The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations – the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What’s the most effective approach to ensure proper source attribution in the final reports?
Question 2 of 134
After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?
Question 3 of 134
Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?
Question 4 of 134
You’ve configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization – the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool selection behavior?
Question 5 of 134
The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports “insufficient results” rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don’t match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources. What’s the most effective way to improve subagent adaptability?
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