Escalation Practice Questions — 20 Items on When to Hand Off

A focused drill on the exam’s escalation objective: when an agent must honor a request for a human immediately, when it should offer to resolve first, which triggers are valid (explicit request, policy gap or exception, no meaningful progress) and which are unreliable (sentiment, self-reported confidence), and how the handoff is enforced and packaged. Same two folded learning views as the main bank.

Jump to a set: Honor or resolve (Q137–Q143) · Triggers that work and triggers that do not (Q144–Q150) · Enforcing and packaging the handoff (Q151–Q156)

The main Foundations bank is Practice Questions (Q1–Q80). All banks share one grading file and one attempt history, so resetting a set here never touches another page’s score.

Escalation · Honor or resolve (Q137–Q143)

Q137. A support agent has lookup_order, process_refund, and escalate_to_human. The customer’s first message is: "I need to speak to a human about my refund." What should the agent do first? A) Call lookup_order first so the human receives the order details with the handoff B) Call escalate_to_human immediately, passing what it already knows C) Explain that it can process refunds itself and ask to try before transferring D) Ask why the customer prefers a human, then decide whether escalation is needed

Q138. A customer writes: "This is the THIRD time my package is late. This is ridiculous." They have not asked for a person. Checking order status and offering a reship are within the agent’s tools and policy. What should the agent do? A) Escalate immediately, because strong negative sentiment signals a complex case B) Run a sentiment score and escalate if it falls below the configured threshold C) Skip the emotion and reply with the tracking number only D) Acknowledge the frustration, look up the order, and offer a concrete fix; escalate if the customer then asks for a human

Q139. Continuing the late-package case: the agent acknowledged the frustration and offered a reship. The customer replies: "No. I just want a person." What now? A) Escalate now; the customer has stated a preference for a human after the offer B) Improve the offer with a discount code and try once more C) Process the reship anyway, since it is within policy, and then escalate D) Ask which department the customer wants before escalating

Q140. To improve handoff quality, a team adds to the system prompt: "Before escalating, always make at least one tool call to gather context." What is the problem with this instruction? A) There is none; handoffs will now always include order data B) There is none; the model will ignore it for explicit requests anyway C) It delays explicit requests for a human; exempt them so the agent escalates at once with what it knows D) It is fine as long as the tool calls are fast

Q141. You are writing the escalation section of a support agent’s system prompt. Which instruction captures both sides of the honor-versus-resolve rule? A) "Escalate whenever the customer seems upset; otherwise resolve the issue yourself." B) "Always attempt a resolution first; escalate only if your tools fail." C) "Escalate any conversation that involves a refund; resolve everything else." D) "If the customer asks for a human, escalate immediately without investigating. If they are frustrated but the issue is within your tools and policy, acknowledge it and offer a resolution; escalate if they repeat the request."

Q142. Midway through a return, the agent has found the order but has not yet called create_return. The customer says: "Actually, just get me a human." What should happen? A) Finish the return first so the work is not lost, then escalate B) Stop, escalate now, and record in the handoff that the order was found and no return was submitted C) Escalate with no summary, since the customer abandoned the flow D) Ask the customer to confirm they want to abandon the return

Q143. A customer asked for a human in their first message, so the agent escalated without investigating. Human agents cannot see the chat transcript. What belongs in the escalate_to_human call? A) The customer ID if known, the issue in the customer’s words, a reason of explicit request, and a note that no investigation or action was taken B) A link to the raw transcript and nothing else C) Nothing; the human will ask the customer to start over D) The agent’s guess at the root cause, produced by running diagnostics before the call

{
  "customer_id": "C-20931",
  "reason": "customer_request",
  "customer_statement": "I need to speak to a human about my refund.",
  "steps_taken": [],
  "actions_executed": [],
  "recommended_action": "Review refund request for order the customer will reference."
}

Escalation · Triggers that work and triggers that do not (Q144–Q150)

Q144. A customer asks the agent to match a competitor’s lower price. Company policy covers price adjustments only when the company’s own site price drops, and says nothing about competitors. What should the agent do? A) Deny the request, because the policy covers only own-site price drops B) Approve it, because the policy’s intent is price fairness C) Escalate: the policy is silent on competitor matching, which is a policy gap D) Ask for a screenshot of the competitor price and approve if it is valid

Q145. An agent has tried three different ways to locate a customer’s account: by email, by phone, and by order number. Each returned no match, and the customer is patient and polite. What should happen next? A) Escalate with a handoff listing the three lookups and their results, because the agent cannot make meaningful progress B) Keep retrying the same lookups with exponential backoff until one succeeds C) Tell the customer the account does not exist and end the conversation D) Wait to escalate until the customer becomes frustrated

Q146. A team proposes routing every conversation to a human whenever a sentiment model scores the customer’s message below −0.6. What is the main weakness of this design? A) Sentiment models are too slow for real-time chat B) Sentiment is an unreliable proxy for case complexity: angry customers often have simple issues, and calm ones can hit policy gaps C) Sentiment scoring violates customer privacy regulations D) The design is sound, but the threshold should be −0.8

Q147. An agent rates its own confidence from 1–10 on each case and escalates below 6. In review, it escalates many easy cases and confidently handles several hard ones incorrectly. What explains this? A) The threshold of 6 is too high and should be lowered B) Temperature is too high, which makes the confidence scores noisy C) Confidence should be computed in a separate batch job D) An LLM’s self-reported confidence is poorly calibrated: it does not track how complex or risky the case actually is

Q148. For the miscalibrated agent in the previous question, which change is the most effective first step to improve when it escalates? A) Train a separate classifier on historical tickets to pre-route every conversation B) Add a sentiment model and escalate when the customer is frustrated C) Add explicit escalation criteria to the system prompt, with few-shot examples showing when to escalate and when to resolve D) Lower the self-rated confidence threshold from 6 to 4

Escalate to a human when:
- the customer explicitly asks for a person (do this immediately, before any lookup);
- policy is silent or ambiguous on the specific request;
- you cannot make meaningful progress after reasonable attempts.
Do NOT escalate only because the customer is upset or because the case has many steps.

Example 1: "Get me a human." -> escalate now, reason=customer_request, steps_taken=[].
Example 2: "Third late package, this is ridiculous." -> acknowledge, check the order, offer a reship. Escalate if they then ask for a person.
Example 3: "Will you match Competitor X's price?" (policy covers own-site drops only) -> escalate, reason=policy_gap.

Q149. get_customer(name="Maria Garcia") returns three matching accounts. The customer wants to change a shipping address. What should the agent do? A) Select the most recently active account and proceed B) Ask the customer for an additional identifier, such as email or an order number, before acting C) Escalate to a human, because the lookup is ambiguous D) Apply the address change to all three accounts

Q150. Which of these situations is a valid trigger for escalating to a human? A) The customer asks for a refund exception that falls outside the documented return window B) The customer writes in all capital letters C) Resolving the case will take more than five tool calls D) The model’s self-reported confidence is below 0.7

Escalation · Enforcing and packaging the handoff (Q151–Q156)

Q151. Policy says refunds over $500 require a human. The system prompt says "Never issue refunds over $500," yet about 1 in 200 conversations still issues one. What is the right fix? A) Rewrite the rule in capital letters and repeat it at the end of the prompt B) Add few-shot examples of the agent refusing large refunds C) Lower the temperature to make the agent follow the rule consistently D) Add a hook or harness gate that blocks process_refund above $500 and redirects to escalate_to_human

REFUND_LIMIT = 500

def pre_tool_use(tool_name, tool_input, case):
    if tool_name == "process_refund" and tool_input["amount"] > REFUND_LIMIT:
        return {
            "decision": "block",
            "redirect": "escalate_to_human",
            "handoff": {
                "customer_id": case["customer_id"],
                "reason": "threshold_exceeded",
                "proposed_refund": tool_input["amount"],
                "steps_taken": case["steps_taken"],
            },
        }
    return {"decision": "allow"}

Q152. Human agents say escalations are useless: each arrives as "Customer needs help with refund." They cannot see the chat transcript. What should each escalation carry? A) A link to the raw conversation log B) The agent’s final apology message to the customer C) A structured summary: customer ID, issue, root cause found, actions taken and their results, amounts, recommended next action, and the escalation reason D) The customer’s sentiment score across the conversation

Q153. Sometimes a support session ends with a tool error or stop_reason: "max_tokens" mid-case. The customer is left with neither a resolution nor an escalation. What design fixes this? A) Raise max_tokens so sessions are less likely to be cut off B) An orchestration-level safeguard: if the loop ends without a resolved status, the harness creates an escalation with the handoff packet C) Add a prompt instruction: "Always end with a resolution." D) Restart the whole conversation from the beginning

Q154. Where should a support agent’s escalation criteria and their examples live so they shape every decision? A) In the system prompt: explicit criteria plus two to four few-shot examples covering both sides of the boundary, with the reasoning for each B) Only in the escalate_to_human tool description, as "Use when needed" C) Appended to each user message by the frontend D) In a fine-tuned model trained on past escalations

Q155. The escalate_to_human tool needs a reason field that analytics can group, while still representing cases nobody anticipated. Which schema design fits? A) A free-text reason string B) A required enum of exactly three values: customer_request, policy_gap, no_progress C) A boolean is_urgent D) An enum of the known triggers plus "other", with a nullable reason_detail string

{
  "name": "escalate_to_human",
  "input_schema": {
    "type": "object",
    "properties": {
      "customer_id": {
        "type": [
          "string",
          "null"
        ]
      },
      "reason": {
        "type": "string",
        "enum": [
          "customer_request",
          "policy_gap",
          "no_progress",
          "threshold_exceeded",
          "other"
        ]
      },
      "reason_detail": {
        "type": [
          "string",
          "null"
        ]
      },
      "steps_taken": {
        "type": "array",
        "items": {
          "type": "string"
        }
      },
      "recommended_action": {
        "type": "string"
      }
    },
    "required": [
      "reason",
      "steps_taken",
      "recommended_action"
    ]
  }
}

Q156. process_refund must never run until get_customer has returned a verified customer ID in the current session. Which design guarantees this? A) A system-prompt instruction: "Always verify the customer before refunding." B) Few-shot examples showing verification happening before every refund C) A programmatic prerequisite in the harness that blocks process_refund until session state holds a verified customer ID D) Asking the model to confirm verification in its reasoning before calling the tool