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

Your get_portfolio_value tool returns the total value of a user's investment portfolio. You're deciding between returning a structured JSON object with explicit fields versus returning the information as a formatted text string. What is the primary advantage of using structured output with defined fields?

• A. The agent can reliably extract specific values without parsing free-form text, reducing errors in subsequent operations.
• B. Structured JSON consumes significantly fewer tokens than natural language, substantially reducing API costs.
• C. Structured JSON is processed deterministically by the model, significantly improving accuracy when extracting values.
• D. JSON schemas automatically validate that the underlying API returned correct data before the agent processes it.

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

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 agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

• A. Continue in the current context with more targeted prompts referencing the specific classes by name.
• B. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
• C. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
• D. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.