team-builder

Running multiple AI agents in parallel involves selecting up to five agent personas from different domains and dispatching them concurrently. The system handles task spawning, parallel execution, and failure tracking, ultimately synthesizing the collected results into a single unified report.

Assembling an AI agent team uses an interactive domain-aware picker to browse agent personas from subdirectory or flat layouts. You select up to five agents to compose a multi-agent team tailored to your workflow automation needs, streamlining cross-domain collaboration.

The maximum number of agents you can deploy concurrently is five. Enforcing this limit balances operational usefulness with cost, ensuring that the parallel execution of your selected agent personas remains manageable and effective for generating synthesized reports.

Multi-agent parallel execution handles failures by collecting per-agent results with inline failure notes. When a dispatched agent encounters an issue, the system captures the failure context within the structured synthesis, allowing the unified report to highlight agreements and tensions accurately.

Yes, you can browse agents from different domains like marketing, sales, and engineering. The system supports multi-domain collections, allowing you to discover and select agent personas across various fields using either domain-based subdirectories or flat layout structures.

The synthesized report from parallel agent execution contains a structured summary highlighting agreements, tensions, and recommended next steps. It aggregates the individual outputs of the concurrently dispatched agents, including any inline failure notes, into one cohesive document.