Glossary
Load Balancing
Distributing incoming requests across multiple AI agent instances to optimize resource utilization, minimize latency, and prevent overload.
What is Load Balancing?
Load balancers direct traffic to healthy agent instances, distributing work to prevent any single instance from becoming a bottleneck. Strategies include round-robin rotation, least-connections routing to less-busy instances, or weighted distribution based on instance capacity. Load balancing improves both performance and reliability by preventing overload and providing redundancy.
Effective load balancing requires health checking to detect and route around failed instances, session affinity if agents maintain state, and dynamic adjustment as instance count scales. Load balancers may operate at network layer, application layer, or both. For global deployments, geographic load balancing routes users to the nearest region to reduce latency. Monitoring ensures balanced distribution without hot spots.
Example
An agent service runs 20 instances behind a load balancer. When one instance experiences high CPU and slow responses, the load balancer detects degraded performance through health checks and reduces traffic to that instance while distributing additional load across healthy instances, maintaining overall system responsiveness.
How Signet addresses this
Signet's Reliability dimension values load balancing as evidence of production-ready architecture. Agents deployed with effective load balancing demonstrate better availability and latency consistency, achieving higher reliability scores than single-instance deployments.
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