I built Hermes Agent Assistant, a lightweight agentic AI system designed to demonstrate how modern AI agents can be structured using a modular architecture instead of a simple, single-prompt response model. The system takes an abstract user task, breaks it down into structured steps using a dedicated planner, executes those steps sequentially via an execution engine, utilizes targeted tools, and stores the interaction context in a persistent memory system. Most AI applications today are simple wrappers around LLMs that rely on a single input-output loop. I wanted to understand and demonstrate how production-grade, autonomous agent systems operate internally. Specifically, I wanted to explore how: Hermes Agent is my architecture simulation built to solve this problem in a highly accessible, lightweight, and scalable format. The codebase is split cleanly into four autonomous components that mirror real-world AI agent meshes: User Request (e.g., /run?task=...) │ ▼ ┌───────────────────────────┐ │ PLANNER │ ➔ Slices abstract goals into └─────────────┬─────────────┘ structured, sequential steps. │ ▼ ┌───────────────────────────┐ │ EXECUTOR │ ➔ Orchestrates task completion └─────────────┬─────────────┘ by processing each step. │ ▼ ┌───────────────────────────┐ │ TOOLS LAYER │ ➔ Provides functional utilities └─────────────┬─────────────┘ (simulated web search, logic, maths). │ ▼ ┌───────────────────────────┐ │ MEMORY SYSTEM │ ➔ Persists execution logs statefully └───────────────────────────┘ into local JSON storage. srv-d88revegvqtc73bdj380 (Render Infrastructure Node)Unlike traditional, rigid APIs or simple conversational chatbots, Hermes Agent: POST /run?task=search AI agents HTTP/1.1
Host: hermes-agent-tanush.onrender.com
{
"task": "search AI agents",
"plan": [
"analyze request parameters", "query tool registry for search utilities", "summarize agent data structural output"
],
"result": "final structured output successfully generated and written to persistent storage."
}
Planner , Executor , and Critic agents working collaboratively with separate system prompts.