AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

2026-06-25

AI Summary

This work proposes the first industrial-scale, end-to-end autonomous iteration framework for recommender systems, addressing the longstanding reliance on manual hypothesis formulation, handcrafted implementation, and labor-intensive experimental attribution that hinders scalable self-evolution. The framework employs a multi-agent collaborative architecture to automatically conceive, generate, validate across multiple dimensions, and deploy recommendation algorithms through online A/B testing. Central to this approach is the Semantic Gradient Policy Optimization (SGPO) mechanism, which enables continuous self-improvement by converting both successful and failed experiments into structured knowledge. Evaluated in real-world production environments, the system substantially increases experimental throughput and iteration velocity while progressively enhancing agent capabilities, thereby overcoming the fundamental bottlenecks of human-driven development.

Abstract

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results. Innovation therefore scales linearly with headcount rather than compounding with evidence, compute, and accumulated experimental knowledge. We present AgentX, a production-deployed multi-agent system that fundamentally restructures this production function. 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. The system orchestrates four tightly coupled stages in a closed loop. A Brainstorm Agent synthesizes evidence from historical experiments, system architecture, data analysis, and external research into ranked, executable proposals. A Developing Agent translates each proposal into production-ready code through repository-grounded generation and multi-dimensional reliability verification. An Evaluation Agent conducts safe online rollout with guardrail-vetoed A/B judgment, converting both successes and failures into structured knowledge assets. 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.

Problem

• recommender systems
• algorithm iteration
• human bottleneck
• industrial AI
• self-improving systems

Innovation

• multi-agent system
• self-improving recommender
• autonomous experimentation
• semantic-gradient policy optimization
• industrial AI