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Research & Analysis

Overview

Researchers, analysts, and knowledge workers spend hours finding, reading, and synthesizing information.

Agents dramatically accelerate this work.

Literature Review & Paper Analysis

class ResearchAgent:
 """Automate literature review"""

 def find_relevant_papers(self, research_question):
 """Search literature for relevant papers"""

 # Decompose question into search queries
 queries = self.decompose_research_question(research_question)

 papers = []

 for query in queries:
 # Search academic databases
 results = self.search_papers(query)

 # Filter relevant papers
 relevant = self.filter_relevant(results, research_question)

 papers.extend(relevant)

 # Rank by relevance
 ranked = self.rank_by_relevance(papers, research_question)

 return ranked[:50] # Top 50

 def analyze_paper(self, paper_pdf):
 """Extract key information from paper"""

 analysis = {
 'title': self.extract_title(paper_pdf),
 'authors': self.extract_authors(paper_pdf),
 'abstract': self.extract_abstract(paper_pdf),
 'key_findings': self.extract_key_findings(paper_pdf),
 'methodology': self.extract_methodology(paper_pdf),
 'limitations': self.extract_limitations(paper_pdf),
 'references': self.extract_references(paper_pdf)
 }

 return analysis

 def synthesize_findings(self, papers, research_question):
 """Combine findings across papers"""

 analyses = [self.analyze_paper(p) for p in papers]

 synthesis_prompt = f"""
 Research question: {research_question}

 Key findings from {len(analyses)} papers:
 {format_findings(analyses)}

 Synthesize:
 1. What's the consensus?
 2. What's controversial?
 3. What gaps remain?
 4. What's the next frontier?
 """

 synthesis = self.llm.call(synthesis_prompt)

 return {
 'summary': synthesis,
 'cited_papers': papers,
 'gaps': self.identify_gaps(analyses)
 }

Business Impact:

  • Complete literature review in hours (vs weeks)
  • Never miss relevant papers
  • Identify research gaps automatically
  • ROI: 400-600% annual

Data Analysis & Visualization

Automated Analysis

class AnalysisAgent:
 """Automate data analysis"""

 def analyze_dataset(self, data):
 """Comprehensive data analysis"""

 # Step 1: Explore data
 exploration = {
 'shape': data.shape,
 'dtypes': data.dtypes,
 'missing': data.isnull().sum(),
 'basic_stats': data.describe()
 }

 # Step 2: Identify patterns
 patterns = self.find_patterns(data)

 # Step 3: Generate visualizations
 visualizations = self.generate_visualizations(data, patterns)

 # Step 4: Statistical analysis
 statistics = self.run_statistical_tests(data)

 # Step 5: Generate insights
 insights = self.generate_insights(
 exploration,
 patterns,
 statistics
)

 return {
 'exploration': exploration,
 'patterns': patterns,
 'insights': insights,
 'visualizations': visualizations
 }

 def generate_insights(self, exploration, patterns, stats):
 """Extract meaningful insights"""

 insights_prompt = f"""
 Data exploration:
 {exploration}

 Patterns found:
 {patterns}

 Statistical tests:
 {stats}

 What are the key insights?
 1. Most important finding?
 2. Surprising patterns?
 3. Actionable recommendations?
 """

 insights = self.llm.call(insights_prompt)
 return self.parse_insights(insights)

 def generate_report(self, analysis):
 """Create comprehensive report"""

 report = f"""
 Data Analysis Report

 Summary:
 {analysis['exploration']}

 Key Patterns:
 {format_patterns(analysis['patterns'])}

 Insights:
 {format_insights(analysis['insights'])}

 Visualizations:
 [Generated charts here]

 Recommendations:
 {self.extract_recommendations(analysis['insights'])}
 """

 return report

Business Impact:

  • 70% faster analysis
  • More thorough exploration
  • Better visualizations
  • Actionable insights extracted automatically
  • ROI: 300-500% annual

Competitive Intelligence

Market Research Automation

class IntelligenceAgent:
 """Automated competitive intelligence"""

 def analyze_competitor(self, company_name):
 """Comprehensive competitor analysis"""

 # Gather information
 company_info = self.research_company(company_name)
 news = self.gather_recent_news(company_name)
 social_media = self.analyze_social_media(company_name)
 financial = self.get_financial_data(company_name)

 # Analyze
 analysis = {
 'overview': company_info,
 'recent_news': news,
 'public_sentiment': social_media,
 'financial_health': financial,
 'strengths': self.identify_strengths(company_info),
 'weaknesses': self.identify_weaknesses(company_info),
 'threats': self.identify_threats(company_info),
 'opportunities': self.identify_opportunities(company_info)
 }

 return analysis

 def identify_threats(self, company_info):
 """What threats does this competitor pose?"""

 threat_prompt = f"""
 Competitor: {company_info}

 Analyze threats to our business:
 1. Direct competition in our markets?
 2. Threats to our market position?
 3. Technology threats?
 4. Customer acquisition threats?

 Be specific and quantify if possible.
 """

 threats = self.llm.call(threat_prompt)
 return self.parse_threats(threats)

Business Impact:

  • Real-time competitive monitoring
  • Identify threats earlier
  • Understand market positioning
  • ROI: 200-400% annual

Report Generation

Automated Report Creation

class ReportAgent:
 """Automatically generate reports"""

 def generate_quarterly_report(self, company_data):
 """Create executive summary"""

 # Gather metrics
 kpis = self.extract_kpis(company_data)
 trends = self.analyze_trends(company_data)
 alerts = self.identify_alerts(company_data)

 # Generate sections
 report_sections = {
 'executive_summary': self.generate_summary(kpis),
 'key_metrics': self.format_metrics(kpis),
 'trends': self.analyze_and_describe_trends(trends),
 'alerts': self.highlight_alerts(alerts),
 'recommendations': self.generate_recommendations(alerts),
 'next_steps': self.suggest_next_steps(trends)
 }

 # Combine into report
 report = self.format_report(report_sections)

 return report

Business Impact:

  • Reports created 90% faster
  • Always current and consistent
  • Executives get more time for strategy
  • ROI: 150-300% annual

3 Warnings

Warning 1: Hallucinated Citations

# WRONG
# Agent generates fake citations
citations = agent.extract_citations(paper)
# But some citations don't exist!

# Research compromised

# RIGHT
# Verify all citations
citations = agent.extract_citations(paper)
for citation in citations:
 verify_citation_exists(citation)
 if not found:
 flag_as_uncertain()

Warning 2: Over-Relying on Summaries

# WRONG
# Agent summarizes paper
summary = agent.summarize_paper(paper)
# But key details are missed!

# Understanding is incomplete

# RIGHT
# Use summary as starting point
summary = agent.summarize_paper(paper)
# But still read actual paper
read_full_paper(paper)
verify_summary_accuracy()

Warning 3: Biased Data Analysis

# WRONG
# Agent analyzes biased data
data = biased_dataset()
analysis = agent.analyze(data)
# Conclusion reinforces bias

# RIGHT
# Check for data bias first
bias_analysis = agent.detect_bias(data)
if bias_analysis.has_bias:
 flag_and_correct()
else:
 analysis = agent.analyze(data)

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Last Updated: August 9, 2026