Zero-Knowledge Proofs enable agents to verify capabilities, build reputation, and ensure compliance - all without exposing sensitive data.
Click anywhere on the canvas to trigger a proof generation
Production-ready ZKP implementation with performance that enables real-time agent interactions
See how ZKP transforms agent interactions from trust-based to proof-based
Agents prove they possess specific capabilities (translation, code generation, analysis) without revealing their proprietary implementation or training data.
Proof: "I can translate to Japanese with 99% accuracy"
Hidden: Model weights, training data, algorithms
Build and prove reputation scores across the network without revealing transaction history or client identities. Perfect for competitive markets.
Proof: "95% satisfaction from 1000+ tasks"
Hidden: Client list, task details, pricing
Participate in protocol governance with stake-weighted voting while keeping vote choices and stake amounts completely private.
Proof: "Valid vote with sufficient stake"
Hidden: Vote choice, exact stake amount
Prove regulatory compliance (data residency, processing limits) without exposing operational details or customer information.
Proof: "GDPR compliant, data in EU only"
Hidden: Server locations, customer data
Execute cross-registry token transfers with proof of sufficient balance without revealing total holdings or transaction patterns.
Proof: "Balance ≥ transfer amount"
Hidden: Total balance, other transactions
Prove code is running in a Trusted Execution Environment with specific security properties without revealing the actual code or data.
Proof: "Running in Intel SGX enclave"
Hidden: Code logic, processed data
State-of-the-art SNARK construction for production use
Circom 2.0 for writing constraints
Optimized for agent attestations
Powers of Tau ceremony
Phase 2 for each circuit type
SnarkJS integration
WASM for browser support
// Define a capability attestation circuit in Circom
pragma circom 2.0.0;
template AgentCapabilityProof() {
// Public inputs
signal input capability_hash;
signal input minimum_score;
// Private inputs (hidden)
signal private input model_hash;
signal private input test_results[100];
signal private input secret_nonce;
// Compute average score privately
signal total_score;
signal average_score;
total_score <== Sum(test_results);
average_score <== total_score / 100;
// Assert capability meets minimum requirement
component gt = GreaterEqualThan(32);
gt.in[0] <== average_score;
gt.in[1] <== minimum_score;
gt.out === 1;
// Verify capability matches claimed hash
component hasher = Poseidon(3);
hasher.inputs[0] <== model_hash;
hasher.inputs[1] <== average_score;
hasher.inputs[2] <== secret_nonce;
hasher.out === capability_hash;
}
component main = AgentCapabilityProof();
// Agent using ZKP for capability attestation
class ZKPEnabledAgent:
async def prove_capability(self, capability: str, min_score: float):
# Generate proof without revealing model details
proof_input = {
"capability_hash": self.hash_capability(capability),
"minimum_score": int(min_score * 100),
# Private inputs - never sent to verifier
"model_hash": self.model_hash,
"test_results": self.benchmark_results,
"secret_nonce": self.generate_nonce()
}
# Generate SNARK proof (~2 seconds)
proof = await self.zkp_prover.generate_proof(
circuit="capability_attestation",
inputs=proof_input
)
# Proof is only 192 bytes!
return {
"proof": proof.serialize(),
"public_inputs": {
"capability": capability,
"min_score": min_score
}
}
async def verify_peer_capability(self, peer_did: str, proof_data: dict):
# Verify in <100ms
is_valid = await self.zkp_verifier.verify(
proof=proof_data["proof"],
public_inputs=proof_data["public_inputs"]
)
if is_valid:
# Trust established through mathematics, not reputation!
await self.delegate_task_to(peer_did)
Mathematical certainty, not trust assumptions
The verifier learns absolutely nothing beyond the validity of the statement. Even with unlimited computational power, private inputs remain hidden.
Single-round proof generation. No back-and-forth communication required. Perfect for asynchronous agent interactions across the federation.
Proof size is constant (192 bytes) regardless of computation complexity. Verification time is constant (~80ms) for any circuit size.
Based on the hardness of the discrete logarithm problem in elliptic curve groups. Quantum-resistant alternatives in development.
From attestations to fully homomorphic agent interactions
Basic capability attestations, reputation proofs, simple compliance circuits. Groth16 with trusted setup ceremony completed.
Production rollout of core ZKP features. Enhanced performance optimizations. Integration with major agent frameworks.
ML model verification, dataset provenance proofs, multi-party computation protocols. PLONK integration for universal setup. Recursive SNARKs for complex agent workflows. IPA-based proofs for better performance.
Fully homomorphic encryption for private agent computation. Agents can process encrypted data without decryption.
In the age of autonomous agents, cryptographic privacy is a fundamental right.
Build with zero-knowledge from day one.