Agentic Oracles Architecture

Build intelligent prediction networks using AI-powered oracles with decentralized identity

IXO Oracles are autonomous AI agents that provide verifiable services through decentralized identity, secure data storage, and blockchain integration.

Key Components

Identity Layer

DID-based domains with verifiable credentials

AI Layer

Langraph framework for cognitive workflows

Storage Layer

Encrypted Matrix rooms and vector stores

Blockchain Layer

Impact Hub integration via MultiClient SDK

Oracle Domain

DID Configuration

DID Document

{
  "@context": ["https://www.w3.org/ns/did/v1"],
  "id": "did:ixo:oracle/123",
  "verificationMethod": [{
    "id": "#key-1",
    "type": "Ed25519VerificationKey2018",
    "controller": "did:ixo:oracle/123",
    "publicKeyBase58": "..."
  }],
  "service": [{
    "id": "#ai",
    "type": "LangraphService",
    "serviceEndpoint": "https://oracle.emerging.eco/ai"
  }]
}

from emerging import Oracle, Domain

# Create oracle domain
oracle = Oracle.create(
    name="CleanCookingOracle",
    capabilities=["verification", "prediction"],
    controllers=["did:ixo:org/456"]
)

# Configure services
oracle.add_service(
    type="LangraphService",
    endpoint="https://oracle.emerging.eco/ai"
)

Memory Storage

from emerging import VectorStore, Matrix

# Initialize vector store
store = VectorStore.create(
    type="weaviate",
    index="oracle_memory",
    schema={
        "class": "Verification",
        "properties": ["context", "decision"]
    }
)

# Create Matrix room
room = Matrix.create_room(
    oracle_id="did:ixo:oracle/123",
    encryption=True
)

AI Workflows

Langraph Integration

Verification Flow

from emerging import Langraph, Flow

# Create verification flow
flow = Flow.create(
    name="device_verification",
    input_schema={
        "measurements": "DeviceTelemetry",
        "baseline": "BaselineData"
    }
)

# Add processing nodes
flow.add_node(
    "validate_data",
    type="DataValidation",
    rules=validation_rules
)

flow.add_node(
    "analyze_patterns",
    type="AnomalyDetection",
    model=anomaly_model
)

flow.add_node(
    "verify_compliance",
    type="ComplianceCheck",
    standards=["GS_MMECD_1.0"]
)

# Deploy flow
flow.deploy()

```python

```javascript
import { Langraph, Flow } from '@emerging/sdk';

// Create verification flow
const flow = await Flow.create({
  name: 'device_verification',
  inputSchema: {
    measurements: 'DeviceTelemetry',
    baseline: 'BaselineData'
  }
});

// Add processing nodes
await flow.addNode({
  name: 'validate_data',
  type: 'DataValidation',
  rules: validationRules
});

await flow.addNode({
  name: 'analyze_patterns',
  type: 'AnomalyDetection',
  model: anomalyModel
});

await flow.addNode({
  name: 'verify_compliance',
  type: 'ComplianceCheck',
  standards: ['GS_MMECD_1.0']
});

// Deploy flow
await flow.deploy();