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10. Build an AI Research Agent

Build an autonomous AI research agent that can research any topic end-to-end — planning a research strategy, searching multiple sources, reading and synthesizing information, fact-checking, and generating a comprehensive report with citations.

Research is time-consuming. An AI research agent automates the entire research lifecycle — from understanding what you need to know, to delivering a synthesized report with verified sources.


Researchers, analysts, and knowledge workers spend hours gathering and synthesizing information. An AI research agent should:

  • Understand the research question and plan an approach
  • Search multiple sources (web, academic, internal)
  • Read and extract key information from each source
  • Synthesize findings into a coherent report
  • Fact-check claims and provide citations
  • Support iterative refinement based on feedback

A consulting firm needs a research agent that can prepare briefs on any industry topic within 15 minutes — searching 50+ sources, analyzing findings, and generating a structured report ready for client delivery.


#FeatureDescription
FR1Research planningDecompose question into sub-questions
FR2Multi-source searchWeb, academic, internal knowledge base
FR3Content extractionRead and extract key information
FR4SynthesisCombine findings into coherent analysis
FR5Fact-checkingVerify claims across multiple sources
FR6Citation generationEvery claim linked to source
FR7Report generationStructured report with executive summary
FR8Iterative refinementFollow-up questions and deep dives
#RequirementTarget
NFR1Research time< 15 min for complex topic
NFR2Source coverage20-50 sources per research
NFR3Accuracy> 95% factually correct
NFR4Citation accuracyEvery claim has verifiable source
NFR5ScalabilityMultiple concurrent research tasks

LayerTechnologyPurpose
FrontendNext.js + TailwindResearch dashboard
BackendFastAPI (Python)Agent orchestration
Agent FrameworkLangGraphAgent workflow management
SearchSerpAPI, Bing, PubMed, arXivMulti-source search
AIGPT-4o / ClaudePlanning, reading, synthesis
Vector DBQdrantResearch document storage
DatabasePostgreSQLResearch records, reports
QueueCelery + RedisAsync research tasks
CacheRedisSearch result caching

flowchart TD
subgraph PLANNER["Planner Agent"]
DECOMP["Decompose Question\nInto sub-questions"]
STRATEGY["Search Strategy\nWhich sources to query"]
QUERIES["Generate Search Queries\n10-20 queries"]
end
subgraph SEARCHER["Search Agent"]
WEB_S["Web Search\nBing/Google"]
ACADEMIC["Academic Search\nPubMed/arXiv"]
INTERNAL["Internal KB\nVector search"]
end
subgraph READER["Reading Agent"]
EXTRACT["Extract Content\nKey facts, stats, quotes"]
SUMMARIZE_SRC["Source Summary\nPer-source summary"]
EVAL_SRC["Source Evaluation\nCredibility check"]
end
subgraph SYNTHESIZER["Synthesis Agent"]
COMBINE["Combine Findings\nThematic grouping"]
CROSS["Cross-reference\nVerify across sources"]
IDENTIFY_GAPS["Identify Gaps\nWhat's still unknown"]
end
subgraph WRITER["Writing Agent"]
OUTLINE["Generate Outline\nExecutive summary"]
DRAFT["Draft Report\nStructured content"]
CITE["Add Citations\nEvery claim sourced"]
REVIEW["Self-Review\nFactuality check"]
end
PLANNER --> SEARCHER
SEARCHER --> READER
READER --> SYNTHESIZER
SYNTHESIZER --> WRITER
style PLANNER fill:#3b82f6,color:#fff
style SEARCHER fill:#f59e0b,color:#fff
style READER fill:#8b5cf6,color:#fff
style SYNTHESIZER fill:#6366f1,color:#fff
style WRITER fill:#22c55e,color:#fff

sequenceDiagram
participant U as User
participant P as Planner
participant S as Searcher
participant R as Reader
participant Synth as Synthesizer
participant W as Writer
U->>P: "Research: Impact of AI on healthcare"
P->>P: Decompose: (1) Clinical AI, (2) Diagnostics, (3) Drug discovery, (4) Ethics
P->>P: Generate 15 search queries
P->>S: Execute searches
S->>S: Search web (30 results)
S->>S: Search PubMed (20 papers)
S->>S: Search internal KB
S-->>P: 50 sources collected
P->>R: Read and extract sources
R->>R: Extract key facts per source
R->>R: Summarize each source
R->>R: Evaluate credibility
R-->>P: 50 source summaries
P->>Synth: Synthesize findings
Synth->>Synth: Group by theme
Synth->>Synth: Cross-reference claims
Synth->>Synth: Identify gaps
Synth-->>P: Synthesized analysis
P->>W: Generate report
W->>W: Create outline
W->>W: Draft each section
W->>W: Add citations
W->>W: Self-review
W-->>U: Final report

MethodEndpointPurpose
POST/api/researchStart new research task
GET/api/research/{id}Get research status
GET/api/research/{id}/reportGet generated report
GET/api/research/{id}/sourcesGet sources used
POST/api/research/{id}/refineRefine with follow-up
GET/api/research/historyPast research tasks

MetricMethodTarget
Report qualityHuman expert review> 4.0/5
Source diversityUnique sources used> 20
Factual accuracyVerified claims> 95%
Citation accuracySource matches claim> 95%
Time efficiencyTime vs manual research> 5x faster

ConcernImplementation
Source credibilityRate sources by domain authority
Bias detectionFlag potential bias in sources
Data privacyDon’t include PII in research output
API limitsRespect search API rate limits
AttributionClearly mark AI-generated content

Q: Design the multi-agent architecture for a research system.

Agents: (1) Planner — Decomposes question, generates strategy, tracks progress, (2) Search Agent — Executes searches across multiple APIs, deduplicates results, (3) Reader Agent — Extracts key information, evaluates source credibility, (4) Synthesis Agent — Combines findings, cross-references claims, identifies gaps, (5) Writer Agent — Generates structured report with citations, self-reviews. Each agent uses LangGraph for state management and error recovery.


FeatureImplementation
PlanningLLM decomposes question into sub-questions
Multi-source searchWeb + academic + internal
Reading & extractionLLM extracts and summarizes per source
SynthesisThematic grouping + cross-referencing
Report generationStructured report with citations
Agent frameworkLangGraph for orchestration
Fact-checkingCross-source verification

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