Building Decentralized AI Oracles: FBA Consensus and Privacy-Preserving Workflows with Chainlink CRE

How I Built 3 Chainlink CRE Workflows in One Week — FBA Oracle, Privacy Payroll & DeFi Intelligence at 3 AM in Secunderabad

Engineer Venkateshwar Rao Nagala developed a three-tier Chainlink CRE system for the 2026 Convergence Hackathon. The architecture successfully utilizes Federated Byzantine Agreement (FBA) to synchronize Claude Opus and GPT-4o outputs for prediction market settlement.

Why This Matters

Standard AI oracles are susceptible to high-confidence hallucinations, which can lead to incorrect automated settlements in prediction markets. By applying the Stellar-based Federated Byzantine Agreement protocol to LLMs, this project ensures that consensus requires quorum intersection, effectively blocking autonomous settlement when models disagree.

Furthermore, the implementation of Chainlink’s Confidential Runtime Environment (CRE) addresses the critical security challenge of managing API secrets. By handling credentials at the Decentralized Oracle Network (DON) level and using confidential HTTP clients, developers can perform complex off-chain computations and DeFi market intelligence without exposing sensitive keys to the public blockchain.

Key Insights

• FBA consensus achieved 97% confidence by intersecting results from Claude Opus (99%) and GPT-4o (95%) regarding Fed rate decisions.
• Privacy-preserving batch payroll settlements record only a Keccak256 hash on-chain, keeping recipient data within CRE confidential compute.
• The hyperliquid_mcp workflow queries 229 markets via CRE Confidential HTTP, utilizing base64-encoded bodies for request handling.
• Deployment requires strict secret isolation; sharing a single secrets.yaml across multiple workflows causes cross-workflow conflicts in the CRE runtime.
• MetaMask’s ‘Imported Account’ priority in Remix can lead to deployment failures if the primary funded account is not manually selected.
• The CRE HTTPClient requires request bodies to be base64-encoded using Buffer.from(), as btoa() is unavailable in the environment.

Working Examples

Fetching secrets at the DON level and passing them to node mode for FBA consensus calculation.

const claudeSecret = runtime.getSecret({ id: config.claudeApiKeySecretName }).result();
const openaiSecret = runtime.getSecret({ id: config.openaiApiKeySecretName }).result();
const result = runtime.runInNodeMode(
  runFbaConsensus,
  consensusIdenticalAggregation<SettlementResult>()
)(claudeApiKey, openaiApiKey).result();

Properly encoding the request body for the CRE HTTPClient.

body: Buffer.from(
  JSON.stringify({ type: "meta" })
).toString("base64"), // CRE requires base64 encoded body

Practical Applications

• Prediction Markets: Use FBA to prevent single-model hallucinations from settling markets, triggering human review on model disagreement.
• Privacy-Preserving Payroll: Record Keccak256 batch hashes on Ethereum Sepolia to prove settlement while masking individual salary amounts and recipient addresses.
• DeFi Market Intelligence: Monitor bid-ask spreads across 200+ perpetual markets using Hyperliquid’s API without exposing credentials on-chain.

References:

• https://dev.to/tenalirama2025/how-i-built-3-chainlink-cre-workflows-in-one-week-fba-oracle-privacy-payroll-defi-4nha
• https://github.com/tenalirama2025-creator/chainlink-prediction-settler