10. Build an AI Research Agent
Introduction
Section titled “Introduction”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.
Problem Statement
Section titled “Problem Statement”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
Business Use Case
Section titled “Business Use Case”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.
Requirements
Section titled “Requirements”Functional Requirements
Section titled “Functional Requirements”| # | Feature | Description |
|---|---|---|
| FR1 | Research planning | Decompose question into sub-questions |
| FR2 | Multi-source search | Web, academic, internal knowledge base |
| FR3 | Content extraction | Read and extract key information |
| FR4 | Synthesis | Combine findings into coherent analysis |
| FR5 | Fact-checking | Verify claims across multiple sources |
| FR6 | Citation generation | Every claim linked to source |
| FR7 | Report generation | Structured report with executive summary |
| FR8 | Iterative refinement | Follow-up questions and deep dives |
Non-Functional Requirements
Section titled “Non-Functional Requirements”| # | Requirement | Target |
|---|---|---|
| NFR1 | Research time | < 15 min for complex topic |
| NFR2 | Source coverage | 20-50 sources per research |
| NFR3 | Accuracy | > 95% factually correct |
| NFR4 | Citation accuracy | Every claim has verifiable source |
| NFR5 | Scalability | Multiple concurrent research tasks |
Technology Stack
Section titled “Technology Stack”| Layer | Technology | Purpose |
|---|---|---|
| Frontend | Next.js + Tailwind | Research dashboard |
| Backend | FastAPI (Python) | Agent orchestration |
| Agent Framework | LangGraph | Agent workflow management |
| Search | SerpAPI, Bing, PubMed, arXiv | Multi-source search |
| AI | GPT-4o / Claude | Planning, reading, synthesis |
| Vector DB | Qdrant | Research document storage |
| Database | PostgreSQL | Research records, reports |
| Queue | Celery + Redis | Async research tasks |
| Cache | Redis | Search result caching |
Agent Architecture
Section titled “Agent Architecture”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:#fffResearch Workflow
Section titled “Research Workflow”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 reportAPI Design
Section titled “API Design”| Method | Endpoint | Purpose |
|---|---|---|
| POST | /api/research | Start new research task |
| GET | /api/research/{id} | Get research status |
| GET | /api/research/{id}/report | Get generated report |
| GET | /api/research/{id}/sources | Get sources used |
| POST | /api/research/{id}/refine | Refine with follow-up |
| GET | /api/research/history | Past research tasks |
Evaluation
Section titled “Evaluation”| Metric | Method | Target |
|---|---|---|
| Report quality | Human expert review | > 4.0/5 |
| Source diversity | Unique sources used | > 20 |
| Factual accuracy | Verified claims | > 95% |
| Citation accuracy | Source matches claim | > 95% |
| Time efficiency | Time vs manual research | > 5x faster |
Security
Section titled “Security”| Concern | Implementation |
|---|---|
| Source credibility | Rate sources by domain authority |
| Bias detection | Flag potential bias in sources |
| Data privacy | Don’t include PII in research output |
| API limits | Respect search API rate limits |
| Attribution | Clearly mark AI-generated content |
Interview Questions
Section titled “Interview Questions”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.
Summary
Section titled “Summary”| Feature | Implementation |
|---|---|
| Planning | LLM decomposes question into sub-questions |
| Multi-source search | Web + academic + internal |
| Reading & extraction | LLM extracts and summarizes per source |
| Synthesis | Thematic grouping + cross-referencing |
| Report generation | Structured report with citations |
| Agent framework | LangGraph for orchestration |
| Fact-checking | Cross-source verification |
Navigation
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