DroxenBot: A Real-Time Autonomous Agent for On-Chain Detection and Ranking of Emerging Digital Assets

This paper presents DroxenBot, a real-time autonomous monitoring agent designed to detect, evaluate, and rank emerging digital assets in the Solana ecosystem. The system integrates blockchain data acquisition, decentralized exchange analytics, wallet-behavior analysis, multi-stage risk filtering, and heuristic ranking to identify potentially significant market activity among newly launched and trending digital assets. It organizes qualifying assets into structured monitoring tiers and delivers automated alerts through Telegram. The system provides monitoring and decision support; it does not autonomously execute trades. The research documents approximately nine months of live deployment and iterative system refinement. It describes changes in signal volume and operator-estimated hit rate as filtering and ranking mechanisms were refined. The reported performance estimates are based on ongoing observational assessment. No single fixed quantitative success criterion was applied consistently throughout the deployment, and no baseline comparison was conducted. The findings should therefore be interpreted as observational engineering findings rather than statistically validated predictive performance or evidence of trading profitability. The paper describes the system architecture, implementation approach, deployment experience, limitations, and directions for further research. Proposed extensions include reproducible evaluation protocols, baseline comparisons, machine-learning ranking, temporal and graph-based analysis, and cross-chain evaluation. Supporting research documentation and supplementary materials are available in the associated GitHub repository. The complete production implementation, proprietary filtering and scoring configuration, and raw historical deployment logs are not publicly released.