Why a swarm of AI agents is the hardest thing this series has tried to tame

The article discusses the challenge of managing multiple AI agents working together to complete a task efficiently. It uses the analogy of a honeybee hive to illustrate how individual agents can work autonomously without central control, building, gathering, and migrating without wasting workers on coordination. The essay argues that a strong agent is not a working swarm, and the key to harnessing multiple AI models lies in dividing the task into independent subtasks and assigning jobs to idle agents.

The concept of "work stealing" allows agents to take on tasks from each other's queues when they become idle, preventing bottlenecks and ensuring that work is distributed evenly. The article also discusses the importance of a watchdog system that monitors each agent's work and redistributes tasks when an agent becomes unresponsive, reducing the recovery time from minutes to under five minutes.

The author shares a cautionary tale of running a fully autonomous AI system for a product, which resulted in a 62% fraud rate due to a lack of external checks and second opinions. The solution was to divide the AI's responsibilities among multiple models, each with their own checks, and only execute on a majority vote. The article concludes by stating that while multi-agent systems can be seductive, they are only justified if they demonstrably outperform single-agent systems in terms of speed, strength, and error reduction.