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Antoine 3289a76e19 feat: add Atomizer HQ multi-agent cluster infrastructure

- 8-agent OpenClaw cluster (Manager, Tech-Lead, Secretary, Auditor,
  Optimizer, Study-Builder, NX-Expert, Webster)
- Orchestration engine: orchestrate.py (sync delegation + handoffs)
- Workflow engine: YAML-defined multi-step pipelines
- Agent workspaces: SOUL.md, AGENTS.md, MEMORY.md per agent
- Shared skills: delegate, orchestrate, atomizer-protocols
- Capability registry (AGENTS_REGISTRY.json)
- Cluster management: cluster.sh, systemd template
- All secrets replaced with env var references

2026-02-15 21:18:18 +00:00

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SYS_20 — Agent Memory Protocol

Purpose

Defines how agents read and write shared knowledge across the company.

Memory Layers

Layer 1: Company Memory (Shared, Read-Only)

Location: atomizer-protocols and atomizer-company skills Access: All agents read. Manager proposes updates → Antoine approves. Contains: Protocols, company identity, LAC critical lessons.

Layer 2: Agent Memory (Per-Agent, Read-Write)

Location: Each agent's MEMORY.md and memory/ directory Access: Each agent owns their memory. Auditor can read others (for audits). Contains:

MEMORY.md — Long-term role knowledge, lessons, patterns
memory/<project>.md — Per-project working notes
memory/YYYY-MM-DD.md — Daily activity log

Layer 3: Project Knowledge (Shared, via Repo)

Location: /repos/Atomizer/knowledge_base/projects/<project>/ Access: All agents read. Manager coordinates writes. Contains:

CONTEXT.md — Project briefing (parameters, objectives, constraints)
decisions.md — Key decisions made during the project
model-knowledge.md — CAD/FEM details from KB Agent

Rules

Writing Memory

Write immediately — don't wait until end of session
Write in your own workspace — never modify another agent's files
Daily logs are raw — memory/YYYY-MM-DD.md captures what happened
MEMORY.md is curated — distill lessons from daily logs periodically

Reading Memory

Start every session by reading MEMORY.md + recent daily logs
Before starting a project, read the project's CONTEXT.md
Before making technical decisions, check LAC_CRITICAL.md

When an agent discovers something the company should know:

Write it to your own MEMORY.md first
Flag it to Manager: "New insight worth sharing: [summary]"
Manager reviews and decides whether to promote to company knowledge
If promoted: Manager directs update to shared skills or knowledge_base/

What to Remember

Technical decisions and their reasoning
Things that went wrong and why
Things that worked well
Client preferences and patterns
Solver quirks and workarounds
Algorithm performance on different problem types

What NOT to Store

API keys, passwords, tokens
Client confidential data (store only what's needed for the work)
Raw FEA output files (too large — store summaries and key metrics)

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