Research & Analysis: Knowledge Work Automation¶
Overview¶
Researchers, analysts, and knowledge workers spend hours finding, reading, and synthesizing information.
Agents dramatically accelerate this work.
Literature Review & Paper Analysis¶
Automated Literature Search¶
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)
Last Updated: August 9, 2026