Subject: My attempt at an "OS-inspired" AI architecture
Hi HN,
I'm a Product Manager, not a systems engineer. I built AI Station Navigator as a proof-of-concept to solve a specific problem I faced: Context Pollution.
When using AI agents for complex tasks, the context window gets cluttered quickly, causing the model to hallucinate or get confused.
To solve this, I designed this project using a Computer Architecture Analogy. I treated the agent system like a traditional OS to better manage resources and isolation.
Here is the mapping I used to design the system:
-- CPU approx. LLM (Claude)
The raw computing power driving the capabilities.
-- Kernel approx. Orchestration Layer (Claude Code + CLAUDE.md)
Handles intent recognition, task scheduling, and context management.
-- Processes approx. Sub-Agents
The core feature: Execution runs in isolated sub-agents. When a task is done, the sub-agent "dies," freeing up context. This prevents the main thread from getting polluted.
-- Applications approx. Skills (GitHub Repos)
"App Store-style" installation of tools via GitHub links.
-- Drivers approx. MCP + Hooks
Standardized interfaces for external tools and system automation.
-- Runtime approx. Portable Environment
Self-contained Python/Node environment (no installation hell).
The Code: https://github.com/canishowtime/ai-station-navigator/
I'd love to hear your thoughts on this architectural approach.