The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

• A. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
• B. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
• C. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
• D. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

• A. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
• B. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
• C. Route extractions for review only when downstream systems report data quality issues or processing failures.
• D. Randomly sample 5% of extractions for review.

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

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

• A. Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.
• B. Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.
• C. Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
• D. Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2? attempts.
For which failure pattern would additional retries be LEAST effective?

• A. The model extracts citation counts as locale-formatted strings ("1,234") when the schema requires integers.
• B. The model extracts "et al." for co-authors when the full list exists only in an external document not in the input.
• C. The model extracts keywords as a nested object organized by category when the schema requires a flat array of strings.
• D. The model extracts dates as ISO 8601 datetime strings ("2023-03-15T00:00:00Z") when the schema requires only the date portion (YYYY-MM-DD).