Agentic Oracles

import { createOracle } from "@ixo/oracle-agent-sdk";

// Initialize oracle
const oracle = await createOracle({
  type: "VerificationOracle",
  model: "causal-inference-v1",
  confidence: 0.95,
  evidenceTypes: ["telemetry", "image", "location"]
});

// Configure verification rules
await oracle.setVerificationRules({
  requiredEvidence: ["deviceId", "timestamp", "location"],
  thresholds: {
    minimumConfidence: 0.9,
    maximumDeviation: 0.05
  },
  constraints: {
    timeWindow: "24h",
    locationPrecision: 6
  }
});

// Create verification session
const session = await oracle.createVerification({
  subject: "did:ixo:entity/123",
  context: {
    location: {
      latitude: 51.5074,
      longitude: -0.1278,
      precision: 6
    },
    timestamp: Date.now()
  }
});

// Process data
const result = await session.verify(data, {
  evidence: supportingEvidence,
  rules: customRules
});

// Generate attestation
const attestation = await session.generateAttestation({
  result,
  method: "Ed25519Signature2020"
});

// Create causal model
const model = await oracle.createCausalModel({
  variables: ["intervention", "outcome", "context"],
  relationships: [
    { from: "intervention", to: "outcome" },
    { from: "context", to: "outcome" }
  ]
});

// Train model
await model.train(historicalData);

// Analyze causality
const analysis = await model.analyze({
  intervention: interventionData,
  outcome: outcomeData,
  context: contextData
});

// Generate insights
const insights = await model.generateInsights(analysis);

// Get prescriptive recommendations
const recommendations = await model.generateRecommendations({
  goal: "Increase outcome by 20%",
  constraints: {
    budget: 100000,
    timeframe: "3 months"
  }
});