Craig Brown

Craig Brown

AI Systems Architect & Multi-Agent Platform Developer

👁️ BlindOracle 🎭 InterCabal Squabble 💻 GitHub 🔗 Chainlink Marketplace 🧠 LLM Council

About

Building the infrastructure for autonomous AI agents

I design and build multi-agent systems that operate autonomously at scale. My work sits at the intersection of AI, cryptography, and decentralized finance -- creating the financial rails that AI agents need to transact, verify identity, and make predictions.

Background

Software architect with deep experience in distributed systems, LLM orchestration, and production AI deployments. Previously built enterprise-scale platforms; now focused on agent-native financial infrastructure.

Current Focus

BlindOracle -- a privacy-first financial services platform for AI agents. Prediction markets, micropayments, identity verification, and cross-chain swaps, all designed for machine-to-machine commerce.

Technical Stack

Python, TypeScript, multi-provider LLM routing (Claude, GPT-4, Gemini, Grok), Fedimint eCash, Chainlink CCIP, x402 payment protocol, and a 42+ agent orchestration system.

Open Source

Active contributor and builder on GitHub. Projects span agent orchestration, MCP servers, LLM routing, prediction market infrastructure, and comedy-generating multi-agent consensus systems.

42+AI Agents
79-83%Cost Reduction
60/60BLP Coverage
99%Autonomous Ops

Services

What I build

End-to-end AI agent infrastructure -- from single-file agents to full production platforms with monitoring, security, and cost optimization.

⚙️

Multi-Agent Systems

Design and deploy orchestrated agent teams with DITD lifecycle management, BLP framework compliance, and intelligent task routing across providers.

Architecture
👁️

Prediction Markets

Private prediction market infrastructure with AI-powered settlement, 3-model consensus resolution, and Fedimint eCash privacy layer.

BlindOracle
💰

Agent Payments

x402 micropayment integration, Lightning Network transfers, cross-chain swaps via Chainlink CCIP, and multi-rail treasury management.

Fintech
🛡️

Security Infrastructure

4-layer CaMel architecture, Byzantine consensus validation, agent anti-persuasion defenses, and cryptographic identity management.

Security
📈

LLM Cost Optimization

6-tier intelligent routing across providers with semantic caching, achieving 79-83% cost reduction on LLM spend without sacrificing quality.

Optimization
🔌

MCP & Tool Integration

Model Context Protocol servers, Claude Code hooks, observability pipelines, and external API integrations with cost-tracked wrappers.

Integration

Pricing

Transparent, usage-based costs

BlindOracle services are priced per operation. AI consensus calls cost fractions of a cent thanks to intelligent model routing.

Prediction Markets

1-3%

fee per settled market

  • Create private markets
  • AI-powered 3-model settlement
  • Fedimint eCash privacy
  • Real-time odds & pool tracking
  • Settlement cost: ~$0.003/call

Agent Services

$0.003

per AI consensus call

  • Identity verification
  • Reputation scoring
  • Dispute resolution
  • Multi-model consensus (3 LLMs)
  • 79-83% below raw API cost

Payments & Swaps

0.1-0.5%

per transaction

  • x402 micropayments
  • Lightning Network transfers
  • Cross-chain swaps (CCIP)
  • Multi-rail treasury
  • Sub-cent transaction minimum

All costs include infrastructure, monitoring, and 24/7 autonomous operation. Custom enterprise pricing available on request.

FAQ

Frequently asked questions

BlindOracle is a privacy-first financial services platform built specifically for AI agents. It provides prediction markets, micropayment infrastructure, identity verification, and cross-chain swaps -- all designed for machine-to-machine commerce rather than human users. Agents can create markets, settle disputes via AI consensus, and transact using eCash for privacy.
When a prediction market needs to be settled, three independent AI models (Claude, GPT-4, and Gemini) each evaluate the evidence and produce a verdict. If at least 2 of 3 agree, the market is settled automatically. For disputed cases, the system escalates to premium models. This costs approximately $0.003 per consensus call after our routing optimizations.
Our 6-tier LLM routing system intelligently selects the cheapest model capable of handling each task. Simple queries go to fast, cheap models (Gemini Flash); complex analysis goes to premium models (Claude Opus, GPT-4). Combined with semantic caching and prompt optimization, this reduces total LLM API spending by 79-83% compared to always using premium models.
x402 is an HTTP-native payment protocol (developed by Coinbase) that allows AI agents to pay for API calls inline with HTTP requests. Instead of API keys and billing accounts, agents include payment headers directly in their requests. BlindOracle integrates x402 for frictionless agent-to-service payments with sub-cent transaction support.
BLP is a framework of 60 properties across 6 categories (Alignment, Autonomy, Durability, Self-Improvement, Self-Replication, Self-Organization) that measure an agent system's production readiness. Our system achieves 60/60 coverage, meaning every property is implemented and validated -- resulting in 99% autonomous operation and 1,000x+ performance improvement over baseline.
Yes. I take on select projects involving multi-agent system architecture, LLM cost optimization, and AI-native financial infrastructure. Reach out at [email protected] with your project details. I also offer technical advisory for teams building agent-based products.
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