Antoine
3289a76e19
- 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
203 lines
8.4 KiB
Markdown
203 lines
8.4 KiB
Markdown
---
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tags:
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- Project/Atomizer
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- Agentic
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- Decisions
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up: "[[P-Atomizer-Overhaul-Framework-Agentic/MAP - Atomizer Overhaul Framework Agentic]]"
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date: 2026-02-07
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status: active
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---
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# 📋 Decision Log — Atomizer Overhaul: Framework Agentic
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---
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## DEC-A001: Use Clawdbot Multi-Agent (Not Custom Framework)
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**Date:** 2026-02-07
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**Status:** 🟡 Proposed (awaiting Antoine's review)
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**Proposed by:** Mario
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**Options Considered:**
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| Option | Pros | Cons |
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|--------|------|------|
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| A) Clawdbot Multi-Agent | Already running, Slack native, proven patterns, per-agent isolation | Tied to Clawdbot's architecture, some multi-agent features still maturing |
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| B) Agent Zero | Designed for multi-agent | Less mature, no Slack native support, would need integration |
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| C) CrewAI | Purpose-built for agent teams | Limited isolation, less flexible memory, Slack needs adapters |
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| D) Custom Framework | Full control | Massive build effort, reinventing wheels |
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**Recommendation:** Option A — Clawdbot Multi-Agent
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**Rationale:** We already have a running Clawdbot instance with Slack integration. Multi-agent routing is a built-in feature. The infrastructure exists; we just need to configure it. Building from scratch would take months and delay the actual value.
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---
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## DEC-A002: Phased Rollout (Not Big Bang)
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**Date:** 2026-02-07
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**Status:** 🟡 Proposed
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**Proposed by:** Mario
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**Decision:** Start with 3 agents (Phase 0), scale to 12 over 10 weeks.
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**Rationale:** Risk of over-engineering. Multi-agent coordination has emergent complexity — better to discover issues with 3 agents than debug 12 simultaneously.
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---
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## DEC-A003: Manager as Communication Bottleneck
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**Date:** 2026-02-07
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**Status:** 🟡 Proposed
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**Proposed by:** Mario
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**Decision:** Only the Manager initiates cross-agent work in project channels. Other agents respond when @-mentioned, but don't independently reach out to each other.
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**Rationale:** Prevents "agent storm" where agents endlessly ping each other. Manager maintains control and traceability. This can be relaxed later if agents prove reliable.
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---
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## DEC-A004: Single Gateway, Multiple Agents
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**Date:** 2026-02-07
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**Status:** 🟡 Proposed
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**Proposed by:** Mario
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**Decision:** Run all agents on one Clawdbot gateway process.
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**Rationale:** Simpler to manage, enables `sessions_send` between agents, single config. Can split later if resources demand it.
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---
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## DEC-A005: Model Tiering Strategy
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**Date:** 2026-02-07
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**Status:** ❌ Superseded by DEC-A008
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**Proposed by:** Mario
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**Original Decision (superseded):** Tiered model approach with older models.
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**Replaced by:** DEC-A008 — use latest models (Sonnet 5, GPT-5.3-Codex, Gemini 3.0).
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**Rationale still valid:** Cost optimization via tiering. Not every role needs Opus 4.6. Match model capability to role complexity.
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---
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## DEC-A006: Dedicated Slack Workspace
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**Date:** 2026-02-07
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**Status:** ✅ Accepted (Antoine's request)
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**Proposed by:** Antoine
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**Decision:** Create a dedicated Slack workspace for Atomizer Engineering — separate from Antoine's personal workspace.
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**Rationale:** This is a product. Antoine will make videos, demos. Needs to look professional and clean. No personal channels mixed in. Each agent gets proper identity with avatar + name.
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---
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## DEC-A007: Study Builder Agent (Separate from Optimizer)
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**Date:** 2026-02-07
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**Status:** ✅ Accepted
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**Proposed by:** Antoine + Mario
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**Decision:** Add a Study Builder agent that writes the actual Python code (run_optimization.py), separate from the Optimizer who designs the strategy.
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**Rationale:** Optimizer designs, Study Builder implements. Clean separation. Study Builder can use a coding-specialized model (GPT-5.3-Codex). Code must run on Windows with NX.
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---
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## DEC-A008: Use Latest Models (Sonnet 5, Codex 5.3, Gemini 3.0)
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**Date:** 2026-02-07
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**Status:** ✅ Accepted
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**Proposed by:** Antoine
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**Decision:** Use cutting-edge models: Opus 4.6 for reasoning, Sonnet 5 (when released) for technical work, GPT-5.3-Codex for code generation, Gemini 3.0 for research.
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**Rationale:** This is a showcase product. Use the best available. Architecture is model-agnostic — swap models via config.
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---
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## DEC-A009: Autonomous with Approval Gates
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**Date:** 2026-02-07
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**Status:** ✅ Accepted
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**Proposed by:** Antoine
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**Decision:** Agents are maximally autonomous for routine work but require Antoine's approval for: new tools/features, divergent approaches, client deliverables, scope changes, framework modifications.
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**Rationale:** Balance between efficiency and control. Antoine doesn't want to micromanage but needs to steer. Secretary learns what to escalate over time.
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---
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## DEC-A010: Framework Steward = Manager Sub-Role
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**Date:** 2026-02-07
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**Status:** ✅ Accepted
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**Proposed by:** Mario
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**Decision:** The Manager agent also serves as Framework Steward — ensuring the Atomizer framework evolves properly, learnings are captured, and protocols improve over time. Not a separate agent.
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**Rationale:** Avoids agent bloat. Manager already has the visibility across all projects. Framework evolution is a management responsibility.
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---
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## DEC-A011: Windows Execution — Syncthing + Manual Script Launch
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**Date:** 2026-02-08
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**Status:** ✅ Accepted
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**Proposed by:** Mario | **Decided by:** Antoine
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**Decision:** Syncthing delivers job files to Windows. Antoine runs `run_optimization.py` manually to kick off the full iteration loop. The script handles all iterations autonomously (NX solve → extract → evaluate → next trial). No SSH/API needed for Phase 1.
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**Rationale:** Matches existing Atomizer workflow. Simple, reliable. Can upgrade to remote exec later if manual trigger becomes a bottleneck.
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---
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## DEC-A012: Separate Clawdbot Gateway (Docker)
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**Date:** 2026-02-08
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**Status:** ✅ Accepted
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**Proposed by:** Mario | **Decided by:** Antoine
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**Decision:** Atomizer gets a **separate Clawdbot gateway** running in Docker on the T420. Mario's personal Clawdbot stays native (systemd). Eventually, Atomizer moves to a dedicated machine.
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**Rationale:** Complete isolation — independent config, Slack workspace, restarts. Mario's personal assistant is unaffected. T420 is the incubator, not the final home.
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**Note:** Docker is not yet installed on T420 — needs to be set up before Phase 0.
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---
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## DEC-A013: Single Bot with Per-Agent Identity
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**Date:** 2026-02-08
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**Status:** ✅ Accepted
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**Proposed by:** Mario | **Decided by:** Antoine
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**Decision:** Single Clawdbot Slack bot app managing all agents. Each agent has its own name, emoji, and personality via Clawdbot's identity system. The UX should feel like interacting with individual people — organic, @-mentionable — even though one process orchestrates everything behind the scenes.
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**Rationale:** Don't over-complicate the plumbing. One "god" process, but the Slack experience feels like a real team. Implementation simplicity with great UX.
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## DEC-A014: KB Agent — Semi-Auto Ingestion + Inherited CAD Documenter Skill
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**Date:** 2026-02-08
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**Status:** ✅ Accepted
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**Proposed by:** Mario + Antoine
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**Decision:** Semi-automatic — KB Agent flags new CAD Documenter output, Antoine approves before ingestion. The skill architecture uses inheritance:
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- **Base layer:** General Atomaste CAD Documenter skill (lives in Mario's workspace) — whisper transcription, frame extraction, engineering KB
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- **Atomizer layer:** KB Agent over-specializes with Atomizer-specific behaviors — auto-tagging part numbers, linking to optimization studies, extracting FEA parameters, feeding into LAC system
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The general skill remains a broad Atomaste tool; Atomizer's version adds domain-specific intelligence on top.
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**Rationale:** CAD Documenter is too valuable to lock inside Atomizer. Keep the general tool for all Atomaste work; let Atomizer extend it.
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---
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## DEC-A015: Nightly Memory Digestion (“Restore → Sort → Dream → Resolve”)
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**Date:** 2026-02-12
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**Status:** ✅ Accepted
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**Proposed by:** Manager | **Decided by:** Antoine
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**Decision:** Adopt the nightly memory methodology (Restore → Sort → Dream → Resolve), run automatically at **00:00 America/Toronto**, and post the brief to **#all-atomizer-hq**.
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**Rationale:** Ensures daily work compounds into durable memory + actionable next steps, while preventing noise from polluting long-term context.
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---
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## Pending Decisions
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*No pending decisions at this time.*
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---
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*Created: 2026-02-07 by Mario*
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