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A customer writes: "I've been going back and forth on this return for days. I just want to speak to someone who can actually help me." The agent has confirmed via lookup_order that the return is straightforward - within policy and eligible for immediate processing. What should the agent do?

• A. Call escalate_to_human immediately to honor the customer's request
• B. Acknowledge frustration, inform them this is resolvable now, and offer to complete it or escalate
• C. Ask what specifically hasn't worked in previous attempts before deciding whether to escalate or resolve automatically
• D. Process the refund via process_refund to resolve the underlying issue, then inform them it's complete

During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order, but when attempting to call process_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?

• A. Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically
• B. Escalate immediately to a human agent since the refund action cannot be completed
• C. Implement automatic retries with exponential backoff for process_refund, keeping the conversation open until the refund is successfully processed
• D. Explain the billing confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later

Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%. How should you address this trade-off to improve detection across both categories?

• A. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
• B. Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
• C. Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in a single prompt and eliminate the recall trade-off.
• D. Split the review into separate focused prompts--one for security and API design and another for business logic--each with dedicated examples, and then consolidate the findings before posting.

A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?

• A. Shorten all outputs.
• B. Raise temperature.
• C. Remove role instructions.
• D. Add reliable retrieval sources.

A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

• A. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
• B. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
• C. Implement sliding window context that retains the most recent 30 turns.
• D. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.