Overview
Multi-agent data analysis systems automate the analytics workflow, from raw data extraction through insight generation. Each agent specializes in a phase of the data pipeline.
Architecture
Data Sources → Extraction Agent → Raw Data
↓
Cleaning Agent → Clean Data
↓
Analysis Agent → Metrics
↓
Statistics Agent → Statistical Analysis
↓
Visualization Agent → Charts/Graphs
↓
Insight Generator → Key Findings
↓
Report Writer → Final Report
Agent Roles
Data Extraction Agent
- Connects to various data sources
- Executes queries
- Handles API integrations
Data Cleaning Agent
- Identifies and handles missing values
- Removes duplicates
- Standardizes formats
Analysis Agent
- Calculates key metrics
- Performs aggregations
- Identifies trends
Statistics Agent
- Runs statistical tests
- Calculates confidence intervals
- Identifies significant findings
Visualization Agent
- Creates appropriate chart types
- Ensures clear communication
- Handles formatting
Insight Generation Agent
- Synthesizes findings into actionable insights
- Identifies key takeaways
- Highlights anomalies
Report Writer Agent
- Structures final report
- Writes executive summary
- Ensures accessibility
Enterprise Applications
- Business Intelligence: Automated dashboard updates
- Marketing Analytics: Campaign performance analysis
- Operations: KPI monitoring and alerting
- Finance: Financial reporting automation
Key Patterns
- Tool Use Pattern: Database queries, APIs, visualization libraries
- Handoff Pattern: Data artifacts passed between agents
- Human-in-the-Loop: Validation of significant findings
Evaluation Challenges
- Statistical validity requires domain expertise
- Insight quality is subjective
- Visualization effectiveness varies by audience
- Report usefulness depends on business context