Recur is a decentralized data infrastructure protocol built to support the next generation of autonomous AI agents. The platform coordinates data collection, refinement, and incentives to ensure agents have access to high-quality, continuously improving datasets.
Rather than focusing on static training pipelines, Recur is designed for systems that learn, adapt, and operate over time.
AI performance is constrained by data quality and availability. Existing data pipelines are centralized, expensive, and poorly suited for autonomous agents that require ongoing feedback, iteration, and domain-specific information.
As agents become more independent and long-lived, static datasets and closed labeling systems become insufficient.
Recur introduces a decentralized approach to data infrastructure, coordinating contributors, incentives, and quality control through crypto-native mechanisms. By aligning economic incentives with data quality, Recur enables agents to access fresh, relevant data while continuously improving performance over time.
This architecture supports a wide range of agent-driven use cases, from consumer applications to enterprise automation.
As AI shifts from static models to autonomous agents, data infrastructure becomes the defining constraint. Recur positions itself as foundational infrastructure for the agent economy—powering learning systems that evolve in production rather than stopping at deployment.
