Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

• A. Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
• B. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
• C. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
• D. Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.

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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 used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

• A. Use --continue to pick up where the most recent conversation left off
• B. Use --resume auth-deep-dive to load that specific session by name
• C. Start fresh and re-read the same files
• D. Use --session-id with the UUID from yesterday's session transcript file

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Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

• A. Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
• B. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
• C. Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
• D. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.

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In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?

• A. Add a verification step where the report generator uses semantic similarity matching against original sources to reconstruct which claims came from which documents.
• B. Maintain complete transcripts of all subagent interactions and add a citation-resolution agent to analyze logs and determine attributions before report generation.
• C. Require all subagents to output structured claim-source mappings that the synthesis agent must preserve and merge when combining findings from multiple sources.
• D. Have the coordinator inject source identifier prefixes into text before each handoff, then parse these prefixes at report generation to reconstruct citations.

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When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

• A. Return a success response with a "status" field indicating the error type
• B. Return the error message in the tool result content with the isError flag set to true
• C. Log the error server-side and return an empty result to avoid confusing the model
• D. Throw an exception from the tool handler so the agent framework can catch and log it

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