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

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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

• A. Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
• B. Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
• C. Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
• D. Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer sees an unfamiliar error message "SYNC_CONFLICT: entity version mismatch detected" in production logs but doesn't know which of the 12 services in the codebase generates it. They ask the agent to help locate the source code. What exploration approach will most efficiently find the responsible code?

• A. Use Glob to find files in directories commonly associated with error handling (such as errors/, exceptions/,or handlers/) across all services, then Read each matching file.
• B. Read the project's README and service configuration files to understand the architecture, then systematically Read source files in each service directory.
• C. Use Grep to search for distinctive text from the error message (like "SYNC_CONFLICT" or "entity version mismatch"), then Read the matching files to understand context.
• D. Use Grep to find all files that import the project's error handling module, then Read those files to locate custom error definitions.

Which practice MOST improves prompt maintainability?

• A. Random ordering.
• B. Clearly separated sections with headings.
• C. One paragraph containing all instructions.
• D. Minimal punctuation.

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

• A. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
• B. Pass the full accumulated context from all prior agents.
• C. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
• D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.