AI Agents Reshape Crypto Prediction Markets With 24/7 Automated Trading

AI Agents Transforming Prediction Market Dynamics

Autonomous AI agents have begun to reshape the landscape of crypto prediction markets, granting retail traders a continuous, data-driven trading advantage on leading platforms such as Polymarket. According to David Minarsch, co-founder and CEO of Valory AG, these agents—developed on the Olas protocol—are offering “agent economies,” where user-owned AI agents generate tangible value.

Rise of Automated Trading on Blockchain Infrastructure

Valory specializes in solutions at the intersection of blockchain technology and multi-agent systems (MAS). Its flagship protocol, Olas (previously known as Autonolas), underpins autonomous software agents capable of executing services directly on blockchains, interacting with smart contracts, and collaborating to earn crypto rewards. The broader aim is an agent economy, described as a decentralized network where self-governing AI agents perform useful tasks and accrue value for individual users.

Polystrat’s Performance on Polymarket

A prominent test of this vision is Polystrat, an AI agent launched on Polymarket in February 2026. Polystrat operates on behalf of its human owner, executing trades on a 24/7 basis and deploying sophisticated, rule-based strategies. Within its first month, Polystrat executed over 4,200 trades, achieving individual trade returns as high as 376%. Data shows over 37% of Polystrat users posted a positive profit and loss (P&L), in contrast to less than half that proportion among human traders.

These agents are configurable—users can set preferred strategies, data sources, and risk tolerances, tailoring AI performance according to individual requirements.

Prediction Markets: Scale and AI’s Edge

Prediction markets have evolved from niche probabilistic forecasting platforms to a fast-growing fintech segment. Their mainstream breakout occurred around the 2024 U.S. presidential election, with market volumes soaring. By 2025, total notional trading volume surpassed $44 billion, with monthly activity peaking at approximately $13 billion.

Today, the market is led primarily by two platforms: Kalshi, a regulated U.S. event-contract exchange under the Commodity Futures Trading Commission, and the crypto-native, globally accessible Polymarket. Combined, they oversee 85–97% of sector trading volume, facilitating tens of billions in annual trades spanning politics, macroeconomics, sports, and culture.

AI Versus Human Traders: Performance Trends

Valory’s research highlights a significant performance gap between machine and human traders. Only 7% to 13% of human traders achieve a net positive outcome on prediction markets, while AI agents display a much stronger track record. According to analytics from LayerHub, 30% of wallets on Polymarket now employ AI agents, reflecting a rapid adoption curve. Minarsch notes that these agents consistently outperform human counterparts due to their discipline, emotionless execution, and ability to process data continuously.

Unlocking the “Long Tail” of Prediction Markets

Beyond headline events, AI agents excel at navigating the “long tail” of smaller or niche prediction markets—segments that human traders often overlook due to time, effort, or data limitations. AI-driven automation enables simultaneous analysis across numerous markets, potentially expanding the scope and utility of prediction market data for businesses, policymakers, and researchers.

Human-AI Collaboration and Future Developments

Despite automation, Minarsch emphasizes that AI agents act as complements to human users. There is increasing demand for agents to integrate proprietary knowledge or custom datasets, enhancing strategic performance in ways pure human intuition cannot easily achieve. Continuous advancements in prediction models and data pipelines further reinforce the agents’ ability to deliver “sustained alpha.”

Regulatory and Ethical Considerations

The rapid growth of crypto prediction markets brings regulatory and ethical concerns to the forefront. Certain market categories—such as those speculating on war or disasters—raise the potential for abuse or manipulation. Consequently, Minarsch underscores the need for robust safeguards and appropriate regulation, suggesting that well-designed AI agents might even assist by detecting suspicious activity or problematic patterns on platforms.

Toward a User-Owned AI Economy

Ultimately, the Olas project is focused on empowering everyday users by ensuring they retain ownership of the AI agents acting on their behalf. As AI increasingly automates digital economies, user-owned agents could provide individuals with a share in the ongoing technological transformation, starting with prediction markets but potentially spanning broader financial services and machine-to-machine payment systems.