After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

• A. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
• B. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
• C. The subagents need to share a single API connection to enable automatic context sharing between invocations.
• D. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.

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

An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

• A. Max tokens
• B. Context window
• C. Prompt length
• D. Temperature

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

During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

• A. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
• B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
• C. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
• D. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.