Research

Multi-Agent Research Synthesis

Overview

What It Is

Agent teams that conduct comprehensive research by gathering, analyzing, and synthesizing information from multiple sources into coherent insights and reports.

Agent Types
Query Planner AgentSearch AgentExtraction AgentSynthesis AgentFact-Check AgentWriting AgentEditor Agent
Need help implementing this use case?
Talk to Us

Deep Dive

Overview

Multi-agent research synthesis systems decompose complex research questions into subtasks handled by specialized agents. This approach, known as "Agentic RAG" when combined with retrieval, enables deeper analysis than single-agent systems.

Architecture (MA-RAG Style)

Research Question → Query Planner → Sub-questions
                         ↓
                   Search Agent → Raw Sources
                         ↓
                 Extraction Agent → Key Findings
                         ↓
                 Synthesis Agent → Draft Insights
                         ↓
                Fact-Check Agent → Verified Content
                         ↓
                  Writing Agent → Report
                         ↓
                  Editor Agent → Final Output

Agent Roles

Query Planner Agent

  • Decomposes complex questions into searchable sub-queries
  • Identifies information gaps
  • Prioritizes research paths

Search Agent

  • Executes searches across multiple sources
  • Retrieves documents, papers, web content
  • Handles API integrations

Extraction Agent

  • Pulls key facts, quotes, and data from sources
  • Maintains source attribution
  • Structures extracted information

Synthesis Agent

  • Combines findings into coherent narratives
  • Identifies patterns and contradictions
  • Generates preliminary insights

Fact-Check Agent

  • Verifies claims against original sources
  • Flags uncertain or contradictory information
  • Ensures citation accuracy

Writing Agent

  • Transforms synthesis into polished prose
  • Follows style guidelines
  • Structures content appropriately

Key Innovation: Agentic RAG

Traditional RAG retrieves documents and generates responses. Agentic RAG adds:

  • Reflection: Evaluating retrieval quality
  • Planning: Multi-step retrieval strategies
  • Correction: Dynamic query refinement

Real-World Applications

  • SAP Joule Deep Research: Multi-domain questions synthesizing internal data and external intelligence
  • Academic Literature Review: Systematic analysis of research papers
  • Competitive Intelligence: Market and competitor analysis
  • Due Diligence: M&A research and verification

MA-RAG Results

"Even a small LLaMA3-8B model equipped with MA-RAG surpasses larger standalone LLMs" on complex QA tasks—demonstrating that multi-agent collaboration can compensate for individual model limitations.

Evaluation Challenges

Research quality depends on source quality and coverage. Synthesis may introduce biases not present in sources. Fact-checking has limits—some claims are genuinely contested. Attribution and citation accuracy require careful verification.

Get personalized recommendations
Try Advisor
Tags
researchsynthesisagentic-ragfact-checkinganalysis

Was this use case helpful?