A customer service agent sometimes fails to complete multi-step workflows when APIs respond slowly or inconsistently.
Which approach most effectively increases robustness when working with unreliable APIs?
The selected design maps to Add retries with exponential backoff and set request timeouts, 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: Restrict available tools to reduce decision complexity; C: Cache recent API results to limit unnecessary repeated calls; D: Adjust generation parameters to produce more predictable responses, 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.
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