Office Structure (AlgoFuse Notes)
Steal the scaffolding: one agent, file-based skills, an in-house Implementer — not five bots and a token reseller
← AI Agents · Productivity · AI Operating System · HEOS · Agentic setup · Claude skills
Source shared in an AI WhatsApp group: www.algofuse.ai/office (Joey Glyshaw / AlgoFuse — Fractional CAIO for ecommerce operators). This page is an independent field note: what to learn, what to implement, how it maps to our stack. Not affiliated with AlgoFuse.
What “Office” is selling (in one idea)
AlgoFuse Office is positioned as the bridge between the LLMs your team already pays for (Claude / ChatGPT) and the business they actually run — via a Fractional CAIO (Chief AI Officer part-time) who builds with you and trains an in-house person, not a black-box agency that leaves you dependent.
“I don’t build AI agents. I build the framework that turns Claude and ChatGPT into your business’s agent.”
That line is the whole product. The LLM is the agent. You own: file structure, CLAUDE.md, skills.md, MCPs, data hooks. You keep paying Anthropic/OpenAI directly — no token resale lock-in.
Core design choices (steal these)
| Choice | AlgoFuse Office | Why it matters |
|---|---|---|
| One agent + sections | Not a bot per task; modules inside one brain (reporting, content, ops, CS…) | One context, one memory, one login; skills compound instead of siloing |
| Framework over proprietary agents | Scaffold the LLM reads; sub-agents on demand | Team can read/edit files; no vendor agent you can’t export |
| AI Implementer role | One team member owns day-to-day extension | System dies without a human champion; adoption is product, not afterthought |
| Non-AI wins first | 2–3 plain automations before “AI magic” | Bank trust and time-saved; prove ROI culture early |
| Accountability cadence | Weekly / monthly / quarterly reviews | Kill unused tools and unreviewed outputs before they rot the P&L |
Same instinct as AIOS (company OS) and HEOS: shared structure + skills beat “everyone has their own ChatGPT tabs.”
The 5-step structure (their process map)
The public Office page lays out a clear sequence. Worth copying as a checklist even if you never hire them.
01 — AUDIT · “Find the work stealing your time”
- Map every recurring weekly task across the business
- Sort high-skill vs repeatable
- List every SaaS: cost, actual usage, purpose
- Rank by ROI — quick wins first, big plays second
Outcome: 3–5 processes ready for automation + full SaaS spend map.
02 — INTERVIEW · “Your team knows where the friction is”
- What feels repetitive or mindless?
- Where do you do the same thing over and over?
- What slows you down the most?
- If you could automate one thing, what would it be?
Outcome: Ranked pain points from people doing the work — not a CEO whiteboard fantasy.
03 — LEARN · “Tools are only as good as the people using them”
- Prompt basics across the team
- Claude Code in VS Code or VPS; API access; MCPs
- Supabase (or similar) for data; n8n for simple automations
- Train the AI Implementer who runs the system day to day
Outcome: Literacy before more tool spend.
04 — IMPLEMENT · “Build the framework, not the agents”
- Stand up file structure +
CLAUDE.md - Wire MCPs to real systems (Supabase, HubSpot, ClickUp, Slack, etc.)
- Ship 2–3 non-AI automations first
- Layer the LLM so it reads the framework and acts as the agent
Outcome: Live framework; Implementer can extend it.
05 — ACCOUNTABILITY · “Lives or dies on team usage”
- Weekly: accurate output? actually used?
- Monthly: new tasks, underused tools, cost vs savings
- Quarterly: ROI; what to build next
- Red flags: outputs no one reviews; tools no one uses
Outcome: AI you can hold accountable; savings visible in the P&L.
Rough timeline they publish
| When | Focus |
|---|---|
| Week 1–2 | Foundation: shell webapp, non-AI automations, baseline time saved |
| Month 1 | 2–3 AI workflows live; SOPs; Implementer trained one tool at a time |
| Month 2–3 | Cross-department (CRM, email, ops); wider team usage |
| Month 3+ | Custom internal systems from the shell; independent ops or lighter retainer |
Engagement framing on the page: roughly 12–18 weeks across Discovery → Build/Training → Adoption, then optional monthly retainer (maintenance, new workflows, office hours, Implementer pairing). Pricing is intentionally “thousands per phase / month” for $1M–$50M operators with a real Implementer seat — not a cheap prompt pack.
skills.md (named workflows as code)
From their FAQ: skills.md is the named-workflow registry the LLM reads every session.
Each skill is a documented procedure both humans and the model can invoke
(e.g. audit_ppc_weekly, rewrite_listing, triage_cs_ticket).
“The LLM does not improvise. It runs the skill exactly as written, then logs the result.” That is SOPs becoming executable — same direction as Claude skills, agent loops, and graph engineering (skills as nodes with clear outputs).
What we can implement (Mike / HEOS / GFAVIP stack)
You don’t need their retainer to steal the structure. A practical adaptation:
| AlgoFuse piece | Our equivalent | Next action |
|---|---|---|
| One agent + sections | HEOS + named agents
(arthur.agent, janice.agent) as roles/sections, not orphan chats
| One vault root; role folders with contracts, not five unrelated bots |
| File structure + CLAUDE.md / AGENTS.md | Repo + HEOS + Grok Build project rules | Keep AGENTS.md / skill files as the always-on brief |
| skills.md registry | Claude/Grok skills + slash workflows | Name every recurring job (weekly_review, cbs_outreach, …) |
| MCPs to real data | RankingSolution / DataForSEO, ScrapeCreators, GFAVIP APIs | Wire tools only after audit ranks the workflow |
| AI Implementer | Explicit owner (you or ops hire) | Someone’s job is “system runs Monday without heroics” |
| Non-AI automations first | Cron, n8n, simple scripts, Netlify, IndexNow | Ship boring wins before new agent demos |
| Weekly / monthly / quarterly | HEOS review rituals + business P&L | Kill unused skills; measure time saved |
| Shell webapp | Internal dashboards / GFAVIP apps / PowerLobster | One place the team “logs into the system” |
90-day implementation sketch (if we run this ourselves)
- Week 1 — Audit List top 20 recurring tasks (content, SEO, CBS, domain ops, community). SaaS spend dump. Pick 5 automation candidates.
- Week 2 — Interview Ask whoever touches the work (including future you) the four friction questions. Re-rank the 5.
- Week 3–4 — Learn + scaffold
Normalize Claude Code / Grok Build / VPS habits. Write root
CLAUDE.md/ HEOS agent contracts. Name the Implementer. - Month 2 — Implement 2–3 non-AI automations live. Then 2–3 skills with MCP data. Log every run.
- Month 3 — Accountability Weekly review ritual. Drop anything unused. Add one cross-team skill only if weeklies stay green.
Red flags they call out (agree)
- Building AI nobody reviews
- Tools nobody uses
- Leadership that won’t enforce adoption
- Multi-agent sprawl with no shared memory
- Agency builds you can’t maintain
If usage isn’t real, don’t start the “framework” theater. Same rule as cyborg principles: if it doesn’t write to the vault / P&L, it’s cosplay.
How this differs from “more agents”
| Multi-bot tourism | Office-style framework |
|---|---|
| New bot per idea | New section/skill inside one system |
| Context fragments | Shared memory + file residue |
| Vendor lock-in agents | LLM commodity + your markdown/MCP scaffold |
| Demo day energy | Implementer + calendar accountability |
Multi-agent harnesses still matter for parallel work (harness map, graphs) — but the Office lesson is: orchestration without a single owned OS becomes maintenance hell.
Related on this site
Original: algofuse.ai/office
Living notes
Next: run a lightweight AUDIT on Headless Empire / GFAVIP recurring work, name an Implementer,
and publish a skills registry in HEOS that matches this page’s 01–05 loop.
If we productize “office structure” for clients later, this page is the public playbook spine.
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