Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

• A. Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
• B. Return fields organized into verified and needs_verification objects based on confidence thresholds.
• C. Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
• D. Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.

Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

• A. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.
• B. Keep all tools separate but implement response caching for analyze_article calls.
• C. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
• D. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.

Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?

• A. Call escalate_to_human passing only the customer's original message.
• B. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
• C. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.
• D. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

• A. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
• B. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
• C. Proceed with additional lookups without modifying the existing tool output context
• D. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues