AI Agent for Research: Automate Literature Reviews, Data Collection & Analysis

Researchers spend 50% of their time finding and reading papers. An AI research agent does it in minutes — and doesn't miss anything. Here's how to build one.

Why Research Needs AI Agents (Not Just ChatGPT)

You've probably asked ChatGPT to summarize a paper. Maybe you've used Perplexity to find sources. That's not a research agent — that's a search engine with a chat interface.

A real research agent is fundamentally different:

The difference is like comparing Google Search to a research assistant you've trained for 6 months. One gives you links. The other gives you synthesized, verified knowledge.

Here's what a properly built research agent handles autonomously:

• Literature discovery — searches Semantic Scholar, arXiv, PubMed, Google Scholar across 200M+ papers
• Relevance filtering — scores papers on methodology quality, citation count, recency, and topic fit
• Data extraction — pulls key findings, methods, sample sizes, effect sizes into structured formats
• Cross-paper synthesis — identifies agreements, contradictions, and gaps across dozens of papers
• Citation chain analysis — follows references forward and backward to find related work
• Continuous monitoring — watches for new publications matching your research interests
• Draft generation — produces literature review sections with proper citations

The 5-Layer Research Agent Architecture

Most people try to build a research agent as a single prompt. That fails immediately — research is too complex for one-shot processing. You need layers:

┌─────────────────────────────────────────┐
│         Layer 5: Writing Assistant       │
│   Draft sections, format citations,      │
│   maintain consistent voice              │
├─────────────────────────────────────────┤
│       Layer 4: Monitoring & Alerts       │
│   Watch for new papers, track trends,    │
│   weekly digest generation               │
├─────────────────────────────────────────┤
│      Layer 3: Cross-Paper Synthesis      │
│   Compare findings, identify gaps,       │
│   build evidence maps                    │
├─────────────────────────────────────────┤
│      Layer 2: Data Extraction            │
│   Pull structured data from papers:      │
│   methods, findings, stats, limitations  │
├─────────────────────────────────────────┤
│     Layer 1: Literature Discovery        │
│   Search APIs, filter relevance,         │
│   manage paper database                  │
└─────────────────────────────────────────┘

Each layer has its own tools, prompts, and quality checks. Let's build each one.

Layer 1: Automated Literature Review

The foundation. Your agent needs to search academic databases, filter results, and build a paper database. Here's the architecture:

import anthropic
import httpx

client = anthropic.Anthropic()

async def search_semantic_scholar(query: str, limit: int = 20):
    """Search Semantic Scholar for papers."""
    url = "https://api.semanticscholar.org/graph/v1/paper/search"
    params = {
        "query": query,
        "limit": limit,
        "fields": "title,abstract,year,citationCount,authors,"
                  "url,venue,publicationTypes,openAccessPdf,"
                  "tldr,referenceCount"
    }
    async with httpx.AsyncClient() as http:
        resp = await http.get(url, params=params)
        return resp.json().get("data", [])

async def search_arxiv(query: str, max_results: int = 20):
    """Search arXiv for preprints."""
    import feedparser
    url = f"http://export.arxiv.org/api/query?search_query=all:{query}"
    url += f"&max_results={max_results}&sortBy=relevance"
    async with httpx.AsyncClient() as http:
        resp = await http.get(url)
        feed = feedparser.parse(resp.text)
        return [{
            "title": e.title,
            "abstract": e.summary,
            "authors": [a.name for a in e.authors],
            "url": e.link,
            "published": e.published,
            "categories": [t.term for t in e.tags]
        } for e in feed.entries]

def score_relevance(paper: dict, research_question: str) -> float:
    """Score paper relevance 0-1 using the LLM."""
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=100,
        messages=[{
            "role": "user",
            "content": f"""Score this paper's relevance to the research question.
Research question: {research_question}
Paper title: {paper['title']}
Abstract: {paper.get('abstract', 'N/A')}

Reply with ONLY a number between 0.0 and 1.0."""
        }]
    )
    return float(response.content[0].text.strip())

The key insight: don't rely on a single search query. Your agent should generate 5-10 query variations from your research question, search across multiple databases, deduplicate by DOI, then rank by relevance.

async def comprehensive_search(research_question: str):
    """Multi-query, multi-source search."""
    # Step 1: Generate query variations
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=500,
        messages=[{
            "role": "user",
            "content": f"""Generate 5 different search queries for this
research question. Use different terminology, synonyms, and angles.
Return as JSON array of strings.

Research question: {research_question}"""
        }]
    )
    queries = json.loads(response.content[0].text)

    # Step 2: Search all sources with all queries
    all_papers = []
    for query in queries:
        all_papers += await search_semantic_scholar(query)
        all_papers += await search_arxiv(query)
        await asyncio.sleep(1)  # Rate limiting

    # Step 3: Deduplicate by title similarity
    unique = deduplicate_papers(all_papers)

    # Step 4: Score and rank
    for paper in unique:
        paper["relevance"] = score_relevance(paper, research_question)

    return sorted(unique, key=lambda p: p["relevance"], reverse=True)

Layer 2: Data Extraction & Structuring

Finding papers is step one. The real value is extracting structured data from each paper so your agent can reason across them.

For each relevant paper, your agent extracts:

{
  "paper_id": "doi:10.1234/example",
  "title": "...",
  "research_question": "What did this paper investigate?",
  "methodology": {
    "type": "RCT | observational | meta-analysis | survey | ...",
    "sample_size": 1500,
    "population": "Adults aged 25-65 in urban areas",
    "duration": "12 months",
    "controls": "Placebo group, n=750"
  },
  "key_findings": [
    {
      "claim": "Treatment X reduced outcome Y by 23%",
      "evidence": "p < 0.001, 95% CI [18%, 28%]",
      "effect_size": 0.45,
      "confidence": "high"
    }
  ],
  "limitations": [
    "Self-reported data",
    "Single geographic region"
  ],
  "future_work": ["Longitudinal follow-up needed"],
  "cited_by_count": 142,
  "references_of_interest": ["doi:...", "doi:..."]
}

This is where most AI research tools fail. They summarize — your agent structures. The difference matters when you're comparing 50 papers.

def extract_paper_data(paper_text: str, research_question: str) -> dict:
    """Extract structured data from a paper."""
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=2000,
        messages=[{
            "role": "user",
            "content": f"""Extract structured research data from this paper.
Focus on findings relevant to: {research_question}

Paper text:
{paper_text[:15000]}

Return as JSON with these fields:
- research_question (what the paper investigated)
- methodology (type, sample_size, population, duration, controls)
- key_findings (array of claim, evidence, effect_size, confidence)
- limitations (array of strings)
- future_work (array of strings)
- relevance_to_my_question (0-1 score with explanation)

Be precise. If data isn't stated, use null. Never fabricate statistics."""
        }]
    )
    return json.loads(response.content[0].text)

Layer 3: Cross-Paper Synthesis

This is the most powerful layer — and what separates a research agent from a fancy search tool. Given structured data from 20-50 papers, your agent identifies:

• Consensus — what do most papers agree on?
• Contradictions — where do findings conflict? Why?
• Gaps — what hasn't been studied?
• Trends — how has the field evolved over time?
• Methodology patterns — which methods produce stronger results?

def synthesize_findings(papers: list[dict], research_question: str):
    """Cross-paper synthesis to identify patterns."""
    # Prepare structured summaries
    summaries = "\n\n".join([
        f"Paper: {p['title']} ({p['year']})\n"
        f"Method: {p['methodology']['type']}, n={p['methodology']['sample_size']}\n"
        f"Findings: {json.dumps(p['key_findings'])}\n"
        f"Limitations: {', '.join(p['limitations'])}"
        for p in papers
    ])

    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=4000,
        messages=[{
            "role": "user",
            "content": f"""Synthesize findings from {len(papers)} papers on:
{research_question}

{summaries}

Provide:
1. CONSENSUS: What do most papers agree on? (cite specific papers)
2. CONTRADICTIONS: Where do findings conflict? Explain possible reasons
   (methodology differences, population differences, etc.)
3. GAPS: What important questions remain unanswered?
4. EVIDENCE STRENGTH: Rate overall evidence as Strong/Moderate/Weak
   with explanation
5. TRENDS: How have findings/methods evolved over time?
6. RECOMMENDED READING: Top 5 must-read papers and why

Be specific. Cite papers by author and year. Flag any potential biases."""
        }]
    )
    return response.content[0].text

Layer 4: Continuous Monitoring & Alerts

Research doesn't stop after your initial review. Your agent should watch for new publications and alert you when something relevant drops.

import schedule
import json
from datetime import datetime, timedelta

class ResearchMonitor:
    def __init__(self, research_topics: list[dict]):
        self.topics = research_topics  # [{query, min_relevance}]
        self.seen_papers = self.load_seen()

    async def check_new_papers(self):
        """Run daily to find new relevant papers."""
        new_finds = []
        yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")

        for topic in self.topics:
            papers = await search_semantic_scholar(
                topic["query"], limit=50
            )
            for paper in papers:
                if paper["paperId"] in self.seen_papers:
                    continue
                relevance = score_relevance(paper, topic["query"])
                if relevance >= topic["min_relevance"]:
                    new_finds.append({
                        **paper,
                        "relevance": relevance,
                        "topic": topic["query"]
                    })
                self.seen_papers.add(paper["paperId"])

        if new_finds:
            self.send_digest(new_finds)
        self.save_seen()

    def send_digest(self, papers: list):
        """Send weekly research digest."""
        digest = "# 📚 Weekly Research Digest\n\n"
        for p in sorted(papers, key=lambda x: x["relevance"], reverse=True):
            digest += f"### {p['title']}\n"
            digest += f"**Relevance:** {p['relevance']:.0%} | "
            digest += f"**Citations:** {p.get('citationCount', 'N/A')}\n"
            digest += f"{p.get('tldr', {}).get('text', p.get('abstract', '')[:200])}\n"
            digest += f"[Read paper]({p['url']})\n\n"
        # Send via email, Slack, or save to file
        return digest

# Usage
monitor = ResearchMonitor([
    {"query": "large language model reasoning", "min_relevance": 0.7},
    {"query": "AI agent tool use", "min_relevance": 0.8},
])

Layer 5: Research Writing Assistant

The final layer turns your structured data and synthesis into actual writing — literature review sections, research summaries, or briefing documents.

def write_literature_review_section(
    topic: str,
    papers: list[dict],
    synthesis: str,
    style: str = "academic"  # academic | business | technical
):
    """Generate a literature review section with verified citations."""
    paper_refs = "\n".join([
        f"[{i+1}] {p['authors'][0]} et al. ({p['year']}). "
        f"{p['title']}. {p.get('venue', 'N/A')}."
        for i, p in enumerate(papers)
    ])

    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=3000,
        messages=[{
            "role": "user",
            "content": f"""Write a literature review section on: {topic}

Style: {style}

Available papers (ONLY cite these):
{paper_refs}

Synthesis of findings:
{synthesis}

Rules:
- Cite papers as [1], [2], etc. — ONLY from the list above
- Group by theme, not chronologically
- Highlight contradictions and gaps
- Use hedging language where evidence is weak
- End with identified gaps that motivate further research
- {
    'Use formal academic tone, passive voice where appropriate'
    if style == 'academic' else
    'Use clear, direct language accessible to non-experts'
}"""
        }]
    )
    return response.content[0].text

Production System Prompt for Research Agents

This system prompt turns Claude into a rigorous research assistant. Copy and adapt for your domain:

SYSTEM_PROMPT = """You are a research agent specializing in systematic
literature review and evidence synthesis.

## Core Principles

1. NEVER fabricate citations or statistics. If you don't have the data,
   say so explicitly.
2. Always distinguish between: established consensus, emerging evidence,
   single-study findings, and your own inference.
3. Use hedging language appropriately: "suggests", "indicates",
   "preliminary evidence shows" — match confidence to evidence strength.
4. When papers contradict each other, explain possible reasons
   (methodology, population, timeframe) rather than picking a winner.
5. Flag potential biases: funding sources, small samples, p-hacking
   indicators, publication bias.

## Available Tools

- search_papers(query, sources, limit) → Find papers across databases
- get_paper_details(paper_id) → Full paper text and metadata
- extract_data(paper_id, schema) → Structured data extraction
- search_citations(paper_id, direction) → Forward/backward citation search
- save_to_database(paper_data) → Store structured paper data
- generate_evidence_map(topic) → Visual map of evidence for/against

## Workflow

When asked to research a topic:
1. Clarify the research question and scope
2. Generate 5-8 search queries (synonyms, related terms, specific + broad)
3. Search across all available databases
4. Filter by relevance (threshold: 0.6)
5. Extract structured data from top 30 papers
6. Synthesize: consensus, contradictions, gaps, trends
7. Present findings with confidence levels and citations

## Citation Format
Use [Author, Year] for in-text. Maintain a reference list that maps to
actual papers in the database. NEVER generate references from memory.

## Quality Checks
Before presenting any finding, verify:
- The paper actually exists (has a DOI or URL)
- The statistic is from the paper (not hallucinated)
- The sample size and methodology support the claim strength
- You've noted relevant limitations
"""

Tool Comparison: 8 Research AI Tools

Tool	Best For	Price	API Access
Semantic Scholar	Paper search & citation data	Free	✅ Full API
Elicit	Systematic reviews, data extraction	Free / $10+/mo	❌ No API
Consensus	Evidence-based answers from papers	Free / $9/mo	❌ No API
Perplexity	Quick research with citations	Free / $20/mo	✅ API available
Scite.ai	Citation context (supporting/contrasting)	$20/mo	✅ API available
Connected Papers	Visual paper graph exploration	Free / $6/mo	❌ No API
OpenAlex	Broadest open dataset (250M+ works)	Free	✅ Full API
Custom Agent (this guide)	Full control, all sources combined	~$5-20/mo (API costs)	✅ You build it

Our recommendation: Start with Elicit or Consensus for quick research. Build your own agent when you need cross-database search, custom extraction schemas, continuous monitoring, or integration into your existing workflow.

Build Your Research Agent in 60 Minutes

Here's a minimal but functional research agent you can deploy today:

5 Research Agent Use Cases

7 Mistakes That Kill Research Agent Accuracy

What's Next

You now have the architecture for a research agent that can:

1. Search 200M+ papers across multiple databases
2. Extract structured data from each paper
3. Synthesize findings and identify contradictions
4. Monitor for new publications automatically
5. Generate literature review sections with verified citations

Start with the 60-minute build. Get it working for one research question. Then expand: add more databases, refine your extraction schemas, set up monitoring for your key topics.

The researchers who adopt AI agents now will have a significant advantage — not because AI replaces research judgment, but because it eliminates the bottleneck of finding and organizing information, freeing you to do what humans do best: think critically and ask better questions.