Business Management: Intelligence Agent
Build an AI agent that provides competitive intelligence and business insights
Transform Your Agent into a Business Strategist
A business intelligence agent acts as your strategic advisor, analyzing market data, tracking competitors, and generating insights from complex business metrics. Unlike passive analytics dashboards, an AI agent can answer natural language questions, proactively surface trends, and synthesize information across multiple data sources.
This chapter shows how to configure your agent with business-focused tools and memory to support competitive analysis, metric tracking, and executive decision-making.
Business Agent Configuration
Configure your agent's system prompt to establish business expertise and analytical behavior:
system_prompt = """You are a business intelligence analyst specializing in competitive research and market insights.
Focus on data-driven analysis, identify trends, and provide actionable recommendations."""This simple prompt shift transforms your agent's tone from academic researcher to strategic analyst, changing how it interprets questions and presents findings.
Core Business Tools
Market Research Tool
Integrate web search to gather competitive intelligence and industry news:
def search_market_data(query: str) -> dict:
results = web_search(query)
return {"insights": results}The agent uses this when asked about competitors, market trends, or industry developments, automatically formulating search queries from business questions.
Metrics Analysis Tool
Add calculation capabilities for business KPIs and financial analysis:
def analyze_metrics(metric: str, values: list) -> dict:
trend = calculate_growth_rate(values)
return {"metric": metric, "trend": trend}This enables the agent to compute growth rates, compare performance across quarters, and identify anomalies in business metrics.
Report Generator
Create a tool for synthesizing insights into executive summaries:
def generate_summary(topic: str, data: dict) -> str:
key_points = extract_insights(data)
return format_executive_brief(topic, key_points)The agent can transform raw data and research findings into concise briefings suitable for decision-makers.
Competitive Analysis Workflow
Competitor Monitoring
Ask your agent to track specific competitors and market segments:
User: "What are the latest product announcements from our top 3 competitors in cloud AI services?"
Agent workflow: The agent searches news sources for each competitor, filters for product launches, and compares feature sets against your offerings.
Trend Identification
Enable pattern recognition across market data:
User: "Analyze our quarterly revenue growth compared to industry benchmarks."
Agent workflow: The agent retrieves historical data, calculates growth rates, searches for industry reports, and highlights whether performance exceeds or lags sector averages.
Strategic Recommendations
Leverage the agent's synthesis capabilities for decision support:
User: "Should we enter the Southeast Asian market based on current competition?"
Agent workflow: The agent researches market size, identifies existing players, analyzes barriers to entry, and provides a structured recommendation with supporting evidence.
Business Memory and Context
Business Context Memory: Configure your agent to remember your company's competitive landscape, key performance indicators, and strategic priorities. This eliminates repetitive context-setting and enables the agent to provide insights aligned with your business model. For example, after you specify your target customer segments once, the agent automatically filters market research to focus on those demographics in future analyses.
Store business context in the agent's conversation history:
context = {
"competitors": ["CompanyA", "CompanyB"],
"kpis": ["CAC", "LTV", "churn_rate"],
"market": "enterprise_saas"
}Key Capabilities Summary
What Business Agents Excel At: Business intelligence agents transform hours of manual research into minutes of conversational analysis. They excel at competitive monitoring by continuously tracking rival companies and surfacing relevant changes, trend analysis by identifying patterns across market data and internal metrics, and executive reporting by synthesizing complex information into decision-ready summaries. The combination of web access, calculation tools, and business-tuned prompts creates an agent that thinks like a strategic analyst rather than a generic chatbot.
Practical Applications
Use case: Daily competitor monitoring and strategic positioning analysis.
Example questions:
- "What pricing changes have our competitors made this quarter?"
- "Which features do competitors offer that we lack?"
- "Summarize competitor positioning from their latest marketing materials."
Agent advantages: Continuous monitoring across multiple sources, pattern recognition in competitor behavior, synthesis of scattered information into strategic insights.
Use case: KPI tracking and anomaly detection across business metrics.
Example questions:
- "Why did customer acquisition cost spike last month?"
- "Compare our growth rate to industry averages for SaaS companies."
- "Which product lines are underperforming relative to forecasts?"
Agent advantages: Automated metric calculation, cross-dataset analysis, natural language access to complex analytics without BI dashboards.
Use case: Market sizing, trend identification, and opportunity assessment.
Example questions:
- "What's the market size for AI-powered research tools in academia?"
- "Identify emerging trends in enterprise knowledge management."
- "Should we build mobile apps or focus on web platform?"
Agent advantages: Rapid research synthesis, multi-source validation, framework-based analysis (TAM/SAM/SOM, Porter's Five Forces).
From Generic to Domain-Specific
Specialization Through Configuration: The same agent architecture serves economics researchers, software engineers, and business analysts by changing system prompts, tool selection, and memory structure. For business intelligence, the key differentiators are access to competitive data sources, financial calculation capabilities, and training to present insights in executive-friendly formats. This specialization happens at the configuration layer without modifying core agent logic.
Next Steps
Your business intelligence agent now provides strategic insights, competitive monitoring, and data analysis through conversational interaction. In the next chapter, you'll learn advanced patterns for multi-turn planning, tool chaining, and proactive suggestions that enhance agent capabilities across all domains.