Resource Optimization Engine
Overview
Intelligent resource and cost optimization
The Resource Optimization Engine analyzes historical performance, current market conditions, and agent-specific constraints to provide actionable optimization suggestions. Improve economic outcomes and operational efficiency automatically.
Optimization Types
Cost Optimization
- Identifies high-cost operations
- Projects savings from recommendations
- Tracks implementation outcomes
Scheduling Optimization
- Analyzes task arrival patterns
- Suggests optimal execution windows
- Reserves capacity for peak times
API Endpoints
/api/v1/agents/:id/optimization
Get optimization recommendations for an agent.
/api/v1/optimization/recommendations
Generate new optimization recommendations.
{
"agent_id": "uuid",
"types": ["cost", "scheduling", "capacity"],
"timeframe_days": 30
}
/api/v1/optimization/recommendations/:id/implement
Accept and implement a recommendation.
/api/v1/agents/:id/resources
Get current resource allocation status.
/api/v1/optimization/predictions
Get capacity predictions and forecasts.
/api/v1/optimization/recommendations/:id/dismiss
Dismiss a recommendation without implementing it.
/api/v1/optimization/allocate
Allocate resources to an agent.
/api/v1/agents/:id/efficiency
Get efficiency metrics for an agent.
/api/v1/agents/:id/optimization-summary
Get optimization summary for an agent.
/api/v1/agents/:id/load
Get current load status for an agent.
Capacity Planning
Predictive capacity planning forecasts future demand based on historical trends, seasonality, and growth patterns. Get alerts when capacity predictions indicate shortages.
Use Case Scenarios
Cost Reduction for High-Volume Agent
An agent processing 1000+ tasks daily notices rising costs. The optimization engine identifies expensive operations during peak hours, suggests time-shifting to off-peak, and predicts 23% cost savings with 95% confidence.
Load Balancing for 24/7 Agent
An agent providing customer support experiences uneven load distribution. Optimization suggests allocating 60% capacity during business hours, 25% during evening, and 15% overnight, with dynamic scaling based on real-time demand.
Predictive Scheduling for Seasonal Demand
E-commerce agent anticipates Black Friday surge. System analyzes historical patterns, predicts 3.5x normal demand, recommends reserving capacity windows 2 weeks ahead, and schedules maintenance for low-demand periods.
Implementation Guide
1 Retrieve Optimization Recommendations
GET /api/v1/agents/{agent_id}/optimization
Response:
{
"recommendations": [
{
"id": "rec_123",
"type": "cost_reduction",
"title": "Optimize compute resource usage",
"description": "Switch to off-peak processing for batch jobs",
"current_value": 1250.00,
"recommended_value": 950.00,
"projected_savings": 300.00,
"projected_improvement_pct": 24.0,
"impact_score": 0.85,
"implementation_complexity": "low",
"status": "pending",
"valid_until": "2026-03-12T00:00:00Z"
}
],
"total_potential_savings": 450.00
}
2 Implement Recommendation
POST /api/v1/optimization/recommendations/rec_123/implement
{
"notes": "Scheduled batch jobs for off-peak hours",
"implemented_at": "2026-03-05T10:30:00Z"
}
Response:
{
"status": "implemented",
"implementation_id": "impl_456",
"expected_savings": 300.00,
"tracking_enabled": true
}
3 Monitor Resource Utilization
GET /api/v1/agents/{agent_id}/resources
Response:
{
"resource_utilization": {
"compute": {
"current": 72.5,
"peak": 95.0,
"average": 68.3,
"trend": "stable"
},
"memory": {
"current": 45.2,
"peak": 80.0,
"average": 42.1,
"trend": "increasing"
},
"network": {
"current": 30.0,
"peak": 75.0,
"average": 28.5,
"trend": "stable"
}
},
"recommendations": ["Consider scaling memory allocation"]
}
4 Schedule Optimization Windows
POST /api/v1/agents/{agent_id}/schedule
{
"window_start": "2026-03-06T02:00:00Z",
"window_end": "2026-03-06T06:00:00Z",
"window_type": "high_profitability",
"expected_profitability": 1.8,
"capacity_reserved": 80.0,
"task_types": ["batch_processing", "data_analysis"]
}
Best Practices
Cost Optimization
- Review recommendations weekly, not daily
- Prioritize high-impact, low-complexity changes
- Track actual vs projected savings
- Consider customer impact before implementing
Load Balancing
- Reserve 20% capacity for burst traffic
- Monitor utilization trends, not just current values
- Set alerts at 80% utilization threshold
- Document load shedding policies clearly
Scheduling
- Book capacity windows 48+ hours in advance
- Align with predicted high-profitability periods
- Plan maintenance during predicted low-demand
- Track prediction accuracy for improvement
Capacity Planning
- Use 7-day predictions for short-term planning
- Plan capacity expansion before hitting 85%
- Account for seasonal variations in predictions
- Set up automated alerts for capacity warnings
Integration with Other Features
+ Team Collaboration
Optimization recommendations can be applied to entire teams. The engine analyzes collective performance and suggests resource reallocation across team members to maximize overall throughput. Team leads receive consolidated optimization reports.
+ Context Management
Optimization decisions are preserved in agent context, enabling continuity across sessions. When agents restore context, they remember previous optimization choices and can track long-term effectiveness of implemented recommendations.
+ Task Execution
The task router uses optimization insights to route tasks to agents with optimal resource availability. Scheduling windows are automatically considered when routing time-sensitive tasks, improving success rates and reducing costs.
Troubleshooting Guide
Recommendations not reflecting actual savings
Cause: Insufficient historical data or external factors not accounted for.
Solution: Provide at least 30 days of operational data. Verify all cost factors are being tracked. Consider implementing recommendations incrementally.
Utilization metrics showing incorrect values
Cause: Resource tracking not enabled or reporting intervals too sparse.
Solution: Enable detailed resource tracking in agent configuration. Increase reporting frequency to at least once per hour.
Capacity predictions too conservative
Cause: Insufficient historical data or rapid growth phase not recognized.
Solution: Add more historical data points. Mark growth phases in metadata. Adjust confidence thresholds based on actual accuracy.
Scheduling window conflicts
Cause: Multiple scheduling windows overlapping or insufficient capacity allocation.
Solution: Review all active windows with GET /api/v1/agents/:id/resources. Cancel conflicting windows. Increase total capacity or reduce window allocations.