Anthropic CCAR-F Value Pack (Frequently Bought Together)

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?

• A. Train a query-complexity classifier using labeled historical data to predict the optimal subagent combination, retraining it periodically.
• B. Create a fast path for factual questions that bypasses subagents entirely, routing every other query through the complete pipeline.
• C. Implement pattern-based routing that classifies queries as single-fact, comparative, or analytical and maps each category to a predefined subagent combination.
• D. Have the coordinator analyze each query and dynamically determine which subagents are required.

A company is building its first production Claude application. Which principle should guide the initial deployment?

• A. Optimize only for response speed.
• B. Disable monitoring to reduce costs.
• C. Start with a simple design and iteratively evaluate improvements.
• D. Maximize prompt complexity immediately.

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, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

• A. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.
• B. Read the project's main entry file and follow import statements to trace where eval() might be used.
• C. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.
• D. Use Bash to run ls -R | grep eval and search the recursively listed filenames.

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.
You've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields. What's the most effective way to iterate toward a working solution?

• A. Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
• B. Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
• C. Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.
• D. Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

• A. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
• B. Configure the web search agent to only return results from the past 6 months.
• C. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
• D. Require subagents to include publication or data collection dates in their structured outputs.