I built an always-on AI orchestrator that ran a fleet of agents.
“Master Control” wasn’t a chatbot. It ran on a Mac mini as the CEO/architect of my agent operation — owning a cron fleet, delegating and quality-checking manager-level worker agents, keeping a layered memory system across restarts, reporting to me on a schedule, and blocking on human approval for anything destructive or client-facing. When cost forced a model migration, I moved its entire brain twice — without losing the operation.
I wanted an agent that owns execution.
Not one that just answers questions.
Running SEO and business operations across multiple clients and my own ventures needs leverage, not a chat window.
That meant one always-on coordinator that could hold the whole operation in memory, run recurring jobs unattended, hand work to other agents and check it, and stay safe enough to touch production systems.
A single persistent orchestrator with real guardrails was the way to get leverage without babysitting every task.
A CTO/COO operating model with a human gate.
MC ran as an OpenClaw agent on a dedicated Mac mini, connected to Slack and Telegram through a gateway process.
MC and a peer Operator agent worked like a CTO and COO; manager-level worker agents “do the work” while the coordinators “delegate and QC the work.” MC owned a cron fleet and maintained a multi-layer memory system so it survived restarts with context intact.
It ran under an approval model: destructive operations and external sends require my confirmation. When Anthropic’s billing changed and API rates became unaffordable, I re-homed MC’s model twice — keeping the operation running.
Destructive operations require human approval; external sends require confirmation. And nothing gets delivered until we both sign off.
— MC's own guardrails · the QC chainOrchestrator topology.
A cron fleet and a layered memory system feed MC. MC coordinates a peer Operator, Hermes workers, and named sub-agents, and reaches me through a gateway into Slack and Telegram. Destructive and client-facing work escalates to a human-approval gate and loops back.
A cron fleet and a layered memory system feed Master Control. MC coordinates a peer Operator, Hermes workers, and named sub-agents, and reaches me through a gateway into Slack and Telegram. Destructive and client-facing work escalates to the human-approval gate and loops back — nothing risky ships without my sign-off.
Agent Runtime
Memory System
Model Routing
Coordinated Fleet
Ops
Network / Security
One coordinator, a whole operation.
Descriptive counts from the operating record — not performance metrics.
MC ran under one identity that spanned three aliases across its lifetime (Master Control / MC / Keystone). Failed cron jobs were diagnosed individually by name, not blanket-restarted — the discipline of an operator, not an autopilot.
Three model homes, one live operation.
When Anthropic’s billing changed and API rates became unaffordable, I re-homed MC’s model backend twice — without dropping the operation.
What it produced — and how it ended.
A working always-on orchestrator, retired on purpose — nothing orphaned.
- Ran a cron fleet and coordinated peer and worker agents through a defined QC chain.
- Maintained persistent memory across restarts and reported to me on a schedule.
- Operated under a human-approval gate for destructive and client-facing actions.
- Survived two cost-driven model migrations without dropping the operation.
- When the primary MC role was retired in early May, I performed a controlled handoff of its control paths to the Ops agent — machine kept intact.
MC’s earliest identity was “Keystone.”
- Keystone is the same agent that ran the Dr. Berg technical-SEO automation, documented separately.
That client-outcome work is covered in its own brief and is not restated here as an MC result.
Origin to controlled decommission.
The orchestrator stands up on Mac mini 1 as an OpenClaw agent, wired into Slack and Telegram through a gateway.
Cost forces a move from Claude Opus 4.6 to OpenAI GPT-5.4 — the operation stays live through the swap.
The primary MC role is retired. I hand its control paths to the Ops agent in a controlled decommission — the machine stays intact, nothing orphaned.
The backend moves again to GPT-5.5 via the OpenAI Codex runtime, with Opus 4.8 wired as fallback.
I designed the brain and ran it.
I named MC, defined its role as the coordinating brain of the fleet, and set the CTO/COO operating model.
I wrote the guardrails — human approval on destructive and external actions, a human gate before anything touches production — architected the multi-layer memory system, wired it into Slack and Telegram, and personally directed the two cost-driven model migrations and the eventual controlled decommission. I was the operator and architect; MC was the execution layer I built and ran.