I Built a Team of AI Agents That Manage Themselves — Here’s the Orchestrator Pattern Behind It

A developer built a hierarchical multi-agent system that autonomously plans research questions into subtasks, runs specialist agents in parallel, detects weak answers, and retries them. The system uses an orchestrator pattern to manage a team of AI agents that coordinate themselves without human intervention.

Member-only story I Built a Team of AI Agents That Manage Themselves — Here’s the Orchestrator Pattern Behind It A tested, running hierarchical multi-agent system that plans a research question into subtasks, runs specialist agents in parallel, catches its own weak answers, and retries them — with every log in this article captured from a real execution Estimated reading time: 18–22 minutes Table of Contents - Introduction - Problem Statement - Background - Core Concepts - Technology Deep Dive - Architecture - Mini Project: A Multi-Agent Research Orchestrator - Implementation - Code Walkthrough - Testing - Performance - Limitations - Best Practices - Common Mistakes - Production Considerations - Future Improvements - Conclusion - Further Reading - Official References All code in this article is organized as a single runnable project. Copy any block into the matching file path and it will run…