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Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?

• A. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
• B. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
• C. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
• D. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.

A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

• A. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
• B. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
• C. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
• D. Implement sliding window context that retains the most recent 30 turns.

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

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're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

• A. Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
• B. Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
• C. Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
• D. Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.

The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

• A. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.
• B. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
• C. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
• D. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.