sf-agent-framework

# Metrics Tracker Utility - Agent Instructions ## Purpose This utility provides instructions for AI agents to generate comprehensive metrics tracking solutions for Salesforce implementations, enabling organizations to monitor KPIs, track performance, and measure business outcomes effectively. ## Agent Instructions ### When to Generate Metrics Tracking Generate metrics tracking components when: - Organizations need KPI monitoring - Performance metrics require tracking - Business outcomes need measurement - Center of Excellence needs metrics - Adoption tracking is required - ROI calculation is needed - Compliance metrics need monitoring ### Core Components to Generate #### 1. Metrics Collection Framework Generate collection components that: - Automate metric data gathering - Schedule periodic collections - Handle real-time metric events - Process batch calculations - Store historical data - Manage metric snapshots Key collection patterns: - Scheduled batch collection - Real-time event tracking - API-based collection - User activity monitoring - System performance capture - Business process metrics #### 2. Metric Calculation Engine Create calculation components for: - Complex formula processing - Statistical calculations - Trend analysis - Predictive modeling - Aggregation logic - Weighted scoring #### 3. Metrics Storage Architecture Implement storage for: - Current metric values - Historical data retention - Trend information - Benchmark data - Target tracking - Alert thresholds ### Configuration Requirements #### Custom Objects ```yaml Metric_Definition__c: - Name (Text) - Category__c (Picklist) - Description__c (Text Area) - Formula__c (Long Text Area) - Unit__c (Picklist) - Frequency__c (Picklist) - Target__c (Number) - Threshold__c (Number) - Owner__c (Lookup to User) - Data_Source__c (Text) - Active__c (Checkbox) Metric_Value__c: - Metric_Definition__c (Master-Detail) - Value__c (Number) - Date__c (Date) - Period__c (Text) - Trend__c (Number) - Status__c (Picklist) - Notes__c (Text Area) Metric_Alert_Rule__c: - Metric_Definition__c (Lookup) - Operator__c (Picklist) - Threshold_Value__c (Number) - Alert_Recipients__c (Text) - Alert_Message__c (Text Area) - Active__c (Checkbox) ``` ### Metric Categories to Support #### Operational Metrics - System performance indicators - User adoption rates - Data quality scores - Process efficiency metrics - Support effectiveness #### Business Metrics - Revenue impact - Cost savings - Productivity gains - Customer satisfaction - Time to market #### Technical Metrics - Code quality scores - Test coverage percentages - API performance - Governor limit usage - Security compliance #### CoE Metrics - Service delivery rates - Innovation index - Knowledge sharing metrics - Standardization rates - Platform maturity ### Calculation Formulas to Implement #### User Adoption Rate ``` Adoption Rate = (Active Users / Total Users) × 100 Where Active Users = Users logged in within period ``` #### Data Quality Score ``` Quality Score = ((Total Records - Quality Issues) / Total Records) × 100 Where Quality Issues = Duplicates + Incomplete + Invalid ``` #### ROI Calculation ``` ROI = ((Benefits - Costs) / Costs) × 100 ``` #### Platform Efficiency ``` Efficiency = Weighted Average of: - API Usage (% of limit) - Storage Usage (% of limit) - License Utilization (% used) ``` ### Implementation Patterns #### Scheduled Collection Pattern 1. Define collection schedule 2. Query metric data sources 3. Apply calculations 4. Store metric values 5. Check alert conditions 6. Update dashboards #### Real-time Event Pattern 1. Capture platform events 2. Process metric updates 3. Calculate running totals 4. Update live dashboards 5. Trigger instant alerts #### Historical Analysis Pattern 1. Retrieve historical data 2. Calculate trends 3. Perform regression analysis 4. Generate predictions 5. Identify patterns ### Dashboard Components to Generate #### Executive Metrics View Display: - Key metric cards - Trend indicators - Target vs actual - Period comparisons - Alert status - Quick insights #### Operational Dashboard Show: - Real-time metrics - Process indicators - Team performance - System health - Queue metrics - SLA tracking #### Analytical View Include: - Historical trends - Correlation analysis - Predictive models - What-if scenarios - Comparative analysis - Export capabilities ### Alert Configuration #### Alert Rules Engine Generate alerts for: - Threshold breaches - Trend deviations - Target misses - Anomaly detection - SLA violations - Compliance issues #### Notification Channels - Email alerts - Chatter posts - Mobile push - Slack integration - Teams notifications - SMS for critical ### Reporting Templates #### Executive Report ``` Platform Health Score: [Score]% [Trend] User Adoption: [Value]% [Status] Data Quality: [Value]% [Status] Cost per Transaction: $[Value] [Trend] ROI: [Value]% [Period] ``` #### Detailed Metrics Report Include sections for: - Executive summary - Metric categories - Trend analysis - Alert summary - Recommendations - Action items ### Best Practices to Implement 1. **Metric Design** - Use SMART criteria - Make metrics actionable - Balance leading/lagging - Automate collection - Enable visualization 2. **Data Collection** - Minimize manual entry - Validate data quality - Handle missing data - Implement error handling - Monitor performance 3. **Calculation Accuracy** - Document formulas - Test edge cases - Handle nulls/zeros - Version calculations - Validate results 4. **Stakeholder Management** - Define ownership - Regular reviews - Clear communication - Training programs - Feedback loops ### Advanced Features to Consider 1. **Machine Learning Integration** - Anomaly detection - Trend prediction - Pattern recognition - Auto-categorization - Smart alerts 2. **Predictive Analytics** - Forecast modeling - What-if analysis - Risk prediction - Capacity planning - Performance forecasting 3. **Integration Capabilities** - External data sources - BI tool connectivity - API endpoints - Real-time streaming - Data export ### Error Handling Instructions Handle these scenarios: 1. Data source unavailable 2. Calculation errors 3. Storage limits 4. Performance issues 5. Permission errors Recovery strategies: - Retry mechanisms - Fallback values - Error logging - Manual override - Alert notifications ### Testing Requirements Generate test classes for: 1. Metric calculations 2. Collection processes 3. Alert rules 4. Dashboard components 5. Integration points ### Success Metrics Track and measure: - Collection reliability - Calculation accuracy - Dashboard performance - Alert effectiveness - User adoption - Business impact