Building and Deploying Advanced Agentic AI Solutions
Domain 4 - Building and deploying advanced agentic AI solutions. This domain tests the full lifecycle: evaluation-driven development, customization, production rollout, and monitoring of agents rather than single prompts.
Build and evaluate trajectories
Define an evaluation set of realistic tasks with expected tool sequences and outcomes before writing prompts. Score task success, tool-call accuracy, groundedness, and trajectory quality - not just the final answer text. Use NeMo Evaluator for automated runs, NeMo Curator for data preparation, and NeMo Customizer for LoRA or full fine-tuning when prompting plateaus. Distillation from a large reasoning model to a smaller Nemotron model is effective for routing, classification, and extraction, leaving the large model for hard reasoning. Retrieval pipelines combine embedding and reranking NIMs with a vector index such as cuVS.
Deploy with gates
Package agents and models as containers and NIM microservices, deploy with Helm or the NIM Operator, and version prompt, tool, and model configuration together so a rollback is a single action. Canary a small traffic slice, gate promotion on tool-call accuracy, groundedness, p95 latency, and cost per task, then expand. Manage secrets outside images and scope them per tool.
Worked example
A claims-triage agent. Offline evaluation over 200 labeled trajectories shows 78% tool-call accuracy; the failures cluster in policy lookup. The team fine-tunes a small model for intent and policy routing, reruns evaluation to 94%, then canaries 5% of traffic with automatic rollback on groundedness regression. Reasoning: the fix came from trajectory analysis, and the canary protects production while evidence accumulates.
Traps
Evaluating only final text; no regression suite when prompts change; deploying prompts and tools without versioning; autoscaling on CPU; skipping canary; embedding secrets in images; monitoring latency but not cost per task or groundedness.