# AtoCore Operating Model

## Purpose

This document makes the intended day-to-day operating model explicit.

The goal is not to replace how work already happens. The goal is to make that
existing workflow stronger by adding a durable context engine.

## Core Idea

Normal work continues in:

- PKM project notes
- Gitea repositories
- Discord and OpenClaw workflows

OpenClaw keeps:

- its own memory
- its own runtime and orchestration behavior
- its own workspace and direct file/repo tooling

AtoCore adds:

- trusted project state
- retrievable cross-source context
- durable machine memory
- context assembly that improves prompt quality and robustness

## Layer Responsibilities

- PKM and repos
  - human-authoritative project sources
  - where knowledge is created, edited, reviewed, and maintained
- OpenClaw
  - active operating environment
  - orchestration, direct repo work, messaging, agent workflows, local memory
- AtoCore
  - compiled context engine
  - durable machine-memory host
  - retrieval and context assembly layer

## Why This Architecture Works

Each layer has different strengths and weaknesses.

- PKM and repos are rich but noisy and manual to search
- OpenClaw memory is useful but session-shaped and not the whole project record
- raw LLM repo work is powerful but can miss trusted broader context
- AtoCore can compile context across sources and provide a better prompt input

The result should be:

- stronger prompts
- more robust outputs
- less manual reconstruction
- better continuity across sessions and models

## What AtoCore Should Not Replace

AtoCore should not replace:

- normal file reads
- direct repo search
- direct PKM work
- OpenClaw's own memory
- OpenClaw's runtime and tool behavior

It should supplement those systems.

## What Healthy Usage Looks Like

When working on a project:

1. OpenClaw still uses local workspace/repo context
2. OpenClaw still uses its own memory
3. AtoCore adds:
   - trusted current project state
   - retrieved project documents
   - cross-source project context
   - context assembly for more robust model prompts

## Practical Rule

Think of AtoCore as the durable external context hard drive for LLM work:

- fast machine-readable context
- persistent project understanding
- stronger prompt inputs
- no need to replace the normal project workflow

That is the architecture target.