Infrastructure · Software component

Rollout Manager

Software componentInfrastructureInfrastructureVariation point (abstract)arc:ModelRolloutManager

An abstract release component that replaces a running agent or model version with a new one according to a rollout strategy while bounding user impact and enabling rollback.

Responsibility. Performs zero-downtime model version updates by progressive traffic shifting.

Also known as: Model version management, Progressive rollout controller, A/B rollout controller, Online A/B testing of fine-tuned vs baseline models, Staged rollout, Progressive deployment controller, Agent release controller, Deployment strategy, Canary deployment controller, Canary release, Progressive delivery, Canary rollout controller, Progressive delivery controller, KServe canary deployment, Rolling update without downtime, Gradual model rollout, Deployment Strategy Controller, Release Rollout Controller, Staged rollout of improvements

When to choose. Choose when agent changes (prompts, tools, models, code) may pass automated tests yet degrade behaviour in production, requiring fine-grained blast-radius control and automated rollback.

Direct neighbourhood (hover for relationship types)

Variants

Variant	When to choose
Blue-Green Deployment Switcher	Choose when validation completes before cutover and instant switch-over with instant failback to a fully running previous version is required.
Canary Rollout Controller	Choose when a new version should be validated on real user traffic with limited exposure before full rollout.
Rolling Update Controller	Choose gradual one-at-a-time replacement when safety matters more than update speed; replacing all instances at once is faster but risky.

Relationships

deployed on structural

Container Orchestrator Ch4.1 Ch4.4

is configured by structural

invokes dependency

is invoked by dependency

reads dependency

writes dependency

is triggered by dynamic

routes to dynamic

sends data to dynamic

Change Impact Attributor Ch10.4

is constrained by control

is orchestrated by control

Training Pipeline Orchestrator Ch10.2

is overridden by control

requires approval from control

Regression Gate Ch3.1A Ch4.1 +1
Release Approver Ch4.1 Ch4.2 +2

is monitored by assurance

monitors assurance

Readiness Endpoint Ch4.4

Design guidance

SHOULD shift traffic to a new model version only after validation.
SHOULD start treatment allocation small (about 10%) and ramp (25%, 50%, 75%, 100%) only after metrics show significant improvement without regressions.
MUST return all traffic to the baseline automatically when treatment metrics degrade beyond rollback thresholds.
SHOULD keep monitoring after full rollout before promoting the new version to the established baseline.
SHOULD monitor efficiency and hallucination metrics through staged rollouts, comparing test versus production.
SHOULD retain a second-scale rollback path to the previous version for every production deployment.
SHOULD size each stage's duration to accumulate enough traffic for statistical significance given the service's request rate.
SHOULD supplement operational metrics with continuous sampled quality evaluation, rolling back on quality drops even when success rate and latency look healthy.
SHOULD continue monitoring for about 24 hours after reaching 100% to detect longer-term behavioural drift.
SHOULD favour frequent small releases with fast automated rollback over infrequent batched releases.
SHOULD watch first-hour post-deployment metrics against baseline and keep rollback easy (Ref8.03).

Quantitative guidance

As stated by the sources; verify before use.

Example lifecycle: 10% day 1, 25% days 2-4, 50% day 5, 100% day 7; 35,000 treatment queries at 87.8% vs 86.2% TSR (Ch3.1A).
New version graduates to baseline after ~2 weeks of stable post-rollout metrics (Ch3.1A).
Stages 5/25/50/100% with 30-minute holds; high-traffic services (10,000+ rpm) may advance every 15 minutes, low-traffic ones need 1-2 hour soaks (Ch4.2).
Serverless alias example: 95%/5%, then 20%, 50%, 100% after 30 healthy minutes (Ch4.2).
Rollback executes within ~30 s by rerouting all traffic; off-hours regressions rolled back within 5 minutes of detection (Ch4.2).
Blast radius: under 100 conversations exposed vs 1,000+ for immediate rollout (~10x reduction) (Ch4.2 worked example).
Typical ramp 5% -> 25% -> 50% -> 75% -> 100% with 15-30 minute observation per stage (Ch4.4).
Worked example: 10% -> 50% -> 100%, 30 minutes per stage, analysis every 5 minutes, abort after 2 consecutive failures (Ch4.4).

Classification

Patterns: Progressive rolloutZero-downtime updateProgressive traffic rampAutomatic rollbackCanary-style A/B rolloutGradual traffic shifting after A/B confirmationCanaryBlue-greenRolling updateStaged canary (5% -> 25% -> 50% -> 100%)Alias-weighted serverless canaryStatistical-significance gatingCanary deploymentStaged traffic ramp with automated analysisWeighted gradual rollout of new agent versions
Technologies: NVIDIA AI EnterpriseArgo RolloutsIstio VirtualServiceKServeNVIDIA NIM Operator
Quality attributes: Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Maintainability (ISO/IEC 25010)Safety (ISO/IEC 25010 | NIST AI RMF: safe)
Risks mitigated: Downtime during model updatesLarge blast radius of a degraded agent versionProlonged user exposure to a broken treatmentCapacity loss during updatesLarge blast radius of a faulty releaseLarge incident blast radiusSlow rollback

Sources

Ch2.7: T. Nguyen, "Multimodal RAG Approaches," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.7. ISBN: 9798244538229.
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.
Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
Ch3.5: T. Nguyen, "Prompt Optimization, Few-Shot Learning, Fine-Tuning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.5. ISBN: 9798244538229.
Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. 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.
Ch4.3: T. Nguyen, "Container Orchestration and Edge Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.3. 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.
Ch4.7: T. Nguyen, "Scaling Strategies," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.7. 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.
Ch8.2A: T. Nguyen, "Error Rates and Reliability," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.2A. ISBN: 9798244538229.
Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.
Ch10.4: T. Nguyen, "Human-in-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.4. ISBN: 9798244538229.
Ref4.03: M. Zhang, J. Wyman, I. M. Bhosale, and W. Tan, "Scaling LLMs with NVIDIA Triton and NVIDIA TensorRT-LLM using Kubernetes," NVIDIA Technical Blog, Oct. 22, 2024. [Online]. Available: https://developer.nvidia.com/blog/scaling-llms-with-nvidia-triton-and-nvidia-tensorrt-llm-using-kubernetes/
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
Ref8.06: "Error Troubleshooting and Incident Response for Agent Systems," unpublished reference note (06-Error-Troubleshooting-Incident-Response.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
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
Ref10.05: "The Data Flywheel: Continuous Improvement Loop," unpublished reference note (05-Data-Flywheel-Continuous-Improvement.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note