The 76-Point Serving Backend Lottery
Forge, a Python guardrails framework from Texas Instruments AI director Antoine Zambelli, shows that agentic reliability is dominated by orchestration, not model capability: Ministral 8B with guardrails (99.3%) outperforms Claude Sonnet without them (87.2%). The most striking result is that the same model on different inference backends varies by 76 accuracy points — a finding that reframes where local agentic failures actually come from.
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