Comprehensive research report on AI agents for prediction markets, proposing a four-layer architecture (data, analysis, execution, learning) for autonomous trading systems. Maps the ecosystem of existing agents, compares Kelly criterion vs fixed-fraction bet sizing, surveys arbitrage strategies across platforms, and outlines business models (agent-as-a-service, liquidity mining, data sales). Argues that AI agents will become the dominant market participants within two years, transforming prediction markets from retail-driven speculation into infrastructure for probabilistic information.

Traces the quantitative finance toolkit from backtesting (Deflated Sharpe Ratio, combinatorial purged cross-validation) through factor models, Black-Litterman portfolio optimization, Bayesian regime detection, and machine learning, then argues each technique transfers directly to prediction markets. The core claim is that prediction markets are the purest testing environment for investment theory because binary resolution eliminates the unobservable noise that obscures strategy quality in traditional finance. Uses LMSR's mathematical identity with the softmax function to bridge quant finance and prediction market pricing.

Provides a quantitative framework for distinguishing gambling from systematic trading on prediction markets, including a five-point diagnostic and three trader archetypes classified by profitability. Explains why Polymarket's CLOB creates renewable structural arbitrage by design, and covers Kelly position sizing, adverse selection measurement via fill quality, and probability term structure as tools for building a repeatable edge.

First-person account from a seven-figure prediction market trader. Covers strategy as a 'bond mule' (locking capital for small premiums on near-resolution markets), OSINT information sources (non-English media, Telegram), fractional Kelly sizing, and trading personality-driven markets. Notes edge has compressed as space matured.