The Architecture of a Personal OS

Table of content

A personal OS has layers, like any operating system. Understand the structure before building.

The stack

┌─────────────────────────────────────┐
│           YOU (the user)            │
├─────────────────────────────────────┤
│         Interface Layer             │
│   (chat, voice, shortcuts, apps)    │
├─────────────────────────────────────┤
│          Agent Layer                │
│   (Claude, GPT, specialized bots)   │
├─────────────────────────────────────┤
│         Memory Layer                │
│   (context, history, knowledge)     │
├─────────────────────────────────────┤
│       Integration Layer             │
│   (calendar, email, tasks, files)   │
├─────────────────────────────────────┤
│          Tool Layer                 │
│   (MCP servers, APIs, automations)  │
└─────────────────────────────────────┘

Layer breakdown

Layer	Components	Purpose
Interface	Chat, voice, shortcuts	How you talk to your OS
Agent	Claude, GPT, specialists	The brain(s) that process requests
Memory	Short-term, long-term, episodic	Persistent context
Integration	Calendar, tasks, email, files	Touching your actual life
Tool	MCP servers, APIs	The hands that execute actions

Memory types

Type	Contains	Example
Short-term	Current context, recent interactions	“We were discussing…”
Long-term	Decisions, preferences, patterns	“You prefer mornings for deep work”
Episodic	What happened when, by topic	“Last Tuesday you decided…”

Implementation: Vector DBs (Pinecone, Chroma), structured storage (Notion), or MCP servers.

Integration layer

Integration	Capabilities
Calendar	Read events, create/modify, protect focus time
Tasks	Create, update status, smart prioritization
Email	Read inbox, draft, categorize, follow up
Files	Access, search, create/edit documents
Notes	Query knowledge base, extract insights

Data flow example

User: "What should I focus on today?"

1. Interface receives input
2. Agent processes request
3. Queries memory: recent context, ongoing projects
4. Queries integrations: calendar, tasks, emails
5. Synthesizes with your stated priorities
6. Returns: prioritized focus list with reasoning

Single vs multi-agent

Approach	Pros	Cons
Single agent	Simple, unified context	Limited by one model
Multi-agent	Specialized, more capable	Complex orchestration

Start with single agent. Add specialists when you hit limits.

Build incrementally

Phase	Add
Week 1	Claude Code + basic MCP servers
Week 2	Memory (episodic-memory plugin)
Week 3	One integration (calendar or tasks)
Week 4	Another integration
Ongoing	Refine based on actual usage

Each layer should provide value independently before adding the next.

Next: Principles for AI Delegation