AI · 1h ago
Why AI Agents Fail at Long Loops: The Missing Piece for Production Apps
Most AI agents can execute single actions but fail at sustained decision loops of 50+ steps, limiting production use. Mano-AFK testing showed that adding an adversarial reviewer and external memory via bash tools boosted success rates from 56% to 90%. The core bottleneck is verification and state persistence, not model capability.
Meridian48 take
The article correctly identifies a critical engineering gap, but the solution—adversarial reviewers and filesystem state—may not scale to complex enterprise workflows without significant overhead.
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Why Most AI Agents Still Can't Loop — And That's Why AI Apps Haven't Exploded →
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