The NVIDIA-Certified Professional: Agentic AI credential covers the part of AI engineering that is hardest to learn from a tutorial — what happens after the agent works on your laptop. This course is built around that gap: orchestration, tool use, retrieval, evaluation, and the operational discipline an agent needs before it can be trusted with real work.
Organised by NVIDIA's own objective domains, so what you study maps onto what you are assessed on.
How an agent decomposes a goal into steps, when to use a single agent against a multi-agent graph, and where to put the boundaries. Most agent failures are architectural rather than model failures, and they are visible in the design before they are visible in production.
Designing the tool surface an agent is given: schemas that constrain rather than merely describe, idempotent operations, error messages written to be read by a model, and the difference between a tool that fails loudly and one that quietly returns something plausible.
Embeddings, vector search, chunking that survives contact with real documents, and reranking. Covered alongside the evaluation work, because a retrieval change that looks better in isolation is the classic way to make an end-to-end system worse.
Serving models with NVIDIA's inference tooling, batching, quantisation trade-offs, GPU memory arithmetic, and reading a utilisation graph well enough to know whether you have a throughput problem or a latency problem.
Building an evaluation set before building the agent, offline scoring, tracing a multi-step run end to end, and the instrumentation you need to answer 'why did it do that' about a request from three days ago.
Input and output guardrails, prompt-injection handling for agents with real tool access, human-in-the-loop checkpoints, cost control, and the deployment patterns that let you roll an agent back the way you would roll back a service.
Where the hours actually go. Each of these is done, not watched.
Proctored, delivered online
NVIDIA's certification program is newer and moves faster than the Red Hat and Cisco tracks, so exam length, delivery, and objective weighting are worth confirming on NVIDIA's own certification page before you book. This course is built around the competencies the credential covers rather than a fixed question count.
Nrdyn offers NVIDIA-Certified Professional: Agentic AI (NCP-AAI) training: a hands-on program on building, deploying, and operating agentic AI systems on NVIDIA's stack, taught by engineers who deploy agents in production. It runs as roughly 32 hours of live instruction with self-paced labs, online or onsite.
No. The labs run on provisioned GPU environments, so you do not need local hardware. If you do have a GPU workstation you are welcome to use it, and the serving and profiling exercises will run there without modification.
Prompt engineering is one lesson here rather than the subject. The course is about the system around the model: tool design, retrieval, evaluation, serving, guardrails, tracing, and rollback. It assumes you can already get a useful answer out of a model and starts from the problem of getting a reliable one, repeatedly, without watching it.
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