You are creating a virtual assistant agent that needs to handle an increasingly wide range of tasks over an extended period. What is the primary benefit of combining external storage (like RAG) with fine-tuning (embodied memory) in this context? 

You’re managing an agentic AI responsible for customer support ticket triage. The agent has been consistently accurate in routing tickets to the appropriate departments. However, a team leader has noticed a significant increase in the number of tickets requiring “escalation” – cases where the agent initially misclassified a complex issue as a simple, routine one, leading to delays and frustrated customers. What would be an appropriate first step in resolving this issue? 

Your team notices a spike in failed tool calls from a deployed workflow agent after a recent API schema update. The agent still returns outputs, but many are irrelevant or incomplete. Which maintenance task should be prioritized to restore accurate behavior? 

When evaluating a multi-agent customer service system experiencing unpredictable scaling costs and performance bottlenecks during peak hours, which analysis approaches effectively identify optimization opportunities for both infrastructure efficiency and service reliability? (Choose two.)

An AI Engineer has deployed a multi-agent system to manage supply chain logistics. Stakeholders request greater insight into how the agents decide on actions across tasks. Which approach would best improve decision transparency without modifying the underlying model architecture?