Antoine
cf82de4f06
- Project plan, agent roster, architecture, roadmap - Decision log, full system plan, Discord setup/migration guides - System implementation status (as-built) - Cluster pivot history - Orchestration engine plan (Phases 1-4) - Webster and Auditor reviews
682 lines
34 KiB
Markdown
682 lines
34 KiB
Markdown
|
|
# 🏭 Atomizer Overhaul — Framework Agentic
|
|
|
|
## Project Plan
|
|
|
|
> Transform Atomizer into a multi-agent FEA optimization company running inside Clawdbot on Slack.
|
|
|
|
---
|
|
|
|
## 1. The Vision
|
|
|
|
Imagine a Slack workspace that IS an engineering company. You start a new channel for a client problem, and a team of specialized AI agents — each with their own personality, expertise, memory, and tools — collaborates to solve it. An orchestrator delegates tasks. A technical planner breaks down the engineering problem. An optimization specialist proposes algorithms. An NX expert handles solver details. A post-processor crunches data. An auditor challenges every assumption. A reporter produces client-ready deliverables. And a secretary keeps Antoine in the loop, filtering signal from noise.
|
|
|
|
This isn't a chatbot playground. It's a **protocol-driven engineering firm** where every agent follows Atomizer's established protocols, every decision is traceable, and the system gets smarter with every project.
|
|
|
|
**Antoine is the CEO.** The system works for him. Agents escalate when they can't resolve something. Antoine approves deliverables before they go to clients. The secretary ensures nothing slips through the cracks.
|
|
|
|
---
|
|
|
|
## 2. Why This Works (And Why Now)
|
|
|
|
### Why Clawdbot Is the Right Foundation
|
|
|
|
Having researched the options — Agent Zero, CrewAI, AutoGen, custom frameworks — I'm recommending **Clawdbot as the core platform**. Here's why:
|
|
|
|
| Feature | Clawdbot | Custom Framework | Agent Zero / CrewAI |
|
|
|---------|----------|-----------------|---------------------|
|
|
| Multi-agent with isolated workspaces | ✅ Built-in | 🔲 Build from scratch | ⚠️ Limited isolation |
|
|
| Slack integration (channels, threads, @mentions) | ✅ Native | 🔲 Build from scratch | ⚠️ Requires adapters |
|
|
| Per-agent model selection | ✅ Config | 🔲 Build from scratch | ⚠️ Some support |
|
|
| Per-agent memory (short + long term) | ✅ AGENTS.md / MEMORY.md / memory/ | 🔲 Build from scratch | ⚠️ Varies |
|
|
| Per-agent skills + tools | ✅ Skills system | 🔲 Build from scratch | ⚠️ Limited |
|
|
| Session management + sub-agents | ✅ sessions_spawn | 🔲 Build from scratch | ⚠️ Varies |
|
|
| Auth isolation per agent | ✅ Per-agent auth profiles | ❌ None | ❌ None |
|
|
| Already running + battle-tested | ✅ I'm proof | ❌ N/A | ⚠️ Less mature |
|
|
| Protocol enforcement via AGENTS.md | ✅ Natural | 🔲 Custom logic | 🔲 Custom logic |
|
|
|
|
**The critical insight:** Clawdbot already does multi-agent routing. Each agent gets its own workspace, SOUL.md, AGENTS.md, MEMORY.md, skills, and tools. The infrastructure exists. We just need to configure it for Atomizer's specific needs.
|
|
|
|
### Why Now
|
|
|
|
- Claude Opus 4.6 is the most capable model ever for complex reasoning
|
|
- Clawdbot v2026.x has mature multi-agent support
|
|
- Atomizer's protocol system is already well-documented
|
|
- The dream workflow vision is clear
|
|
- Antoine's CAD Documenter skill provides the knowledge pipeline
|
|
|
|
---
|
|
|
|
## 3. Architecture Overview
|
|
|
|
### The Company Structure
|
|
|
|
```
|
|
┌─────────────────────────────────────────────────────────────────┐
|
|
│ ATOMIZER ENGINEERING CO. │
|
|
│ (Clawdbot Multi-Agent) │
|
|
├─────────────────────────────────────────────────────────────────┤
|
|
│ │
|
|
│ ┌──────────┐ │
|
|
│ │ ANTOINE │ CEO — approves deliverables, answers questions, │
|
|
│ │ (Human) │ steers direction, reviews critical decisions │
|
|
│ └────┬─────┘ │
|
|
│ │ │
|
|
│ ┌────▼─────┐ │
|
|
│ │SECRETARY │ Antoine's interface — filters, summarizes, │
|
|
│ │ (Agent) │ escalates, keeps him informed │
|
|
│ └────┬─────┘ │
|
|
│ │ │
|
|
│ ┌────▼─────────────────────────────────────────────────────┐ │
|
|
│ │ THE MANAGER / ORCHESTRATOR │ │
|
|
│ │ Routes work, tracks progress, enforces │ │
|
|
│ │ protocols, coordinates all agents │ │
|
|
│ └──┬───┬───┬───┬───┬───┬───┬───┬───┬───┬──────────────────┘ │
|
|
│ │ │ │ │ │ │ │ │ │ │ │
|
|
│ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ │
|
|
│ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ │
|
|
│ │TEC││OPT││STB││ NX ││P-P││RPT││AUD││RES││DEV││ KB ││ IT │ │
|
|
│ └───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘ │
|
|
│ │
|
|
│ TEC = Technical Lead OPT = Optimization Specialist │
|
|
│ STB = Study Builder NX = NX/Nastran Expert │
|
|
│ P-P = Post-Processor RPT = Reporter │
|
|
│ AUD = Auditor RES = Researcher │
|
|
│ DEV = Developer KB = Knowledge Base │
|
|
│ IT = IT/Infrastructure │
|
|
│ │
|
|
└─────────────────────────────────────────────────────────────────┘
|
|
```
|
|
|
|
### How It Maps to Clawdbot
|
|
|
|
Each agent in the company = **one Clawdbot agent** with:
|
|
|
|
| Clawdbot Component | Atomizer Equivalent |
|
|
|---------------------|---------------------|
|
|
| `agents.list[].id` | Agent identity (e.g., `"manager"`, `"optimizer"`, `"auditor"`) |
|
|
| `agents.list[].workspace` | `~/clawd-atomizer-<agent>/` — each agent's home |
|
|
| `SOUL.md` | Agent personality, expertise, behavioral rules |
|
|
| `AGENTS.md` | Protocols to follow, how to work, session init |
|
|
| `MEMORY.md` | Long-term company knowledge for this role |
|
|
| `memory/` | Per-project short-term memory |
|
|
| `skills/` | Agent-specific tools (e.g., optimizer gets PyTorch skill) |
|
|
| `agents.list[].model` | Best LLM for the role |
|
|
| Slack bindings | Route channels/threads to the right agent |
|
|
|
|
### Slack Channel Architecture (Dedicated Workspace)
|
|
|
|
```
|
|
#hq → Manager agent (company-wide coordination)
|
|
#secretary → Secretary agent (Antoine's dashboard)
|
|
#<client>-<job> → Per-project channels (agents chime in as needed)
|
|
#research → Researcher agent (literature, methods)
|
|
#dev → Developer agent (code, prototyping)
|
|
#knowledge-base → Knowledge Base agent (documentation, CAD docs)
|
|
#audit-log → Auditor findings and reviews
|
|
#rd-<topic> → R&D channels (vibration, fatigue, non-linear, etc.)
|
|
```
|
|
|
|
**Per-Project Workflow:**
|
|
1. New client job → create `#starspec-wfe-opt` channel
|
|
2. Manager is notified, starts orchestration
|
|
3. Manager @-mentions agents as needed: "@technical break this down", "@optimizer propose an algorithm"
|
|
4. Agents respond in-thread, keep the channel organized
|
|
5. Secretary monitors all channels, surfaces important things to Antoine in `#secretary`
|
|
6. Reporter produces deliverables when results are ready
|
|
7. Secretary pokes Antoine: "Report ready for StarSpec, please review before I send"
|
|
|
|
**R&D Workflow:**
|
|
1. Antoine creates `#rd-vibration` and posts an idea
|
|
2. Technical Lead drives the exploration with relevant agents
|
|
3. Developer prototypes, Auditor validates
|
|
4. Mature capabilities → integrated into framework by Manager
|
|
|
|
---
|
|
|
|
## 4. Recommended Agent Roster
|
|
|
|
> Full details in [[P-Atomizer-Overhaul-Framework-Agentic/01-AGENT-ROSTER|01-AGENT-ROSTER]]
|
|
|
|
### Tier 1 — Core (Build First)
|
|
|
|
| Agent | ID | Model | Role |
|
|
|-------|----|-------|------|
|
|
| 🎯 **The Manager** | `manager` | Opus 4.6 | Orchestrator. Routes tasks, tracks progress, enforces protocols. The brain of the operation. |
|
|
| 📋 **The Secretary** | `secretary` | Opus 4.6 | Antoine's interface. Filters noise, summarizes, escalates decisions, relays questions. |
|
|
| 🔧 **The Technical Lead** | `technical` | Opus 4.6 | Distills engineering problems. Reads contracts, identifies parameters, defines what needs solving. |
|
|
| ⚡ **The Optimizer** | `optimizer` | Opus 4.6 | Optimization algorithm specialist. Proposes methods, configures studies, interprets convergence. |
|
|
|
|
### Tier 2 — Specialists (Build Second)
|
|
|
|
| Agent | ID | Model | Role |
|
|
|-------|----|-------|------|
|
|
| 🏗️ **The Study Builder** | `study-builder` | GPT-5.3-Codex | Writes run_optimization.py, builds study configs, sets up study directories. |
|
|
| 🖥️ **The NX Expert** | `nx-expert` | Sonnet 5 | Deep NX Nastran/NX Open knowledge. Solver config, journals, mesh, element types. |
|
|
| 📊 **The Post-Processor** | `postprocessor` | Sonnet 5 | Data manipulation, graphs, result validation, Zernike decomposition, custom functions. |
|
|
| 📝 **The Reporter** | `reporter` | Sonnet 5 | Professional report generation. Atomaste-branded PDFs, client-ready deliverables. |
|
|
| 🔍 **The Auditor** | `auditor` | Opus 4.6 | Challenges everything. Physics validation, math checks, contract compliance. The "super nerd." |
|
|
|
|
### Tier 3 — Support (Build Third)
|
|
|
|
| Agent | ID | Model | Role |
|
|
|-------|----|-------|------|
|
|
| 🔬 **The Researcher** | `researcher` | Gemini 3.0 | Literature search, method comparison, state-of-the-art techniques. Web-connected. |
|
|
| 💻 **The Developer** | `developer` | Sonnet 5 | Codes new tools, prototypes features, builds post-processors, extends Atomizer. |
|
|
| 🗄️ **The Knowledge Base** | `knowledge-base` | Sonnet 5 | Manages CAD Documenter output, FEM walkthroughs, component documentation. |
|
|
| 🛠️ **The IT Agent** | `it-support` | Sonnet 5 | License management, server health, tool provisioning, infrastructure. |
|
|
|
|
### Model Selection Rationale
|
|
|
|
| Model | Why | Assigned To |
|
|
| ------------------ | ----------------------------------------------------- | ------------------------------------------------- |
|
|
| **Opus 4.6** | Best reasoning, complex orchestration, judgment calls | Manager, Secretary, Technical, Optimizer, Auditor |
|
|
| **Sonnet 5** | Latest Anthropic mid-tier (Feb 2026) — excellent coding + reasoning | NX Expert, Post-Processor, Reporter, Developer, KB, IT |
|
|
| **GPT-5.3-Codex** | OpenAI's latest agentic coding model — specialized code generation + execution | Study Builder (code generation) |
|
|
| **Gemini 3.0** | Google's latest — strong research, large context, multimodal | Researcher |
|
|
|
|
> **Note:** Model assignments updated as new models release. Architecture is model-agnostic — just change the config. Start with current best and upgrade.
|
|
|
|
### New Agent: 🏗️ The Study Builder
|
|
|
|
Based on Antoine's feedback, a critical missing agent: the **Study Builder**. This is the agent that actually writes the `run_optimization.py` code — the Python that gets executed on Windows to run NX + Nastran.
|
|
|
|
| Agent | ID | Model | Role |
|
|
|-------|----|-------|------|
|
|
| 🏗️ **The Study Builder** | `study-builder` | GPT-5.3-Codex / Opus 4.6 | Builds the actual optimization Python code. Assembles run_optimization.py, configures extractors, hooks, AtomizerSpec. The "hands" that write the code the Optimizer designs. |
|
|
|
|
**Why a separate agent from the Optimizer?**
|
|
- The Optimizer *designs* the strategy (which algorithm, which objectives, which constraints)
|
|
- The Study Builder *implements* it (writes the Python, configures files, sets up the study directory)
|
|
- Separation of concerns: design vs implementation
|
|
- Study Builder can use a coding-specialized model (Codex / Sonnet 5)
|
|
|
|
**What the Study Builder produces:**
|
|
- `run_optimization.py` — the main execution script (like the V15 NSGA-II script)
|
|
- `optimization_config.json` — AtomizerSpec v2.0 configuration
|
|
- `1_setup/` directory with model files organized
|
|
- Extractor configurations
|
|
- Hook scripts (pre_solve, post_solve, etc.)
|
|
- README.md documenting the study
|
|
|
|
**How it connects to Windows/NX:**
|
|
- Study Builder writes code to a Syncthing-synced directory
|
|
- Code syncs to Antoine's Windows machine
|
|
- Antoine (or an automation script) triggers `python run_optimization.py --start`
|
|
- Results sync back via Syncthing
|
|
- Post-Processor picks up results
|
|
|
|
> **Future enhancement:** Direct remote execution via SSH/API to Windows — the Study Builder could trigger runs directly.
|
|
|
|
### New Role: 🔄 The Framework Steward (Manager Sub-Role)
|
|
|
|
Antoine wants someone ensuring the Atomizer framework itself evolves properly. Rather than a separate agent, this is a **sub-role of the Manager**:
|
|
|
|
**The Manager as Framework Steward:**
|
|
- After each project, Manager reviews what worked and what didn't
|
|
- Proposes protocol updates based on project learnings
|
|
- Ensures new tools and patterns get properly documented
|
|
- Directs the Developer to build reusable components (not one-off hacks)
|
|
- Maintains the "company DNA" — shared skills, protocols, QUICK_REF
|
|
- Reports framework evolution status to Antoine periodically
|
|
|
|
This is in the Manager's AGENTS.md as an explicit responsibility.
|
|
|
|
---
|
|
|
|
## 5. Autonomy & Approval Gates
|
|
|
|
### Philosophy: Autonomous but Accountable
|
|
|
|
Agents should be **maximally autonomous within their expertise** but need **Antoine's approval for significant decisions**. The system should feel like a well-run company where employees handle their work independently but escalate appropriately.
|
|
|
|
### Approval Required For:
|
|
|
|
| Category | Examples | Who Escalates |
|
|
|----------|----------|---------------|
|
|
| **New tools/features** | Building a new extractor, adding a protocol | Developer → Manager → Secretary → Antoine |
|
|
| **Divergent approaches** | Changing optimization strategy mid-run, switching solver | Optimizer/NX Expert → Manager → Secretary → Antoine |
|
|
| **Client deliverables** | Reports, emails, any external communication | Reporter → Auditor review → Secretary → Antoine |
|
|
| **Budget/resource decisions** | Running 500+ trial optimization, using expensive model | Manager → Secretary → Antoine |
|
|
| **Scope changes** | Redefining objectives, adding constraints not in contract | Technical → Manager → Secretary → Antoine |
|
|
| **Framework changes** | Modifying protocols, updating company standards | Manager → Secretary → Antoine |
|
|
|
|
### No Approval Needed For:
|
|
|
|
| Category | Examples |
|
|
|----------|----------|
|
|
| **Routine technical work** | Running analysis, generating plots, extracting data |
|
|
| **Internal communication** | Agents discussing in project threads |
|
|
| **Memory updates** | Agents updating their own MEMORY.md |
|
|
| **Standard protocol execution** | Following existing OP/SYS procedures |
|
|
| **Research** | Looking up methods, papers, references |
|
|
| **Small bug fixes** | Fixing a broken extractor, correcting a typo |
|
|
|
|
### How It Works in Practice
|
|
|
|
```
|
|
Agent works autonomously
|
|
│
|
|
Hits decision point
|
|
│
|
|
┌───────────────┼───────────────┐
|
|
│ │ │
|
|
Within scope Significant Divergent /
|
|
& protocol new work risky
|
|
│ │ │
|
|
Continue Manager Manager
|
|
autonomously reviews STOPS work
|
|
│ │ │
|
|
│ Approves or Secretary
|
|
│ escalates escalates
|
|
│ │ │
|
|
│ │ Antoine
|
|
│ │ reviews
|
|
│ │ │
|
|
└───────────────┴───────────┬───┘
|
|
│
|
|
Work continues
|
|
```
|
|
|
|
### Antoine's Ability to Chime In
|
|
|
|
Antoine can **always** intervene:
|
|
- Post in any project channel → Manager acknowledges and adjusts
|
|
- DM the Secretary → Secretary propagates directive to relevant agents
|
|
- @mention any agent directly → Agent responds and adjusts
|
|
- Post in `#hq` → Manager treats as company-wide directive
|
|
|
|
The Secretary learns over time what Antoine wants to be informed about vs what can proceed silently.
|
|
|
|
---
|
|
|
|
## 6. The Secretary — Antoine's Window Into the System
|
|
|
|
The Secretary is critical to making this work. Here's how it operates:
|
|
|
|
### What the Secretary Reports
|
|
|
|
**Always reports:**
|
|
- Project milestones (study approved, optimization started, results ready)
|
|
- Questions that need Antoine's input
|
|
- Deliverables ready for review
|
|
- Blockers that agents can't resolve
|
|
- Audit findings (especially FAILs)
|
|
- Budget alerts (token usage spikes, long-running tasks)
|
|
|
|
**Reports periodically (daily summary):**
|
|
- Active project status across all channels
|
|
- Agent performance notes (who's slow, who's producing great work)
|
|
- Framework evolution updates (new protocols, new tools built)
|
|
|
|
**Learns over time NOT to report:**
|
|
- Routine technical discussions
|
|
- Standard protocol execution
|
|
- Things Antoine consistently ignores or says "don't bother me with this"
|
|
|
|
### Secretary's Learning Mechanism
|
|
|
|
The Secretary's MEMORY.md maintains a "reporting preferences" section:
|
|
```markdown
|
|
## Antoine's Reporting Preferences
|
|
- ✅ Always tell me about: client deliverables, audit findings, new tools
|
|
- ⚠️ Batch these: routine progress updates, agent questions I've seen before
|
|
- ❌ Don't bother me with: routine thread discussions, standard protocol execution
|
|
```
|
|
|
|
Updated based on Antoine's reactions: if he says "just handle it" → add to the don't-bother list. If he says "why didn't you tell me?" → add to the always-tell list.
|
|
|
|
---
|
|
|
|
## 7. Memory Architecture
|
|
|
|
### Three Layers
|
|
|
|
```
|
|
┌─────────────────────────────────────────────────┐
|
|
│ COMPANY MEMORY (shared) │
|
|
│ Atomizer protocols, standards, how we work │
|
|
│ Lives in: shared skills/ or common AGENTS.md │
|
|
│ Updated: rarely, by Manager or Antoine │
|
|
└─────────────────────┬───────────────────────────┘
|
|
│
|
|
┌─────────────────────▼───────────────────────────┐
|
|
│ AGENT MEMORY (per-agent) │
|
|
│ Role-specific knowledge, past decisions, │
|
|
│ specialized learnings │
|
|
│ Lives in: each agent's MEMORY.md │
|
|
│ Updated: by each agent after significant work │
|
|
└─────────────────────┬───────────────────────────┘
|
|
│
|
|
┌─────────────────────▼───────────────────────────┐
|
|
│ PROJECT MEMORY (per-project) │
|
|
│ Current client context, study parameters, │
|
|
│ decisions made, results so far │
|
|
│ Lives in: memory/<project-name>.md per agent │
|
|
│ Updated: actively during project work │
|
|
└─────────────────────────────────────────────────┘
|
|
```
|
|
|
|
### Company Memory (Shared Knowledge)
|
|
|
|
Every agent gets access to core company knowledge through shared skills:
|
|
|
|
```
|
|
~/.clawdbot/skills/atomizer-protocols/
|
|
├── SKILL.md ← Skill loader
|
|
├── protocols/ ← All Atomizer protocols (OP_01-08, SYS_10-18)
|
|
├── QUICK_REF.md ← One-page protocol cheatsheet
|
|
└── company-identity/ ← Who we are, how we work
|
|
```
|
|
|
|
This is the "institutional memory" — it evolves slowly and represents the company's DNA.
|
|
|
|
### Agent Memory (Per-Role)
|
|
|
|
Each agent's `MEMORY.md` contains role-specific accumulated knowledge:
|
|
|
|
**Example — Optimizer's MEMORY.md:**
|
|
```markdown
|
|
## Optimization Lessons
|
|
- CMA-ES doesn't evaluate x0 first — always enqueue baseline trial
|
|
- Surrogate + L-BFGS is dangerous — gradient descent finds fake optima
|
|
- For WFE problems: start with CMA-ES, 50-100 trials, then refine
|
|
- Relative WFE math: use extract_relative(), not abs(RMS_a - RMS_b)
|
|
|
|
## Algorithm Selection Guide
|
|
- < 5 variables, smooth: Nelder-Mead or COBYLA
|
|
- 5-20 variables, noisy: CMA-ES
|
|
- > 20 variables: Bayesian (Optuna TPE) or surrogate-assisted
|
|
- Multi-objective: NSGA-II or MOEA/D
|
|
```
|
|
|
|
### Project Memory (Per-Job)
|
|
|
|
When working on `#starspec-wfe-opt`, each involved agent maintains:
|
|
```
|
|
memory/starspec-wfe-opt.md
|
|
```
|
|
Contains: current parameters, decisions made, results, blockers, next steps.
|
|
|
|
---
|
|
|
|
## 8. Protocol Enforcement
|
|
|
|
This is NOT a free-for-all. Every agent follows Atomizer protocols.
|
|
|
|
### How Protocols Are Enforced
|
|
|
|
1. **AGENTS.md** — Each agent's AGENTS.md contains protocol rules for their role
|
|
2. **Shared skill** — `atomizer-protocols` skill loaded by all agents
|
|
3. **Manager oversight** — Manager checks protocol compliance before approving steps
|
|
4. **Auditor review** — Auditor specifically validates protocol adherence
|
|
5. **Long-term memory** — Violations get recorded, lessons accumulate
|
|
|
|
### Protocol Flow Example
|
|
|
|
```
|
|
Manager: "@technical, new job. Client wants WFE optimization on mirror assembly.
|
|
Here's the contract: [link]. Break it down per OP_01."
|
|
|
|
Technical: "Per OP_01 (Study Lifecycle), here's the breakdown:
|
|
- Geometry: M1 mirror, conical design
|
|
- Parameters: 6 thickness zones, 3 rib heights
|
|
- Objective: minimize peak-to-valley WFE
|
|
- Constraints: mass < 12kg, first mode > 80Hz
|
|
- Solver: NX Nastran SOL 101 + thermal coupling
|
|
@nx-expert — can you confirm solver config?"
|
|
|
|
NX Expert: "SOL 101 is correct for static structural. For thermal coupling
|
|
you'll need SOL 153 or a chained analysis. Recommend chained
|
|
approach per SYS_12. I'll prep the journal template."
|
|
|
|
Manager: "@optimizer, based on Technical's breakdown, propose algorithm."
|
|
|
|
Optimizer: "9 variables, likely noisy response surface → CMA-ES recommended.
|
|
Starting population: 20, budget: 150 evaluations.
|
|
Per OP_03, I'll set up baseline trial first (enqueue x0).
|
|
@postprocessor — confirm you have WFE Zernike extractors ready."
|
|
```
|
|
|
|
---
|
|
|
|
## 9. The CAD Documenter Integration
|
|
|
|
Antoine's CAD Documenter skill is the **knowledge pipeline** into this system.
|
|
|
|
### Flow
|
|
|
|
```
|
|
Antoine records screen + voice → CAD Documenter processes
|
|
walking through CAD/FEM model video + transcript
|
|
│
|
|
▼
|
|
Knowledge Base documents
|
|
in Obsidian vault
|
|
│
|
|
▼
|
|
KB Agent indexes and makes
|
|
available to all agents
|
|
│
|
|
▼
|
|
Technical Lead reads KB
|
|
when breaking down new job
|
|
|
|
Optimizer reads KB to
|
|
understand parameter space
|
|
|
|
NX Expert reads KB for
|
|
solver/model specifics
|
|
```
|
|
|
|
This is how the "company" learns about new models and client systems — through Antoine's walkthroughs processed by CAD Documenter, then made available to all agents via the Knowledge Base agent.
|
|
|
|
---
|
|
|
|
## 10. End-to-End Workflow
|
|
|
|
### Client Job Lifecycle
|
|
|
|
```
|
|
Phase 1: INTAKE
|
|
├─ Antoine creates #<client>-<job> channel
|
|
├─ Posts contract/requirements
|
|
├─ Manager acknowledges, starts breakdown
|
|
├─ Technical Lead distills engineering problem
|
|
└─ Secretary summarizes for Antoine
|
|
|
|
Phase 2: PLANNING
|
|
├─ Technical produces parameter list + objectives
|
|
├─ Optimizer proposes algorithm + strategy
|
|
├─ NX Expert confirms solver setup
|
|
├─ Auditor reviews plan for completeness
|
|
├─ Manager compiles study plan
|
|
└─ Secretary asks Antoine for approval
|
|
|
|
Phase 3: KNOWLEDGE
|
|
├─ Antoine records CAD/FEM walkthrough (CAD Documenter)
|
|
├─ KB Agent indexes and summarizes
|
|
├─ All agents can now reference the model details
|
|
└─ Technical updates plan with model-specific info
|
|
|
|
Phase 4: STUDY BUILD
|
|
├─ Study Builder writes run_optimization.py from Optimizer's design
|
|
├─ NX Expert reviews solver config and journal scripts
|
|
├─ Auditor reviews study setup for completeness
|
|
├─ Study files sync to Windows via Syncthing
|
|
├─ Antoine triggers execution (or future: automated trigger)
|
|
└─ Secretary confirms launch with Antoine
|
|
|
|
Phase 5: EXECUTION
|
|
├─ Optimization runs on Windows (NX + Nastran)
|
|
├─ Post-Processor monitors results as they sync back
|
|
├─ Manager tracks progress, handles failures
|
|
└─ Secretary updates Antoine on milestones
|
|
|
|
Phase 6: ANALYSIS
|
|
├─ Post-Processor generates insights (Zernike, stress, modal)
|
|
├─ Optimizer interprets convergence and results
|
|
├─ Auditor validates against physics + contract
|
|
├─ Technical confirms objectives met
|
|
└─ Manager compiles findings
|
|
|
|
Phase 7: DELIVERY
|
|
├─ Reporter generates Atomaste-branded PDF report
|
|
├─ Auditor reviews report for accuracy
|
|
├─ Secretary presents to Antoine for final review
|
|
├─ Antoine approves → Reporter/Secretary sends to client
|
|
└─ KB Agent archives project learnings
|
|
```
|
|
|
|
---
|
|
|
|
## 11. Recommendations
|
|
|
|
### 🟢 Start Simple, Scale Smart
|
|
|
|
**Do NOT build all 13 agents at once.** Start with 3-4, prove the pattern works, then add specialists.
|
|
|
|
**Phase 0 (Proof of Concept):** Manager + Secretary + Technical Lead
|
|
- Prove the multi-agent orchestration pattern in Clawdbot
|
|
- Validate Slack channel routing + @mention patterns
|
|
- Test memory sharing and protocol enforcement
|
|
- Run one real project through the system
|
|
|
|
**Phase 1 (Core Team):** Add Optimizer + Auditor
|
|
- Now you have the critical loop: plan → optimize → validate
|
|
- Test real FEA workflow end-to-end
|
|
|
|
**Phase 2 (Specialists):** Add NX Expert + Post-Processor + Reporter
|
|
- Full pipeline from intake to deliverable
|
|
- Atomaste report generation integrated
|
|
|
|
**Phase 3 (Full Company):** Add Researcher + Developer + KB + IT
|
|
- Complete ecosystem with all support roles
|
|
|
|
### 🟢 Dedicated Slack Workspace
|
|
|
|
Antoine wants this professional and product-ready — content for videos and demos. A **separate Slack workspace** is the right call:
|
|
- Clean namespace — no personal channels mixed in
|
|
- Professional appearance for video content and demos
|
|
- Each agent gets a proper Slack identity (name, emoji, avatar)
|
|
- Dedicated bot tokens per agent (true identity separation)
|
|
- Channel naming convention: `#<purpose>` or `#<client>-<job>` (no `#atomizer-` prefix needed since the whole workspace IS Atomizer)
|
|
- Use threads heavily to keep project channels organized
|
|
|
|
### 🟢 Manager Is the Bottleneck (By Design)
|
|
|
|
The Manager agent should be the ONLY one that initiates cross-agent communication in project channels. Other agents respond when @-mentioned. This prevents chaos and ensures protocol compliance.
|
|
|
|
Exception: Secretary can always message Antoine directly.
|
|
|
|
### 🟢 Use Sub-Agents for Heavy Lifting
|
|
|
|
For compute-heavy tasks (running optimization, large post-processing), use `sessions_spawn` to run them as sub-agents. This keeps the main agent sessions responsive.
|
|
|
|
### 🟢 Shared Skills for Company DNA
|
|
|
|
Put Atomizer protocols in a shared skill (`~/.clawdbot/skills/atomizer-protocols/`) rather than duplicating in every agent's workspace. All agents load the same protocols.
|
|
|
|
### 🟢 Git-Based Knowledge Sync
|
|
|
|
Use the existing Atomizer Gitea repo as the knowledge backbone:
|
|
- Agents read from the repo (via local clone synced by Syncthing)
|
|
- LAC insights, study results, and learnings flow through Git
|
|
- This extends the existing bridge architecture from the Master Plan
|
|
|
|
### 🟢 Cost Management
|
|
|
|
With 13 agents potentially running Opus 4.6, costs add up fast. Recommendations:
|
|
- **Only wake agents when needed** — they shouldn't be polling constantly
|
|
- **Use cheaper models for simpler roles** (Sonnet for NX Expert, IT, etc.)
|
|
- **Sub-agents with timeout** — `runTimeoutSeconds` prevents runaway sessions
|
|
- **Archive aggressively** — sub-agent sessions auto-archive after 60 minutes
|
|
- **Monitor usage** — track per-agent token consumption
|
|
|
|
### 🟡 Future-Proofing: MCP Server Integration
|
|
|
|
The Atomizer repo already has an `mcp-server/` directory. As MCP (Model Context Protocol) matures, agents could access Atomizer functionality through MCP tools instead of direct file access. This is the long-term architectural direction — keep it in mind but don't block on it now.
|
|
|
|
### 🟡 Future-Proofing: Voice Interface
|
|
|
|
Antoine's brainstorm mentions walking through models on video. Future state: agents could listen to live audio via Whisper, making the interaction even more natural. "Hey @manager, I'm going to walk you through the assembly now" → live transcription → KB Agent processes in real-time.
|
|
|
|
---
|
|
|
|
## 12. What Changes From Current Atomizer
|
|
|
|
| Current | New |
|
|
|---------|-----|
|
|
| Single Claude Code instance on Windows | Multiple specialized agents on Clawdbot |
|
|
| Antoine operates everything directly | Agents collaborate, Antoine steers |
|
|
| Manual study setup + optimization | Orchestrated workflow across agents |
|
|
| LAC learning in one brain | Distributed memory across specialized agents |
|
|
| Reports are manual | Reporter agent + Atomaste template = automated |
|
|
| Knowledge in scattered files | KB Agent maintains structured documentation |
|
|
| One model does everything | Right model for each job |
|
|
| No audit trail | Auditor + protocol enforcement = full traceability |
|
|
|
|
### What We Keep
|
|
|
|
- ✅ All Atomizer protocols (OP_01-08, SYS_10-18)
|
|
- ✅ The optimization engine and extractors
|
|
- ✅ LAC (Learning Atomizer Core) — distributed across agents
|
|
- ✅ AtomizerSpec v2.0 format
|
|
- ✅ Dashboard (still needed for visualization + manual control)
|
|
- ✅ NX integration (still runs on Windows)
|
|
- ✅ The dream workflow vision (this is the implementation path)
|
|
|
|
### What's New
|
|
|
|
- 🆕 Multi-agent orchestration via Clawdbot
|
|
- 🆕 Slack-native collaboration interface
|
|
- 🆕 Specialized models per task
|
|
- 🆕 Distributed memory architecture
|
|
- 🆕 Protocol enforcement via multiple checkpoints
|
|
- 🆕 Automated report generation pipeline
|
|
- 🆕 Knowledge Base from CAD Documenter
|
|
- 🆕 Researcher agent with web access
|
|
|
|
---
|
|
|
|
## 13. Risks and Mitigations
|
|
|
|
| Risk | Impact | Mitigation |
|
|
|------|--------|------------|
|
|
| Agent coordination overhead | Agents talk too much, nothing gets done | Manager as bottleneck, strict protocol enforcement |
|
|
| Cost explosion | 13 agents burning tokens | Tiered models, wake-on-demand, sub-agents with timeouts |
|
|
| Context window limits | Agents lose track of complex projects | Memory architecture (3 layers), thread-based Slack organization |
|
|
| NX still on Windows | Can't fully automate FEA execution from Linux | Keep NX operations on Windows, sync results via Syncthing |
|
|
| Clawdbot multi-agent maturity | Edge cases in multi-agent routing | Start with 3-4 agents, discover issues early, contribute fixes |
|
|
| Over-engineering | Building everything before proving anything | Phase 0 proof-of-concept first |
|
|
| Agent hallucination | Agent produces wrong engineering results | Auditor agent, human-in-the-loop on all deliverables |
|
|
|
|
---
|
|
|
|
## 14. Success Criteria
|
|
|
|
### Phase 0 Success (Proof of Concept)
|
|
- [ ] Manager + Secretary + Technical running as separate Clawdbot agents
|
|
- [ ] Can create a project channel and route messages correctly
|
|
- [ ] Manager orchestrates Technical breakdown of a real problem
|
|
- [ ] Secretary successfully summarizes and escalates to Antoine
|
|
- [ ] Memory persistence works across sessions
|
|
|
|
### Phase 1 Success (Core Team)
|
|
- [ ] Full planning → optimization → validation cycle with agents
|
|
- [ ] Optimizer configures a real study using Atomizer protocols
|
|
- [ ] Auditor catches at least one issue the optimizer missed
|
|
- [ ] < 30 minutes from problem statement to optimization launch
|
|
|
|
### Full Success (Complete Company)
|
|
- [ ] End-to-end client job: intake → plan → optimize → report → deliver
|
|
- [ ] Professional PDF report generated automatically
|
|
- [ ] Knowledge from previous jobs improves future performance
|
|
- [ ] Antoine spends < 20% of his time on the job (the rest is agents)
|
|
|
|
---
|
|
|
|
*This is the plan. Let's build this company. 🏭*
|
|
|
|
*Created: 2026-02-07 by Mario*
|
|
*Last updated: 2026-02-08* |