Infrastructure · Software component
Continuous Integration Runner
Software componentInfrastructureInfrastructurearc:ContinuousIntegrationRunner
A CI/CD workflow engine that executes evaluation jobs automatically when agent code, prompts, tools or model configuration change.
Responsibility. Triggers and hosts automated evaluation runs on every relevant change.
Also known as: CI/CD pipeline, Continuous evaluation workflow, Continuous integration pipeline, CI/CD Pipeline, Agent CI/CD pipeline, Progressive validation pipeline, GPU-enabled CI/CD runner
Relationships
hosts structural
is configured by structural
invokes dependency
- Agent Test Runner Ch4.2
- Bias Evaluator Ref9.09
- Compliance Gate Ref9.09
- Container Image Builder Ch4.2
- Container Image Scanner Ch4.1 Ch4.2
- Deployment Notifier Ch4.2
- Evaluation Harness Ch4.2 Ch4.4 +1
- Evaluation Report Publisher Ch3.1B
- Experiment Tracker Ch4.4
- Load Test Runner Ch4.1
- Readiness Endpoint Ch4.1
- Regression Gate Ch3.1B Ch4.1 +2
- Smoke Tester Ch4.2
- Static Code Analyzer Ch4.1 Ch4.2
- Static Security Scanner Ch4.2 Ref9.09
- Tool Protocol Conformance Validator Ref1.01
reads dependency
writes dependency
emits telemetry to dynamic
is triggered by dynamic
triggers dynamic
orchestrates control
produces lifecycle
Design guidance
- MUST trigger evaluation automatically on every pull request or model change that affects agent behaviour.
- SHOULD always post results, even when the evaluation run errors, so developers see what broke.
- SHOULD run smoke-level evaluations on every change and comprehensive evaluations before major releases.
- MUST block deployment automatically on any failed quality, test, performance or evaluation stage.
- SHOULD run cheap validations (linting, unit tests) before expensive ones (integration tests, quality evaluation).
- MUST run each pipeline in a clean, isolated environment with locked dependencies, since subtle environment differences alter agent behaviour.
- SHOULD run cheap code-quality checks first and short-circuit on failure before expensive LLM-backed tests.
- SHOULD parallelise independent checks and test suites via a matrix strategy.
- SHOULD restrict artifact registration and deployment to main-branch commits.
- SHOULD include GPU-enabled runners able to build optimized engines in the deployment pipeline to eliminate build/runtime environment mismatches.
- MUST build separate engines for each target deployment platform in the CI/CD pipeline.
- SHOULD run security, fairness, privacy and documentation checks on every pull request and push and upload the resulting reports.
Quantitative guidance
As stated by the sources; verify before use.
- Worked example stage timings: code quality 45 s, unit 2 min, integration 8 min, performance 12 min, quality evaluation 25 min, containerization 3 min, registration 30 s, staging 2 min, canary 30 min (Ch4.1).
- Environment setup 30-60 s; pip cache cuts dependency install from 90 s to 10 s (Ch4.2).
- Full pipeline ~35-45 minutes including 30-minute canary soak, enabling 10+ deployments per day (Ch4.2).
- GitHub Enterprise can run 50+ parallel jobs, covering versions/OSs/suites in under 3 minutes (Ch4.2).
Classification
- Patterns
- Regression testingWebhook-triggered pipelineFast-checks-first staged validationContinuous deploymentContinuous delivery with human gateQuality gate funnelFail-fast short-circuitMatrix parallelisationDependency caching
- Technologies
- GitHub ActionsGitLab CIJenkinsCircleCIGitHub Enterprise
- Quality attributes
- Maintainability (ISO/IEC 25010)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Manual evaluation skipped under deadline pressurePerformance regressions reaching productionRegressions reaching productionEnvironment drift between runs
Sources
- Ch3.1A: T. Nguyen, "Implement Evaluation Pipelines and Task Benchmarks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.1A. ISBN: 9798244538229.
- Ch3.1B: T. Nguyen, "Implement Evaluation Pipelines and Task Benchmarks - Guided Practice," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.1B. ISBN: 9798244538229.
- Ch3.3: T. Nguyen, "Web Navigation and Interaction Benchmarks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.3. ISBN: 9798244538229.
- 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.
- 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.
- Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
- Ch4.5: T. Nguyen, "NVIDIA NIM and Triton Inference Server," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.5. ISBN: 9798244538229.
- Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
- Ch7.3: T. Nguyen, "NeMo Agent Toolkit Profiling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.3. ISBN: 9798244538229.
- Ref8.03: "Agent Evaluation Frameworks and Metrics," unpublished reference note (03-Agent-Evaluation-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref9.09: "Compliance Automation and Tools," unpublished reference note (09-Compliance-Automation-Tools.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note