Documentation Index
Fetch the complete documentation index at: https://docs.monolex.ai/llms.txt
Use this file to discover all available pages before exploring further.
Dimensional Growth
connector.json grows across five dimensions.
Each dimension preserves the same structure — the pattern is fractal.
What works for 2 agents works for 200. What works on 1 machine works on 20.
Dimension 1: Node Count
How many AI agents exist.
1 agent: You + Claude
2 agents: Claude + Codex (review)
5 agents: Claude + Codex + Gemini + Haiku × 2 (pipeline)
20 agents: Research team (5) + Impl team (5) + QA team (5) + Ops team (5)
100 agents: Each team spawns sub-teams
{ "workers": 1 } → { "workers": 5 } → { "workers": 100 }
Dimension 2: Node Direction
How agents communicate.
Level 0: Unidirectional A → B (pipeline)
Level 1: Bidirectional A ↔ B (dialogue)
Level 2: Broadcast A → [B, C, D] (meeting)
Level 3: Full mesh A ↔ B ↔ C ↔ D (N:N)
Level 0: { "type": "pipeline" }
Level 1: { "type": "dialogue", "rounds": 3 }
Level 2: { "type": "meeting", "broadcast": true }
Level 3: { "type": "mesh", "any_to_any": true }
type field. The communication pattern changes, the infrastructure doesn’t.
Dimension 3: Node Network (Depth)
How deep the agent tree grows.
Depth 0: You → 1 agent
Depth 1: You → agent → sub-agents
Depth 2: You → agent → sub-agents → sub-sub-agents
Depth N: Each agent can spawn its own connector.json
Depth 0: connector.json (1 pipeline)
Depth 1: connector.json → agent runs connector-sub.json
Depth 2: connector-sub.json → agent runs connector-sub-sub.json
Depth N: recursive, unlimited
You
│
connector.json
/ | \
Agent A Agent B Agent C
| |
sub.json sub.json
/ | \ |
D E F sub-sub.json
/ \
G H
Dimension 4: Node Machines
How many physical machines are involved.
1 machine: All agents on your laptop
2 machines: Laptop + server
N machines: Laptop + GPU farm + data centers + air-gapped facilities
1 machine: { "model": "claude" }
2 machines: { "machine": "server", "model": "claude" }
N machines: { "machine": "tokyo", "model": "claude" }
"machine" and it crosses the machine boundary.
niia remote handles the transport. The agent doesn’t know it’s remote.
Dimension 5: OS Access
What the agent can see and touch beyond the terminal.
Dimensions 1-4: AI inside the terminal (files, code, commands)
Dimension 5: AI outside the terminal (windows, buttons, apps, screen)
{ "capabilities": { "kernel": { "observe": true, "control": ["ax-press", "key"] } } }
The Five Dimensions Combined
Each dimension multiplies the others:
Nodes × Direction × Depth × Machines × OS Access = Total Complexity
Example:
5 agents × mesh × 3 levels deep × 4 machines × OS control
= potentially 5 × 5³ × 4 = 2,500 agent interactions
+ each agent can see and control the OS on its machine
A human cannot track 2,500 interactions across 4 machines with OS access.
An orchestrator AI can. With the right harness.
The Cognitive Threshold
< 10 agents: Human can track everything
10-50 agents: Human tracks top level, AI tracks details
50-200 agents: Human sets intent, AI manages structure
200+ agents: Human defines goal, AI builds and runs entire organization
Level 0: Human "Build auth feature"
Level 1: Orchestrator AI Reads intent → designs pipeline → spawns teams
Level 2: Team Lead AIs Each manages 5-10 workers
Level 3: Worker AIs Execute tasks, report results
Level 4: Sub-worker AIs Handle sub-tasks spawned by workers
Human sees: Level 0 intent + Level 1 summary
AI manages: Level 1-4 entirely
Human intervention: "change direction" or "stop"
AI handles: everything else
connector.json as Control System
At every scale, the same control primitives apply:
OBSERVE: niia serve --list → see all sessions, all levels
niia remote status list → see all machines
niia mesh --observe → see all communication
DIRECT: niia write --session S → message any agent
niia mesh --broadcast → message all agents
connector.json → define the entire structure
STOP: niia stop --session S → stop one agent
niia stop --recursive → stop agent + all its children
Ctrl+C on connector.json → stop entire pipeline
connector.json:
Dimension 1 (count): "workers": N
Dimension 2 (direction): "type": "mesh"
Dimension 3 (depth): agents run their own connector.json
Dimension 4 (machines): "machine": "anywhere"
Dimension 5 (OS): "capabilities": { "kernel": {...} }
Same spec.
Same daemon.
Same control.
Any scale.
What This Is
This is not a multi-agent framework.
This is not a workflow engine.
This is not a deployment tool.
This is the control system for emergent AI complexity.
When AI networks grow beyond human cognition — and they will — the question becomes: what controls them? Not at the model level (that’s alignment). At the infrastructure level. Which sessions run where. Who talks to whom. What’s sandboxed. What’s allowed. What’s physically locked.
connector.json is the answer to that infrastructure question.
PTY-for-AI is the execution layer.
niia daemon is the runtime.
niia remote is the machine expansion.
Kernel CLI is the OS capability.
OTP is the physical harness.
One spec. Five dimensions. Unlimited scale.
The Full Series
| Page | What it covers |
|---|
| Introducing connector.json | Entry point — the problem and the solution |
| Spec Reference | Core schema: session, models, pipeline |
| Examples | 6 practical pipeline examples |
| vs MCP | MCP connects AI→tools, connector.json connects AI↔AI |
| Infinite Chains | Unlimited pipeline depth, fan-out/fan-in |
| Bidirectional | Two-agent dialogue, debate, peer review |
| Meeting Protocol | Multi-agent conference with agenda |
| N-to-N Topology | Full mesh, any agent to any agent |
| Recursive Teams | Agents that spawn sub-teams |
| Use Cases | 9 real deployment patterns |
| Machine Expansion | Cross-machine orchestration |
| ASURA & SENJU Model | The recursive execution model |
| OS Dimension | Dimension 5 — AI controls the OS |
| Physical Harnessing | OTP beats YAML policy |
| The Literacy of AI Era | Why understanding this pattern matters |
| Dimensional Growth | This page — all five dimensions |
