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Experiments with a live agent processing market data through a governed runtime revealed three surprises: prompt structure drives execution reliability over reasoning quality; structured output can influence agent decisions; and separating reasoning and extraction into two calls maintains high parse success. The findings suggest governance belongs at the execution boundary, not on freeform reasoning.
Fireworks AI and Notte introduce the 'Agent Execution Tax' metric after running 720 browser agent tasks across four LLMs, finding that execution reliability—not intelligence—is the primary bottleneck in agentic AI, with one model wasting 22.9% of inference calls on malformed JSON.