User: "Analyze this sales data and create a comprehensive report"
[Uploads: sales_data.csv]

DeepAgent creates tasks:
├── Load and validate sales_data.csv
├── Clean data (remove duplicates, handle missing values)
├── Exploratory data analysis
│   ├── Calculate summary statistics
│   ├── Identify trends
│   └── Detect anomalies
├── Generate visualizations
│   ├── Sales over time chart
│   ├── Top products bar chart
│   ├── Regional breakdown map
│   └── Customer segment analysis
├── Perform statistical analysis
│   ├── Growth rate calculation
│   ├── Seasonality detection
│   └── Forecasting
└── Write comprehensive report

Files created in workspace:
├── sales_data_cleaned.csv
├── analysis_script.py
├── sales_over_time.png
├── top_products.png
├── regional_breakdown.png
├── customer_segments.png
├── statistical_summary.json
└── sales_analysis_report.md

Daily Automated Analysis:
1. Load previous day's orders
2. Calculate key metrics:
   - Revenue, AOV, conversion rate
   - Top products, categories
   - Customer segments
3. Generate visualizations
4. Compare to targets and trends
5. Email executive summary
6. Alert on anomalies

Workspace contains:
├── Historical data (30 days)
├── Daily reports
├── Trend charts
├── Anomaly alerts
└── Forecasting models

User: "Analyze this campaign performance"
[Uploads: campaign_data.csv]

DeepAgent process:
1. Load and validate data ✓
2. Calculate metrics:
   - CTR, CPC, CPA, ROAS
   - Engagement rates
   - Conversion funnel
3. Generate comparison charts:
   - vs. previous campaigns
   - vs. industry benchmarks
   - vs. targets
4. Identify insights:
   - Best performing audiences
   - Optimal ad creatives
   - Time-of-day patterns
5. Provide recommendations
6. Create client-ready presentation

Output:
├── campaign_analysis.pptx
├── performance_dashboard.html
├── detailed_metrics.csv
├── recommendation_report.md
└── visualization_pack.zip

Monthly Task (First business day):
1. Fetch financial data (revenue, expenses, cash flow)
2. Clean and standardize
3. Apply forecasting models:
   - Time series analysis
   - Regression models
   - Moving averages
4. Generate scenarios:
   - Best case
   - Base case
   - Worst case
5. Create visualization dashboard
6. Write CFO brief

DeepAgent workspace:
├── historical_data/ (24 months)
├── forecasting_models/
├── monthly_reports/
├── scenario_analyses/
└── presentation_decks/

User: "Show me sales by region"
Agent: [Generates chart] "Here's the breakdown. North region leads with $2.3M (38%)"

User: "Drill down into North region by product"
Agent: [Generates detailed chart] "Top 3 products in North:
        1. Product A: $850K
        2. Product B: $720K
        3. Product C: $450K"

User: "Compare Product A performance across all regions"
Agent: [Generates comparison chart] "Product A analysis:
        - Strong in North (37% market share)
        - Growing in West (22%, +15% MoM)
        - Opportunity in South (only 8%)"

All charts saved to workspace for later reference.

User: "Analyze correlation between marketing spend and revenue"
[Uploads: marketing_spend.csv, revenue.csv]

DeepAgent:
1. Load both datasets
2. Merge on date
3. Calculate correlations
4. Regression analysis
5. Visualize relationships
6. Identify optimal spend levels
7. Generate actionable insights

Creates:
├── merged_data.csv
├── correlation_matrix.png
├── regression_analysis.png
├── spend_optimization.png
└── insights_report.md

DeepAgent creates:
- Python scripts for repeatable analysis
- SQL queries for database extraction
- Data transformation pipelines
- Visualization code

All saved to workspace:
├── analysis.py
├── data_extraction.sql
├── transform_pipeline.py
└── visualizations.py

You can:
- Download and run locally
- Modify for variations
- Share with team
- Schedule execution