Intelligent Pruning Technology
Our advanced pruning strategies combine static thresholds with reinforcement learning to optimize agent selection while maintaining service quality.
Static Pruning
Deterministic approaches with predictable performance and guaranteed quality.
Threshold-Based Pruning
Filters agents based on capability scores, keeping only those above a defined threshold. Ensures quality by maintaining a minimum capability level.
How It Works
Agents with capability scores below the threshold are pruned before evaluation, reducing the search space while ensuring quality.
Benefits
- • Predictable performance
- • Quality guarantees
- • Simple to configure
Ratio-Based Pruning
Randomly prunes a fixed percentage of agents to achieve desired load reduction. Useful for load balancing and resource management.
How It Works
A fixed ratio of agents are randomly selected for pruning, providing predictable resource usage regardless of agent distribution.
Benefits
- • Precise resource control
- • Load balancing
- • Simple implementation
Dynamic Pruning
Reinforcement learning-powered adaptive pruning that learns optimal policies.
Logistic Dynamic Policy
Our reinforcement learning model uses logistic regression to learn optimal pruning decisions based on agent capabilities, task requirements, and system context.
Features
- •Agent embedding vectors
- •Task embedding vectors
- •Context features (redundancy, workload)
- •Historical performance data
Training
- •REINFORCE algorithm
- •Moving average baseline
- •SGD optimization
- •Optional L2 regularization
Decision Process
Feature Extraction
Concatenate agent embeddings, task embeddings, and context features
Policy Evaluation
Logistic regression outputs probability of keeping the agent
Bernoulli Sampling
Sample decision based on probability (1=keep, 0=prune)
Policy Update
Update model parameters based on reward signal
Safety Constraints
Ensuring service quality through built-in safety mechanisms.
Quality Thresholds
Minimum quality thresholds ensure that pruning never compromises service standards
Coverage Guarantees
Maintain minimum agent coverage to ensure all tasks can be handled
Fallback Mechanisms
Automatic fallback to safer strategies when quality drops below thresholds
Strategy Comparison
Performance characteristics of different pruning approaches.
| Strategy | Latency | Quality | Complexity | Best For |
|---|---|---|---|---|
| Baseline (No Pruning) | High | 100% | Low | Small agent sets |
| Static Threshold | Medium | 95%+ | Low | Quality-critical apps |
| Static Ratio | Low | Variable | Low | Load balancing |
| Dynamic (Safe) | Low | 95%+ | High | Production systems |
| Dynamic (Unsafe) | Very Low | Variable | High | Maximum optimization |
Choose Your Strategy
Select the pruning approach that best fits your use case and performance requirements.