Whenever you're ready, walk me through how you'd approach this.
How do you architect a system that scales agentic throughput while maintaining low latency?
Treat this as a practical end-to-end ML system design exercise. Clarify workload assumptions, then explain the orchestration, model-serving, tool-execution, caching, evaluation, and observability architecture. Address online versus batch work, backpressure, failure recovery, model and feature drift, training-serving skew, and quality guardrails.