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document · 004 · federation madarX OS · company agent network
architecture atlas console

AI generates. Humans approve. Departments collaborate.

# From one AI assistant to a governed company-wide AI workforce.

madarX OS can become the operating layer where every employee and every department has its own team of agents — while cross-department work moves through policies, approvals, queues, memory boundaries, and audit trails.

strategic advantage

vs Hermes
  not only personal execution — institutional coordination
vs Paperclip
  not only control-plane records — real governed runtime
core moat
  federated agent teams with approval-first collaboration
scale path
  local-first → team deployment → enterprise network

Current operating model Federated organization

Departments collaborate through a broker, policy engine, approvals, events, and audit trails — not uncontrolled agent-to-agent calls.

request broker policy engine event bus approval ledger
One-line product claim positioning

madarX OS turns individual AI agents into an approval-first, audit-friendly operating system for entire companies.

Department agent network click a department
Finance department tenant

manager
  Finance Controller Agent
memory
  budgets · revenue · forecasts
tools
  supabase_read · sheet_export · finance_api
approval
  Restricted financial data

Budget Analyst Revenue Reporter Forecast Agent Approval Controller
Cross-department communication simulator marketing asks finance, legal asks engineering, support asks ops
Source department
Target department
Actor role
Request type
Data sensitivity
Trust mode
approval-required

### Marketing → Finance

Risk tier: high. Approver: Finance manager.

- cross-department boundary
- confidential sensitivity

Records created cross_department_requests activity_log approval_requests audit_logs department_queue_jobs
Request flow brokered, not free-for-all
01 Request created

Source agent submits intent, business reason, target department, requested fields, sensitivity, and urgency.

02 Broker normalizes

Request Broker attaches tenant_id, department_id, actor_id, task_id, correlation_id, and target queue.

03 RBAC + policy

Policy engine checks role, department, data owner, scope, risk tier, rate limits, and active approvals.

04 Approval decision

Low-risk can auto-approve by policy; sensitive finance/legal/customer/tool actions pause for manager approval.

05 Target agent executes

Target department manager assigns a specialist agent with scoped memory and tool permissions.

06 Result returned

Only approved fields or artifact links are returned. Raw restricted data can stay inside the owning department.

07 Audit + replay

activity_log, audit_logs, approval_requests, tool_egress, and task events preserve the full chain.

Why this is safer boundary design

Agents do not directly scrape another department’s memory or tools. They request work through a broker. The owning department decides what can be returned.

- Raw finance data can remain inside Finance.
- Marketing receives approved summaries, not unrestricted tables.
- Legal opinions require Legal manager approval.
- External sends and destructive tool calls remain irreversible_external.

Federation architecture six-layer model
01 People + departments employees · department heads · executives · contractors
02 Department tenancy tenant_id · department_id · workspace_id · memory namespace · budget profile
03 Governance plane RBAC · policies · approval rules · rate limits · audit rules
04 Coordination plane request broker · task router · event bus · queues · heartbeat worker
05 Intelligence plane manager agents · specialist agents · Hermes · Claude Code · Codex · Ollama
06 Data + tools SQLite/Postgres-ready store · vector memory · files · GitHub · Supabase · email · terminal
Performance simulator queues · workers · audit volume
Task load: 1,000 / hour Workers: 16
1,440 task/hour capacity
69%worker utilization
8m est. queue wait
5,400 audit events/hour
Scalability rules keep it fast and inspectable

- Every request carries tenant_id, department_id, actor_id, task_id, correlation_id.
- Hot permissions, tool manifests and rate counters can be cached.
- Long work moves to department queues with leases, retries and dead-letter handling.
- Vector memory retrieval is scoped by tenant, department, role, task and policy.
- Audit logs are append-only and can be partitioned by company/department/time.

Advantage matrix Hermes · Paperclip · madarX OS

Dimension | Hermes Agent | Paperclip | madarX OS advantage
--- | --- | --- | ---
Primary shape | Personal runtime for one operator/session. | Company/work control plane and artifacts. | Federated AI workforce: employees, departments, agents, approvals, tools, queues, audit.
Department ownership | Not modeled as departments by default. | Can model org work, but runtime is external. | Each department owns agents, memory namespace, tools, queue, policies and approval manager.
Cross-team work | delegate_task spawns subagents, not company-governed departments. | Tasks/governance exist, but not necessarily local execution. | Brokered cross-department requests with RBAC, policy, manager approval and audit lineage.
Approval strength | Strong command approvals for personal execution. | Governance workflows in control plane. | Approval-first at company boundary: cross-department data, tool scopes, external sends, deploys, finance/legal restrictions.
Execution engines | Hermes itself runs tools and providers. | Adapters phone home; plane can stay separate. | Claude Code, Codex, Hermes, Anthropic, Ollama and future MCP tools behind a single governance layer.
Scalability path | Great single-user/gateway scale. | Org data model scale. | Department queues, worker pools, event bus, policy cache, memory scopes, audit partitions, multi-tenant cloud path.
Business moat | Operator productivity. | Structured autonomous company records. | Accountable AI labor market inside the company: everyone gets agents, and agents can collaborate safely.

Department war-room demo launch a campaign across six departments
Finance · approval.required

Finance Controller allows summarized campaign budget but blocks raw revenue exports.

Every handoff creates durable task, activity, approval, and audit context. This is the difference between “agents chatting” and an accountable AI organization.