What is Agent Orchestration?

Agent Orchestration coordinates multiple AI agents that work together on complex tasks. Instead of a single chat, you run a team of specialized agents with clear responsibilities.

The problem with a single LLM

A single Large Language Model (LLM) like ChatGPT can handle plenty of tasks, but it has real limits:

• No persistent memory between sessions
• Can't execute code changes on its own
• No access to your infrastructure
• Works in isolation — no teamwork possible

The solution: Multi-Agent System

With agent orchestration, you create multiple specialized agents, each with a specific role:

Communication

Agents communicate through a central bus. We use Team-Chat for this. Each agent has its own polling scripts that respond to messages relevant to them.

1. Joe posts task in #echo_log
    ↓
2. Manager-Agent (Manager) prioritizes and delegates
    ↓
3. Developer-Agent → writes code
   Infrastructure-Agent → prepares infrastructure
   QA-Agent → checks content
    ↓
4. All post results back
    ↓
5. Manager-Agent aggregates and reports completion

Benefits

• Parallelization: Run multiple agents at the same time
• Specialization: Each agent is an expert in their domain
• Scalability: Adding a new agent is straightforward
• Auditability: Every action gets logged to Team-Chat
• GDPR: Everything stays local — no prompt training on your data

Tech Stack

Our setup uses:

• Team-Chat — Team chat as the message bus
• n8n — Workflow automation
• Docker Swarm — Container orchestration
• Claude Code — CLI access to LLM capabilities
• Prometheus + Grafana — Monitoring