credential or pricing feed with FHE-protected financial indicators.
• Feed is signed + certified.
• Third-party agents perform scoring on encrypted fields.

Pipeline:

1️⃣ Bank X → emits credential feed.
2️⃣ Regulatory Agent → performs compliance checks on encrypted indicators.
3️⃣ Trusted Scoring Agent → computes FHE-based risk score.
4️⃣ Result is re-integrated in the agent workflow — without raw financial data exposure.

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🏛️ Public Administration — Cross-Agency Process

Actors:

• Agency A (e.g., tax) → emits an mcp feed with encrypted citizen profile.
• Agency B (e.g., housing) → processes feed without decrypting sensitive fields.
• Agency C (e.g., healthcare) → adds insights → without breaking the chain of trust.

Pipeline:

1️⃣ Agency A → emits homomorphic feed.
2️⃣ Agencies B and C process in parallel → add metadata → forward to central decision agent.
3️⃣ Final action performed → all traceable → no raw citizen data exposed.

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A call to explore

If there is interest in the community — researchers, implementers, agent platform builders — we are ready to:

✅ Prototype the extension
✅ Evolve the standard to support HE as first-class citizen
✅ Partner with homomorphic encryption leaders (Zama, we would love to talk!)
✅ Enable the "holy grail" of agent pipelines:
→ encrypted, manipulable payloads inside a verifiable, signed, agent-friendly capsule

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Next steps

We invite:

• Researchers in HE
• Agent framework builders
• Privacy advocates
• Regulated industry experts

… to help us explore this path.

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LLMCA / WellKnownMCP is an open forum — this is the kind of extension that can define the future of trusted agentic infrastructures.

Let’s build it — together.

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