exam-a · Q1Multi-agent research

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?

1. AHave the report generator query the web search agent to re-locate sources for claims in the final report.
2. BHave each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).
3. CSkip summarization and pass full raw outputs from web search and document analysis directly to the report generator.
4. DInstruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.

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exam-a · Q2Multi-agent research

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?

1. APass the full accumulated context from all prior agents.
2. BPass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
3. CPass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
4. DPass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.

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exam-a · Q3Multi-agent research

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?

1. AHave each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.
2. BIndex all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.
3. CHave each agent maintain its own persistent state file and reload it independently at the start of each session.
4. DPersist the coordinator’s conversation log containing all task delegations and responses, providing this to agents when resuming.

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exam-a · Q4Multi-agent research

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?

1. AChoosing from 18 tools instead of 4-5 relevant ones increases decision complexity beyond reliable selection thresholds.
2. BThe tool definitions consume too much context window space, leaving insufficient room for task content.
3. CThe agents’ role descriptions in their system prompts conflict with having access to tools outside that role.
4. DThe coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.

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exam-a · Q6Multi-agent research

The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn’t find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?

1. AHave the report generation agent note which research questions couldn’t be answered, so users understand the limitations of the final output.
2. BIncrease the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information.
3. CHave the coordinator evaluate synthesis output for gaps, then re-delegate to web search and document analysis with targeted queries before invoking synthesis again.
4. DGive the synthesis agent direct access to web search tools so it can autonomously fill knowledge gaps without returning control to the coordinator.

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exam-a · Q7Multi-agent research

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?

1. AThe agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.
2. BThe web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.
3. CThe coordinator agent receives the web search agent’s output and includes relevant findings in the prompt when invoking the document analysis agent.
4. DBoth agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.

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exam-a · Q8Multi-agent research

Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns “industry analysts estimate $50B market size (methodology varies)” and the document analysis agent returns “peer- reviewed study estimates $35B (±$7B, 95% CI),” the coordinator either picks one arbitrarily or produces vague statements like “the market may be $35B-$50B depending on factors.” What systematic approach best addresses this?

1. AConfigure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
2. BAdd a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.
3. CInstruct the synthesis agent to structure reports with explicit sections distinguishing well-established findings from contested ones, preserving original source characterizations and methodological context.
4. DImplement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.

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exam-a · Q9Multi-agent research

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?

1. ARequire all subagents to output structured claim-source mappings that the synthesis agent must preserve and merge when combining findings from multiple sources.
2. BMaintain complete transcripts of all subagent interactions and add a citation-resolution agent to analyze logs and determine attributions before report generation.
3. CAdd a verification step where the report generator uses semantic similarity matching against original sources to reconstruct which claims came from which documents.
4. DHave 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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exam-a · Q10Multi-agent research

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?

1. AThe subagents need to share a single API connection to enable automatic context sharing between invocations.
2. BThe synthesis agent needs tools that can fetch results directly from the other agents’ conversation histories.
3. CThe coordinator did not include the outputs from the previous agents in the synthesis agent’s prompt.
4. DThe synthesis agent’s context window is not large enough to hold the combined outputs from both previous agents.

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exam-a · Q11Multi-agent research

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?

1. AAdd a research planning agent before the search phase that decomposes topics into specific sub-questions.
2. BHave the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
3. CHave the coordinator review analysis output for gap indicators and re-invoke search with gap-informed queries when gaps are detected.
4. DHave the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.

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exam-a · Q30Multi-agent research

The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like “extract the key financial metrics” often return narrative summaries, while “summarize the methodology” sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What’s the most effective way to improve reliability?

1. AHave the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
2. BKeep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
3. CEnhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
4. DSplit the generic tool into purpose-specific tools – extract_data_points, summarize_content, verify_claim_against_source – each with defined input/output contracts

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exam-a · Q36Multi-agent research

The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate – it generates messages like “I’ll ask the web search agent to find sources on this topic” – but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

1. AThe coordinator’s max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
2. BThe coordinator’s allowedTools configuration doesn’t include “Task”, so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
3. CThe AgentDefinitions are configured correctly, but the coordinator’s system prompt doesn’t explicitly list the available subagent types, preventing the model from knowing they can be invoked.
4. DSubagent context isolation means task descriptions from the coordinator don’t automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.

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exam-a · Q82Multi-agent research

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?

1. AInstruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
2. BAdd a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
3. CRequire subagents to include publication or data collection dates in their structured outputs.
4. DConfigure the web search agent to only return results from the past 6 months.

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exam-a · Q83Multi-agent research

In production, you observe that simple fact-checking queries (e.g., “What year was the Paris Climate Agreement signed?”) traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline. Your query distribution is diverse and evolving as users discover new applications. What’s the most effective approach to optimize for varying query complexity?

1. ACreate a fast-path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.
2. BImplement pattern-based routing that categorizes queries by structure (single-fact vs. comparative vs. analytical) and maps each category to a predefined subagent combination.
3. CTrain a query complexity classifier on labeled historical data to predict optimal subagent combinations, retraining periodically as query patterns evolve.
4. DHave the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of query requirements.

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exam-a · Q84Multi-agent research

When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What’s the most effective way to reduce this latency while preserving the coordinator’s ability to monitor and debug the system?

1. ACreate a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
2. BHave the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis
3. CEnable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
4. DImplement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents

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exam-a · Q85Multi-agent research

A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

1. AUpdate the synthesis agent to render each content type appropriately – financial data as tables, news as prose
2. BStandardize all subagent outputs to prose summaries with inline citations
3. CStandardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence
4. DAdd a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation

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