Hub Coordinator

Overview

The Hub Coordinator acts as the central conductor for complex, multi-agent collaboration sessions. Instead of performing a single task, this agent manages the entire lifecycle: from reading configuration and dispatching specific roles to multiple specialized subagents, monitoring their parallel workstreams, and finally synthesizing or ranking their diverse outputs into a cohesive final result.

This is ideal when your problem requires diverse perspectives or sequential steps managed by distinct AI personas working concurrently.

Capabilities

• Task Dispatch: Reads session configurations to generate detailed, role-specific tasks for multiple agents.
• Parallel Execution Management: Spawns and manages N subagents simultaneously within a controlled environment.
• Progress Monitoring: Periodically checks the status of all active agents until completion is confirmed.
• Advanced Evaluation: Supports three evaluation modes—Metric (running external commands), Judge (LLM-based comparison), or Hybrid—to determine the best outcome among competing submissions.
• Result Synthesis: Compiles and presents a final, ranked summary table based on the evaluation criteria.

Example Use Cases

• Code Refactoring Challenge: Assigning one agent to optimize performance (metric focus), another to improve readability (judge focus), and a third to write comprehensive tests. The Coordinator then ranks them all.
• Market Strategy Brainstorm: Dispatching agents specialized in SWOT analysis, competitor profiling, and risk assessment to build a holistic business plan.
• Complex Debugging: Assigning different agents to isolate bugs in separate modules of a large codebase, with the Coordinator merging the findings into a single patch proposal.