{"resourceId":"nvidia-aiq-langgraph-production-scaling-nemo-toolkit-20250827","versions":[{"version":"external-7c313db30ef4d7c733c30cd6f948ff8afb8c4b82bb4e4172a242b3fd9a0777fb","resource":{"id":"nvidia-aiq-langgraph-production-scaling-nemo-toolkit-20250827","title":"NVIDIA production note: AI-Q/LangGraph agent scaling needed NIM CPU fix and phased rollout","organization":"NVIDIA","sector":"AI platform vendor engineering blog","geography":"Global (internal OpenShift AI factory deployment)","publishedAt":"August 27, 2025","publicationDate":"2025-08-27","eventDate":null,"sourceName":"NVIDIA Technical Blog","sourceLabel":"Attributed vendor implementation narrative","sourceUrl":"https://developer.nvidia.com/blog/how-to-scale-your-langgraph-agents-in-production-from-a-single-user-to-1000-coworkers/","evidenceClass":"vendor-claim","outcomeClass":"mixed","topics":["developers-agents","infrastructure","operating-model"],"finding":"NVIDIA engineers describe scaling an internal LangGraph deep-research agent (AI-Q blueprint) on OpenShift with local NIM: single-user profiling found Llama Nemotron Super 49B calls as the bottleneck; load tests at 10–50 concurrency informed hardware forecasts; a Helm misconfiguration starved a NIM of CPU at 100%; LLM timeouts required retries; phased rollout used NeMo Agent Toolkit OTEL + Datadog.","sledRelevance":"Provides a transferable operating pattern for SLED agent platforms—profile, load-test, fix config, phase rollout—while remaining an internal vendor deployment without public SLOs.","evidence":"Opened blog (dated Aug 27, 2025) details three-step process with NeMo Agent Toolkit profiler/sizing calculator, explicit CPU starvation finding, timeout handling snippet, and OTEL/Datadog monitoring during phased user expansion.","architectureImplications":"Size the bottleneck NIM first; validate CPU/GPU allocations in Helm; add timeout/retry policy around reasoning LLMs; require tracing before wide release.","governanceImplications":"Phased access and quality eval gates should be formal change-management steps for campus/agency agents.","securityPrivacyImplications":"On-prem blueprint motivated by confidential information risk of public deep-research tools—relevant to SLED data-handling policies.","caveats":"Internal NVIDIA deployment; example sizing charts noted as toolkit sample not the actual agent data; dated Aug 2025 but newly relevant versus prior nvidia editions that did not cover this agent-scaling path.","streamIds":["nvidia"],"roles":{"sales":"Sell agent platforms with an explicit scale-up methodology (profile to load test to phase), not seat-count guesses.","engineering":"Instrument NeMo Agent Toolkit (or equivalent) profiling; fix resource requests; design retries and tracing.","delivery":"Run concurrency soaks and phased cohort rollouts with observability acceptance gates."},"roleDetails":{"sales":{"customerProblem":"Sponsors fear agent demos that collapse under departmental concurrent use.","stakeholders":["CIO","app owner","platform SRE"],"discoveryQuestions":["What concurrency is year-one?","Which model is the bottleneck?","What observability stack exists?"],"valueHypothesis":"A measured scale-up plan reduces failed enterprise agent launches.","potentialEngagement":"Sizing PoC with profiler and load calculator on representative prompts.","unsupportedClaims":["One GPU per N users is universal","Internal NVIDIA SLOs equal customer SLOs"]},"engineering":{"fit":"Fit for LangGraph/NeMo Agent Toolkit agents backed by NIM on Kubernetes/OpenShift.","architectureIntegration":"Profile single-user; scale bottleneck NIM; wire OTEL; correct Helm CPU/GPU requests.","prerequisites":["Agent toolkit instrumentation","Load generator","Metrics/tracing backend","Retry policy"],"constraints":["Hidden CPU limits","LLM timeouts","Non-deterministic agent paths"],"security":"Keep confidential corpora on-prem; control tool egress.","proofOfValue":["Bottleneck identified","CPU starvation fixed","p95 within target at test concurrency"]},"delivery":{"work":"PoC profiling, soak tests, phased cohorts, and monitoring handoff.","dependencies":["platform","app","observability"],"ownership":"App owns agent logic; platform owns NIM capacity.","skillsAdoption":"Teams need profiler and trace-reading skills.","governanceCheckpoints":["Single-user baseline","Concurrency gate","Cohort expansion review"],"acceptanceCriteria":["No CPU starvation under soak","Timeouts handled","Tracing live in prod"],"risks":["Big-bang rollout","Ignoring quality under optimization","Undersized bottleneck model"]}},"retrievedAt":"2026-09-15T14:15:00Z","enrichedAt":"2026-09-15T14:17:00Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Reliable concurrency determines whether staff/faculty agents are usable beyond early adopters.","procurementImplications":"Require load-test evidence and observability integration in agent platform contracts.","operatingModelImplications":"Agent launch is an SRE practice: cohorts, traces, and capacity forecasts.","sourceVerification":{"openedUrl":"https://developer.nvidia.com/blog/how-to-scale-your-langgraph-agents-in-production-from-a-single-user-to-1000-coworkers/","referenceExcerpt":"one of the NVIDIA NIM microservices was using 100% of its allocated CPU.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}