Key lessons from scaling an AI bot fleet beyond 100 concurrent agents

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A developer shared hard-won lessons after catastrophic production failures while scaling an AI bot fleet past 50 agents. Independent task queues per bot caused workload imbalance, and a centralized dispatcher with load-aware distribution proved essential at the 100-agent threshold. Managing API keys securely became critical after a compromised key triggered a cascade failure across the entire fleet. The developer also found that bots sharing similar knowledge bases tend toward "mode collapse," requiring deliberate behavioral diversity through varied parameters. Coordinated API calls among large bot fleets create thundering-herd problems, which distributed rate limiting with randomized jitter helps mitigate.

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