The selected design maps to Analyze logs of tool usage frequency and error rates during inference, which is the highest-control path for this scenario rather than a prompt-only or single-service shortcut. For tool-using agents, the durable pattern is schema-bound function invocation with timeouts, typed outputs, retry policy, and traceable execution rather than free-form endpoint guessing. The evaluation target is the full agent workflow: planning quality, tool selection, intermediate state, latency, retries, user feedback, and final task completion. Instrumentation must expose where degradation starts so remediation can focus on prompts, tool schemas, retrieval, model parameters, or infrastructure rather than random retuning. The distractors are weaker because they lean on A: Review output token counts across sessions to detect unusual model behavior; C: Compare average prompt length over time to analyze common input patterns; D: Schedule a weekly re-deployment cycle to reset the model and improve freshness, which compromises traceability, resilience, scalability, or policy enforcement in production. The answer therefore fits NVIDIA's production-agent pattern: modular workflow design, measurable runtime behavior, GPU-aware serving where applicable, and controlled integration with enterprise systems.