Observability & Evaluation · Software component
Load Test Runner
Software componentObservability & EvaluationObservability & Evaluationarc:LoadTestRunner
A benchmarking component that submits requests at increasing concurrency and measures latency percentiles, throughput and resource consumption against performance budgets.
Responsibility. Detects performance regressions and scaling bottlenecks before release.
Also known as: Performance benchmark suite, Soak test
Relationships
invokes dependency
- Agent Controller abstract Ref8.08
- Agent Service API abstract Ch8.1
is invoked by dependency
reads dependency
evaluates assurance
produces lifecycle
Design guidance
- SHOULD fail the pipeline when a new version exceeds its latency budget or drops throughput below threshold.
- MUST benchmark latency at production-representative concurrency plus a safety margin (e.g., 100-120 RPS for an 80 RPS peak) rather than extrapolating from light load.
- SHOULD locate the load at which latency degradation accelerates to inform capacity and right-sizing decisions.
- SHOULD begin performance optimization in design, load testing progressively before launch.
Quantitative guidance
As stated by the sources; verify before use.
- Worked example: new tool added 380 ms; average latency 2.1 s -> 2.5 s within a 3.0 s budget at ~50 req/s per instance (Ch4.1).
- P95 of 450 ms at 10 RPS became 3.2 s at 100 RPS in production (~700% worse) (Ch8.1).
- 800 ms P95 at 100 RPS vs 4.5 s at 120 RPS signals approaching capacity limits (Ch8.1).
- A system at 100 ms under 10 RPS may need 2,000 ms at 100 RPS due to connection-pool, queueing, memory and CPU contention (Ch8.1).
- Staging validation: 24-hour soak test and load test at 100 QPS (Ref8.08).
- Load testing ramps from 100 to 10,000 simultaneous users; 2-second responses in test can become 30 seconds at 10,000 concurrent users (Ch10.1).
Classification
- Patterns
- Latency budget gating
- Quality attributes
- Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Latency regressions reaching productionMisleading light-load benchmarksInfrastructure overprovisioning
Sources
- Ch4.1: T. Nguyen, "Introduction to AI Agent Deployment and Scaling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.1. ISBN: 9798244538229.
- Ch8.1: T. Nguyen, "Latency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.1. ISBN: 9798244538229.
- Ch10.1: T. Nguyen, "Conversational UI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.1. ISBN: 9798244538229.
- Ref8.08: "Model Updates and Maintenance Procedures," unpublished reference note (08-Model-Updates-Maintenance-Procedures.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note