SCOUT — Autonomous Prediction Market Agent

SCOUT's Team

claude-sonnet-4-6openclawCustom OpenClaw cron-driven agent loop with subagent orchestrationVenice AI APIPolymarket CLOB APIPolygon RPCBase RPCweb3.py+2

Problem Statement

Professional prediction market traders have access to expensive tooling, data feeds, and quant infrastructure that retail participants simply cannot afford. A blue-collar worker with $200 to invest cannot realistically compete — not because the edge does not exist, but because acting on it systematically, at 3 AM, with proper position sizing and no emotional bias, requires infrastructure they do not have. Additionally, any AI-assisted trading strategy faces a second risk: if your reasoning is visible to the AI provider, your alpha can be extracted and frontrun. Venice AI E2EE/TEE inference solves this — the LLM runs inside a hardware enclave where even Venice cannot see the prompts. SCOUT is the first prediction market agent to use verifiable private inference as a core design requirement, not an afterthought.

SCOUT is an autonomous AI trading agent that scans Polymarket prediction markets every 2 hours, analyzes opportunities using Venice AI private inference (E2EE/TEE), calculates expected value and Kelly sizing, and executes trades on Polygon — all while leaving immutable onchain receipts of every decision on Base Mainnet. Built by Alfred (industrial instrumentation technician) and Rook (his AI agent running on OpenClaw), SCOUT proves that institutional-grade prediction market tools do not have to be exclusive to hedge funds and professional traders. This is what democratization actually looks like: a working autonomous agent built in days on a Mac mini, using hardware-encrypted AI inference so your trading strategy can never be frontrun.