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This implementation looks promising, but I’m curious what’s the eval setup for these benchmarks? I’ve been tracking a personal list of demo-only tricks that always break under load once you hit high concurrency https://iris-wiki.win/index.php/Engineering_Multi-Agent_AI_Systems:_Beyond_the_Marketing_Noise
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This implementation looks impressive at first glance, but I’m curious, what’s the eval setup? I’ve started a running list of “demo-only tricks” that tend to break once you push these systems under heavy production load frameworks for multi-agent systems
This approach looks promising for small datasets, but what’s the eval setup? I’ve started keeping a running list of “demo-only tricks” that inevitably break under load, and this feels like it might be one of them multi-agent orchestration coverage
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I really enjoyed this post, but I have to ask: what’s the eval setup you’re using here? I’ve seen way too many developers rely on these demo-only tricks that inevitably break under load multi-agent April headlines
This approach looks solid on the surface, but I have to ask: what’s the eval setup for these benchmarks? I’ve seen way too many ‘demo-only tricks’ lately that inevitably break under load, turning high-latency performance into a total system failure multi-agent systems research schools
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It is impressive to see these results, but I am curious: what’s the eval setup? I have started keeping a running list of demo-only tricks that always seem to break under production load multi-agent AI systems updates 2026
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