swarm

Multi-agent fleet orchestration is the process of launching and managing a coordinated swarm of independent AI research teams to work concurrently on different aspects of a challenging optimization problem, accelerating discovery through parallel execution.

You can launch multiple AI research teams by using a fleet orchestration skill to dynamically spawn a set number of agents, each with specialized roles, to explore different optimization strategies simultaneously and significantly speed up performance tuning.

A multi-agent swarm communicates and monitors fleet health during concurrent tasks by utilizing a shared file system for inter-agent communication and implementing heartbeat mechanisms to track the status and availability of each research team.

Yes, you can dynamically adjust the number of parallel research teams working on a problem, as the fleet orchestration system supports dynamic team counts to scale concurrent agents based on the specific optimization requirements.

You should use parallel AI agents instead of a single agent when facing complex optimization problems, as launching a coordinated fleet of independent research teams exploring different strategies simultaneously significantly accelerates the process of finding the best solution.

The shared file system approach for multi-agent communication requires strict file management to prevent read/write conflicts across concurrent agents, and performance may bottleneck if the coordinated research teams generate excessive simultaneous file operations.