Azuro Partners with Olas to Develop AI Agents for Enhanced Prediction Markets

by Ouess

Azuro, the leading liquidity layer for on-chain predictions, is partnering with Olas, the primary network for autonomous AI agents, to enhance its ecosystem with advanced AI capabilities.

As a frontrunner in sport and entertainment prediction markets within EVM chains, Azuro hosts over 25 active apps, with more in development. Its unique liquidity pool design, advanced tooling, and oracle solutions enable apps to launch without upfront costs or ongoing operational expenses. This allows developers to focus their resources on user acquisition, engagement, and monetization.

Azuro has seen rapid and consistent growth in transaction volumes over the past year, surpassing $350 million, and generating over $3 million in protocol revenue, making it the top revenue-generating protocol on Polygon.

Olas provides a composable stack for developing autonomous services, facilitating the creation of advanced AI agents represented on-chain by smart contracts. This collaboration gives Olas AI technology access to Azuro’s fast-growing prediction markets ecosystem.

The Azuro AI agent will be designed to learn how to predict sports event outcomes accurately. By utilizing data from Azuro markets, the agent will autonomously analyze and operate within these prediction markets, providing data-driven insights and enhancing predictive capabilities for all participants.

“We are setting the basis for the development of AI agents that can effectively integrate and operate within the Azuro ecosystem. Long term, this integration could unleash a new era in market analysis and empower new applications for prediction markets beyond sports, such as political forecasting, and other entertainment events,” stated Rossen, a core contributor at Azuro.

This partnership marks an exciting development for Azuro’s developers and users. In addition to creating the AI agent, Azuro will host a hackathon to provide tools and frameworks for developing new autonomous agents and expanding use cases in prediction markets.

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