"In a long workflow, tiny per-step defects compound into very large end-to-end losses. At 100 steps, a 1% failure rate at each step leaves only about 36.6% end-to-end success; even 0.1% still leaves only about 90.5%."
"The practical implication is that production reliability should be treated as an error-correction problem. Better base models help, but they are only part of the story."
"The system also needs a way to observe what happened, judge it against the organization's standards, intervene safely when necessary, replay changes before release, and keep durable evidence of what changed and why."
In complex workflows, small errors at each step can lead to significant end-to-end failures. A 1% failure rate can reduce success to 36.6%, while even a 0.1% rate results in only 90.5% success. Factors contributing to production failures include missing knowledge, judgment misalignment, affect regression, and evidence gaps. To enhance production reliability, organizations must treat it as an error-correction problem, ensuring systems can observe, judge, intervene, and maintain evidence of changes effectively.
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