LLM-Based AI Agent Architecture: A New Kind of Personal Computer on Your Device

Tue, 05 May 2026 00:00:00 +0000

LLM-Based AI Agent Architecture: A New Kind of Personal Computer on Your Device

For a long time, we’ve thought of AI as a “chatbot.”

But if you step back and look from a systems architecture perspective, you’ll find that a truly mature AI agent looks more like a new kind of personal computer — one that lives on your device.

It has:

A compute core
Memory
A file system
A software system
Input/output devices
Long-term storage

The difference is:

Its core isn’t a traditional CPU. It’s an LLM.

Part 1: The LLM Engine — A “CPU” Without Memory

The LLM itself has no long-term memory.

It’s more like an inference engine:

Receives input
Reads context
Performs reasoning
Produces output
Then “forgets”

It cannot natively remember things that happened in the past.

Therefore:

The LLM itself is more like a CPU than a complete agent.

It only handles computation.

What makes AI “seem like it knows you” is the context provided externally.

Part 2: Context — The AI Agent’s Memory

If the LLM is the CPU,
then Context is the AI’s memory.

And this memory should be split into two layers.

1. Global Context

This layer belongs to the entire agent.

It records:

User preferences
Long-term goals
Habitual behaviors
Persona settings
Persistent rules
Historical knowledge

For example:

“User prefers Markdown”
“User is learning AI Agents”
“User habitually writes in Chinese”

This information shapes agent behavior over time.

2. Session Context

This layer belongs only to the current conversation.

For example:

The current topic under discussion
The current article structure
The most recent rounds of dialogue
Temporary reasoning results

It’s more like temporary memory during program execution.

The Context Window Is Essentially a “Memory Limit”

An LLM’s Context Window isn’t unlimited.

This means:

History can’t accumulate indefinitely
Information gets more expensive as the window fills
Past the limit, content must be compressed

Therefore:

An agent must manage memory like an operating system:

Compress history
Summarize
Clear low-priority information
Transfer long-term data
Dynamically load needed data

Therefore:

The Context Window is essentially the AI’s memory capacity.

Part 3: Markdown Files — The Agent’s Hard Drive

Long-term data shouldn’t stay in the context window.

Otherwise:

Costs keep rising
Inference slows down
The context balloons rapidly

Therefore:

Long-term memory should live in a file system.

And one very natural form is Markdown files.

For example:

Notes
Project materials
Journals
World-building
User profiles
Writing material
Long-term knowledge bases

All of these can be stored as Markdown.

This means:

Traditional Computer	AI Agent
Hard Drive	Markdown File System

Markdown has one enormous advantage:

It can be read by AI and directly by humans alike.

Therefore:

Humans can edit it
AI can process it
Git can version-control it
Files can sync
It persists even without AI

This creates something like:

“A shared knowledge space between humans and AI.”

Part 4: Skills — Software Installed on AI

Future AI agents won’t only have “knowledge.”

They’ll also have “skills.”

For example:

Writing Skill
Programming Skill
Video Editing Skill
Data Analysis Skill
Project Management Skill

These Skills might be composed of:

Prompts
Workflows
Python code
MCP configurations
Tool invocation rules

They are like:

Software installed on the AI.

Therefore:

Traditional Computer	AI Agent
Software / App	Skill

Skills can be:

Installed
Uninstalled
Updated
Shared
Combined

In the future there may even be:

Skill Stores
Skill Marketplaces
Open-source Skill communities

Part 5: Input/Output — More Than Just Text

One of the biggest misconceptions about traditional chatbots is that people think AI only communicates through text.

In reality, future AI agents will have a complete multimodal I/O system.

Input

AI can read:

Text
Voice
Images
Video
Camera feeds
Files
Screen content
Device state

Output

AI can generate:

Text
Voice
Images
Video
Automated actions
Control commands

Therefore:

An AI agent is fundamentally a new interaction layer.

The Complete System: A “Von Neumann-style” AI Computer

When you put the whole architecture together:

Traditional Computer	AI Agent
CPU	LLM Engine
Memory	Context
Hard Drive	Markdown File System
Software	Skill
Input Device	Multimodal Input
Output Device	Multimodal Output

You’ll find:

It increasingly resembles a real computer.

Except:

This computer isn’t built around a GUI.

It’s built around:

“Language comprehension and reasoning.”

The Operating System: A Personal AI OS

In the future, every person’s device may host a persistent AI Agent.

One that:

Understands you
Remembers you
Helps you work
Manages your knowledge
Schedules your Skills
Operates your devices
Grows alongside you over time

At that point:

What we use might no longer just be:

Windows
macOS
Android

But rather:

A new kind of personal AI operating system, with LLM at its core.

And the chat box we use today

may only be the earliest prototype of this new era.

References

Park, Joon Sung et al.
MemGPT: Towards LLMs as Operating Systems
arXiv:2310.08560
https://arxiv.org/abs/2310.08560
Wang, Lei et al.
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
arXiv:2312.03815
https://arxiv.org/abs/2312.03815

Agent on Code Plato

LLM-Based AI Agent Architecture: A New Kind of Personal Computer on Your Device

LLM-Based AI Agent Architecture: A New Kind of Personal Computer on Your Device

Part 1: The LLM Engine — A “CPU” Without Memory

Part 2: Context — The AI Agent’s Memory

1. Global Context

2. Session Context

The Context Window Is Essentially a “Memory Limit”

Part 3: Markdown Files — The Agent’s Hard Drive

Part 4: Skills — Software Installed on AI

Part 5: Input/Output — More Than Just Text

Input

Output

The Complete System: A “Von Neumann-style” AI Computer

The Operating System: A Personal AI OS

References