Observability & Evaluation · Software component

Agent Test Runner

Software componentObservability & EvaluationObservability & Evaluationarc:AgentTestRunner

A CI component that executes layered automated test suites (isolated unit tests, workflow integration tests, performance benchmarks) against agent code with coverage reporting and per-test timeouts.

Responsibility. Runs automated functional and performance tests of agent code.

Also known as: Automated testing stage, Test matrix

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

Keyword Match Scorer Ch4.2

is invoked by dependency

Continuous Integration Runner Ch4.2

reads dependency

evaluates assurance

Agent Controller abstract Ch4.2

Design guidance

SHOULD use fuzzy key-phrase checks or LLM-as-judge rather than exact string matching for non-deterministic agent outputs.
SHOULD focus integration tests on the ~20% of workflows handling ~80% of traffic plus known failure modes.
SHOULD enforce a per-test timeout to fail hung tests.
SHOULD run unit, integration, performance-benchmark and quality-evaluation test levels before every update (Ref8.08).

Quantitative guidance

As stated by the sources; verify before use.

Unit suite 30-90 s; version matrix cuts 4 minutes to 90 s (2.7x); suite matrix cuts 12 to 5 minutes (Ch4.2).
LLM-as-judge can stretch integration suites from 3 to 15+ minutes; focused suites stay under 5 minutes; per-test timeout 300 s (Ch4.2).

Classification

Patterns: Test pyramid (unit, integration, performance)Critical-path (80/20) test selection
Technologies: pytestpytest-covpytest-timeoutCodecov
Quality attributes: Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Regression bugsHung pipelines from infinite loops or network timeouts

Sources

Ch4.2: T. Nguyen, "Deployment and Scaling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.2. 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Ref8.08: "Model Updates and Maintenance Procedures," unpublished reference note (08-Model-Updates-Maintenance-Procedures.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note