Hermes Agent is INSANE...

2026-04-27 · Source: Wes Roth · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, extended

Summary

This content introduces Hermes Agent, an open-source AI agent, and details its application in building and benchmarking a "Gravity Well" simulation. The simulation involves AI models piloting ships around four suns, managing fuel, momentum, and collisions, with the goal of staying within a moving circle. The entire simulation, including the website and ship control scripts, was built by large language models (LLMs) under human direction. The author uses Hermes Agent to automate the testing and benchmarking of various LLMs, such as Claude Opus 4.5, Claude Son 4.6, GPT 5.4, GPT 5.5 Pro, Grok 420, Deepseek V4 Pro, and Gemi 3.1 Pro, across 20 iterations and 100 different seeds. The content also provides a manual installation guide for Hermes Agent on a Virtual Private Server (VPS) using Hostinger, covering OS selection (Ubuntu LTS), provider configuration (News Portal or OpenRouter), and safety considerations for running AI agents without approval sandboxes.

Key takeaway

For AI Engineers evaluating new large language models, you should consider developing custom, iterative benchmarks like the "Gravity Well" simulation. This approach provides a more accurate assessment of an LLM's ability to understand instructions, generate functional code, and self-improve over time, offering insights beyond standard, potentially over-optimized benchmarks. Deploying agents like Hermes on a VPS allows for automated, round-the-clock testing and rapid iteration.

Key insights

AI agents can autonomously build complex simulations and benchmark LLM performance through iterative code generation and testing.

Principles

• Iterative refinement improves LLM-generated code performance.
• Custom benchmarks reveal true LLM capabilities beyond standard tests.
• Persistent memory enhances AI agent learning over time.

Method

The method involves providing LLMs with English game descriptions, allowing them to generate and iteratively refine ship control scripts over 20 trials, then running the best script across 100 varied simulation seeds to assess performance.

In practice

• Deploy AI agents on VPS for continuous, automated operations.
• Use Hermes Agent to orchestrate multiple LLM sub-agents for complex tasks.
• Configure agents with Docker for sandboxed execution and safety.

Topics

• Hermes Agent
• Gravity Well Simulation
• LLM Benchmarking
• AI Agent Orchestration
• Virtual Private Server Deployment

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Wes Roth.