Why it works: It validates their pain ("herding cats") and offers a specific, technical fix (concurrency control) rather than a vague "AI solution."
The "managing the herd" overhead is real. I found that 80% of my debugging time wasn't fixing bad code, but fixing race conditions where agents were overwriting each other's context or hallucinating because they didn't have the latest state.
I ended up building a "traffic light" protocol (essentially a semaphore for swarms) just to force serialization on critical tasks. It kills the speed slightly but stops the "death spiral" where one agent's error cascades through the herd.
Comments
Why it works: It validates their pain ("herding cats") and offers a specific, technical fix (concurrency control) rather than a vague "AI solution."
The "managing the herd" overhead is real. I found that 80% of my debugging time wasn't fixing bad code, but fixing race conditions where agents were overwriting each other's context or hallucinating because they didn't have the latest state.
I ended up building a "traffic light" protocol (essentially a semaphore for swarms) just to force serialization on critical tasks. It kills the speed slightly but stops the "death spiral" where one agent's error cascades through the herd.
If you're building your own orchestrator or using something like OpenClaw, I open-sourced the concurrency logic here: https://github.com/jovanSAPFIONEER/Network-AI