worker-benchmarks

To benchmark worker system performance, you run comprehensive tests covering trigger detection, registry operations, and concurrency under realistic load. This identifies bottlenecks and guides optimal configuration by measuring latency, throughput, and memory usage.

Evaluating agentic worker latency and throughput involves measuring responsiveness under varying load, parallelism, and data patterns. Standardized output formats provide reproducible benchmarks across configurable iterations to inform capacity planning.

Profiling worker pools to meet target p95 latency goals requires data-driven tuning of scheduling heuristics. By running concurrency tests under realistic load, you can evaluate responsiveness and adjust configurations for optimal production performance.

Yes, this benchmarking approach supports caching and registry operations. It applies to agentic-flow worker workloads, measuring performance across trigger detection, agent selection, caching, and concurrency tests to reveal bottlenecks.

You can run concurrency tests with configurable iterations to satisfy reproducible benchmarks. The process measures latency and throughput under varying load and data patterns, outputting results in standardized formats for analysis.

The best way to evaluate worker responsiveness under varying load scenarios is running a full benchmark suite. This measures latency, throughput, and memory usage across multiple scenarios to reveal performance bottlenecks and guide optimizations.