Evaluating Agent Task Success Rates (EXPLAINED)
Hard AI Agents interview question on evaluating agent task success rates — architecture, trade-offs, eval, and production patterns.
TL;DR — Quick Answer
Define task-specific success rubrics, trajectory scoring, LLM judges calibrated to humans, and private task suites. Track pass@k, cost per success, and regressions on tool schema changes.
The Interview Question
Explain evaluating agent task success rates. How would you apply this when building production AI agents? Cover architecture, safety, and evaluation.
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