worker-benchmarks

You can benchmark agentic-flow worker performance using a structured suite that measures latency, throughput, and resource usage across key operations like trigger detection and agent selection. It generates actionable metrics to guide optimization and capacity planning.

Benchmarking concurrent worker workloads provides per-operation metrics covering memory key generation, model cache efficiency, and agent selection latency. It enforces measurable latency and throughput targets to quantify resource usage during development and production scenarios.

Yes, you can use the benchmark suite for regression testing during development. It supports deterministic benchmarking workflows across triggers, registry operations, and concurrent workers to ensure measurable performance targets are maintained over time.

The best way to measure trigger detection latency is running predefined benchmark types that target agentic-flow worker operations. This enforces deterministic benchmarking workflows to capture per-operation metrics and ensure measurable latency targets are met.

No, benchmarking worker registry operations requires no external dependencies. The suite runs predefined benchmark types natively to quantify latency and throughput, generating actionable performance metrics without needing additional environment setup.

You should benchmark model cache performance separately because it provides specific per-operation metrics distinct from general worker throughput. Isolating model cache efficiency helps identify bottlenecks in agent selection and guides targeted optimization for production workloads.