# ZKAgentVerificationOrchestrator

### Overview

`ZKAgentVerificationOrchestrator.circom` is the primary zkSNARK circuit for verifying the trustworthiness of LLM-based agents in the ZK-AgentMesh protocol. It enables zero-knowledge validation of training quality, ethics, and compliance without revealing any underlying data, model internals, or private metrics.

This circuit composes multiple modular sub-verifiers and outputs a unified proof of agent validity.

***

### Objectives

This circuit proves that a deployed AI agent meets the following conditions:

* Training quality surpasses a defined threshold
* Ethical requirements are satisfied (bias, fairness, harmful content)
* Compliance standards are met (data privacy, encryption, retention)
* Each of these proofs is tied to cryptographic commitments submitted prior to training

The circuit enables these proofs to be verified *without revealing*:

* The training data or model responses
* Specific scores or implementation logic
* The training infrastructure or source data identity

***

### Architecture

The orchestrator integrates three sub-circuits:

* `TrainingQualityVerifier`: Assesses training sample quality and output alignment
* `EthicsComplianceVerifier`: Validates fairness, bias, and red-teaming results
* `ComplianceVerifier`: Verifies privacy policies, data handling, and encryption
* Additionally, it computes a Poseidon-based `master_proof_hash` that binds all subproofs

***

### Inputs

#### Private Inputs (Not revealed during on-chain verification)

**Training**

* `training_samples[n_training_samples*32]`: Flattened matrix of tokenized inputs
* `model_responses[n_training_samples*32]`: Corresponding output tokens
* `quality_scores[n_training_samples]`: Scores computed per sample
* `training_seed`: Randomization value for training
* `model_weights_hash`: Hash of initial model state

**Ethics**

* `bias_test_results[n_ethics_tests]`: Scores from bias detection tests
* `fairness_scores[n_ethics_tests]`: Fairness metrics per test
* `harmful_content_flags[n_ethics_tests]`: Binary flags for unsafe content
* `ethics_training_data_hash`: Hash of ethics-related training dataset

**Compliance**

* `privacy_protection_scores[n_compliance_tests]`
* `data_handling_scores[n_compliance_tests]`
* `encryption_standards[n_compliance_tests]`

**Capability** 

* `capability_scores[n_capability_tests]`
* `performance_benchmarks[n_capability_tests]`

***

#### Public Inputs (Visible to verifiers)

* `agent_id`: Agent unique identifier
* `creator_address`: On-chain wallet address of deployer
* `min_quality_threshold`: Minimum acceptable training quality
* `max_bias_threshold`: Maximum tolerable bias
* `min_privacy_score`: Minimum compliance level
* `required_compliance_standard`: Enum or category selector
* `training_commitment_hash`: Hash of pre-training claims
* `ethics_commitment_hash`: Hash of ethics intent
* `compliance_commitment_hash`: Hash of compliance commitments

#### Public Outputs

* `agent_fully_verified`: Aggregated boolean signal
* `quality_verification_result`: Result from quality sub-circuit
* `ethics_verification_result`: Result from ethics sub-circuit
* `compliance_verification_result`: Result from compliance sub-circuit
* `master_proof_hash`: Combined Poseidon hash of all subproofs
* `verification_timestamp`: Placeholder timestamp; ideally block.time on-chain

***

### Circuit Flow

{% @mermaid/diagram content="graph TD
Start\[Input Signals] --> Q\[TrainingQualityVerifier]
Start --> E\[EthicsComplianceVerifier]
Start --> C\[ComplianceVerifier]
Q --> QO\[quality\_verification\_result]
E --> EO\[ethics\_verification\_result]
C --> CO\[compliance\_verification\_result]
QO --> M\[MasterHasher]
EO --> M
CO --> M
M --> H\[master\_proof\_hash]
QO --> FV\[agent\_fully\_verified]
EO --> FV
CO --> FV" %}

***

### Compilation Instructions

#### Prerequisites

* circom
* snarkjs
* A trusted setup file (e.g. `pot16_final.ptau`)

#### Steps

```bash
# Compile circuit
circom ZKAgentVerificationOrchestrator.circom --r1cs --wasm --sym --c

# Setup powers of tau
snarkjs powersoftau new bn128 16 pot16_0000.ptau
snarkjs powersoftau contribute pot16_0000.ptau pot16_final.ptau

# Generate final proving key
snarkjs groth16 setup ZKAgentVerificationOrchestrator.r1cs pot16_final.ptau circuit_final.zkey

# Export verification key
snarkjs zkey export verificationkey circuit_final.zkey verification_key.json
```

***

### Proof Workflow

#### Input Template (input.json)

```json
{
  "training_samples": [...],
  "model_responses": [...],
  "quality_scores": [...],
  "training_seed": "...",
  "model_weights_hash": "...",
  "bias_test_results": [...],
  "fairness_scores": [...],
  "harmful_content_flags": [...],
  "ethics_training_data_hash": "...",
  "privacy_protection_scores": [...],
  "data_handling_scores": [...],
  "encryption_standards": [...],
  "agent_id": "...",
  "creator_address": "...",
  "min_quality_threshold": 800,
  "max_bias_threshold": 200,
  "min_privacy_score": 850,
  "required_compliance_standard": 1,
  "training_commitment_hash": "...",
  "ethics_commitment_hash": "...",
  "compliance_commitment_hash": "..."
}
```

#### Witness & Proof Generation

```bash
node circuit_js/generate_witness.js circuit.wasm input.json witness.wtns
snarkjs groth16 prove circuit_final.zkey witness.wtns proof.json public.json
snarkjs groth16 verify verification_key.json public.json proof.json
```

***

### Integration

* Used `verification_key.json` in smart contract verifier
* Deployed `ZKAgentVerifier.sol` to validate agent proofs
* Registered verified agents via on-chain hash lookup
* Used `agent_fully_verified == 1` to allow access or monetization
* Reward agents or slash stakes based on verification results

***

### Status

* Stable: Quality, ethics, compliance flows tested
* In Progress: Capability metrics and extensibility
* Planned: Support for recursive proof chaining, dynamic agent roles, explainability circuits