One Command, Many Agents

The bottleneck

Every “AI agent” demo looked slick… until it hit real work. Too many steps, no memory, no logs, and zero orchestration. I wanted one interface, clear reasoning, and agents that actually do things.

The system I built

I started with a lean foundation in n8n—then layered agents under a single orchestrator.

Foundation (10 minutes)

• Telegram Bot via BotFather
• Webhook → Router (photos one path, text another)
• Orchestrator Agent = the manager
• Thinking Tool = pause → reason → decide
• Auto-Logging with Data Tables (input, output, tokens, timestamps)

Orchestrator prompt (core):

“You are the ultimate manager agent. Help the user by delegating tasks to the correct tool.”

Stacked Agents (plugged into the orchestrator)

• Social Media Agent — scrapes YouTube trends
• Content Agent — videos with Google Veo3, images with Flux Pro, edits in Nano Banana 🍌
• Social Upload Agent — posts across platforms via Blotato
• Calendar Agent — checks schedule, traffic, weather; auto-reschedules
• Google Drive Agent — finds, renames, shares files instantly
• Web & Weather Agent — quick context from the open web

I run this on a Raspberry Pi with ngrok. 50-message memory buffer keeps context tight. Everything is logged.

What changed (tangible)

• One command in Telegram triggers the right agent, every time
• Content goes from idea → asset → edits → posted without manual handoffs
• Meetings move themselves when travel or weather shifts
• Files are fetched and shared on request—no hunting through Drive

Why this works

Foundation first. Tools second. The orchestrator delegates; the agents execute. No spaghetti automations. No silent failures (logs catch everything).

Want the exact workflow?

We’ve documented the Telegram AI Agent setup + the Ultimate AI Agent inside Corporate Automation Library Pro.

• 30+ plug-and-play n8n workflows
• 2–3 new automations weekly (content, sales, lead gen, retention)
• Step-by-step deployment guides (each under 30 minutes)

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