We’ve successfully submitted our project to the Colosseum Breakout Hackathon!
Visit
LogoLogo
WebsiteX
  • About Minos
  • Minos SDK
    • Core functions
      • Core functions: Trade
      • Core functions: Vaults
    • Websocket
    • CLI: Sandbox strategy testing
  • Multi-Agent Orchestration
    • Agents
      • Data Collection Agent
      • Analytical Agent
      • Strategy Development Agent
      • Trade Implementation Agent
      • Performance Monitoring Agent
    • OpenAI Swarm and Langchain
  • Terminal: On-chain tasks execution
  • Autonomous trading agents
    • Ariadne: Autonomous Trade Scanner
    • Deucalion: Advanced Copy Trading
    • Androgeus: Rule-Based Trade Executor
    • How to use
    • Funds
  • Agent Vaults
    • Creation process
    • Cancelation process
    • Fees
    • Token
  • DevOps
    • Servers and GPU Infrastructure
    • Retrieval-Augmented Generation (RAG) Operations
      • Iterative Retrieval-Generation
      • Embedding Models
    • Conversational History Management
    • Agent Monitoring & Performance Analytics
  • Telegram terminal
Powered by GitBook

Minos AI 2025

On this page
  1. Multi-Agent Orchestration
  2. Agents

Performance Monitoring Agent

PreviousTrade Implementation AgentNextOpenAI Swarm and Langchain

Last updated 5 days ago

The Performance Monitoring Agent evaluates the system’s operational performance and reliability. It tracks quantitative metrics, including trade profitability, gas cost efficiency, portfolio drawdowns, and Sharpe ratio, alongside qualitative factors like strategy execution consistency. Using statistical process control, the agent detects anomalies, such as smart contract failures, market manipulations, or data pipeline disruptions. We integrated a real-time dashboard for visualizing performance metrics, supplemented by automated alerts for critical issues. The agent generates comprehensive logs, enabling post-trade analysis and model retraining. A challenge was managing the volume of performance data generated by Solana’s high transaction rate; we optimized storage with time-series databases like InfluxDB. This agent’s feedback loop drives continuous system refinement.