ATOCore/docs/operating-model.md

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.