AgentX: A self-improving multi-agent system for automated recommendation algorithm iteration
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[Submitted on 25 Jun 2026]
Summary
This paper presents AgentX, a production-deployed multi-agent system that automates and self-improves the recommendation algorithm iteration process in industrial settings. The system replaces the traditional manual, engineer-dependent workflow with four autonomous stages: a Brainstorm Agent that generates ranked proposals from historical data and research, a Developing Agent that writes production-ready code, an Evaluation Agent that conducts safe A/B testing, and a Harness Evolution layer (SGPO) that continuously improves the agents themselves through semantic-gradient updates. The system aims to scale innovation beyond human headcount limitations by creating a self-evolving development engine for recommendation systems.
Source
bskyAgentX: A self-improving multi-agent system for automated recommendation algorithm iterationarxiv.orgKey quotes
· 3 pulledInnovation therefore scales linearly with headcount rather than compounding with evidence, compute, and accumulated experimental knowledge.
AgentX operates as a self-evolving development engine: it autonomously generates, implements, evaluates, and learns from recommendation experiments at a scale and pace that no manual workflow can sustain.
A Harness Evolution layer (SGPO) then distills execution trajectories into semantic-gradient updates that continuously sharpen the agents themselves — making the system not merely automated, but self-improving.
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