Agent-R^2 for Synthesizing Realistic Productivity Training Data
November 4, 2026
Agent-R^2 is a framework that synthesizes training data for realistic productivity workflows by grouping real-source data. It moves beyond LLM-generated files to provide authentic file environments and deliverable-level evaluations for agents.
HOW THIS AFFECTS YOU
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builderYou can train agents for professional workflows using data that reflects real intent and file contexts.
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founderThis opens opportunities to build agents that handle complex, long-horizon office and productivity tasks.