Safactory: A Scalable Agent Factory for Trustworthy Autonomous Intelligence
For researchers building autonomous agents, Safactory offers a unified framework for systematic risk discovery and continuous improvement, but the contribution is primarily architectural and lacks empirical validation.
Safactory integrates three platforms (parallel simulation, trustworthy data, autonomous evolution) into a unified evolutionary pipeline for trustworthy autonomous intelligence, addressing fragmentation in agent infrastructure. No concrete performance numbers are provided.
As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.