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Design a Multi-Agent Research Assistant

MediumGenerative AI & LLMs00:00
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Your question is Design a Multi-Agent Research Assistant. Take a moment with it on the right.

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Problem

Scenario

You are building an internal research assistant that answers complex analyst questions by coordinating multiple agents: one plans the task, one retrieves internal documents, one queries approved external sources, and one synthesizes a final answer. Users ask multi-step questions that often require comparing policies, summarizing recent changes, and citing evidence. The system is expected to support roughly 8,000 queries per day, with noticeable spikes during incident reviews and quarterly planning.

Constraints

  • p95 latency: 4,000ms for standard queries
  • Cost ceiling: $12K/month at projected volume
  • Unsupported or weakly grounded claims must stay below 4% on a 300-question golden set
  • Must resist prompt injection from retrieved content and external web pages
  • Final answers must include source-backed citations and refuse when evidence is insufficient

Available Resources

  • Internal document corpus of ~200K markdown, PDF, and wiki pages
  • Approved LLM APIs, embedding models, and tool-calling support
  • Hybrid search over internal content and a small allowlisted external search API
  • 20 hours of SME labeling time per month for evals and error analysis

Question

How would you design the agentic workflow and multi-agent orchestration for this system so it remains grounded, safe, and cost-effective under these constraints? Explain how you would decide when to use multiple agents versus a simpler flow, and how you would evaluate whether the orchestration is actually helping.