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

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 asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?

• A. Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.
• B. Use Glob to find files matching common caching patterns (cache.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps.
• C. Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
• D. Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.

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.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today.
The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge. What approach best balances efficiency and accuracy?

• A. Resume the session and immediately have the agent re-read all 12 previously analyzed files
• B. Start fresh session to ensure the agent works with current codebase state without stale assumptions
• C. Resume the session and inform the agent which specific files changed for targeted re-analysis
• D. Resume the session without informing the agent about the changed files

A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

• A. Replace Claude with a smaller local model regardless of task complexity.
• B. Remove unnecessary sensitive information before sending prompts whenever possible.
• C. Send every document without preprocessing to maximize context.
• D. Store every prompt permanently for auditing.

An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?

• A. Ask Claude to "respond normally."
• B. Use multiple unrelated examples.
• C. Increase maximum output tokens only.
• D. Explicitly specify the required JSON schema and formatting rules.