Build An Agent Workshop

The Build An Agent Workshop is a comprehensive, hands-on learning experience that teaches you how to create, deploy, and evaluate AI agents using NVIDIA technology. Through six progressive modules, you'll build intelligent systems that can perform complex tasks, learn to implement Retrieval Augmented Generation (RAG), and master the art of evaluating, improving, and securing agent performance.

This workshop provides everything you need to become proficient in agentic AI development:

• Module 1 - Build an Agent: Create a Report Generation Agent that researches topics and writes comprehensive reports
• Module 2 - Agentic RAG: Build an IT Help Desk agent using RAG with NVIDIA NeMo Retriever
• Module 3 - Agent Evaluation: Learn to measure and improve agent quality using RAGAS metrics and LLM-as-a-judge techniques
• Module 4 - Agent Customization: Customize your agent beyond prompt engineering and tools with agent skills and reinforcement learning (RL).
• Module 5 - Deep Agents: Build deep agents that autonomously handle complex, multi-step tasks—and learn to run them safely and securely in production with sandboxing and isolation.
• Module 6 - Agent Safety: Secure autonomous agents with kernel-level enforcement (via OpenShell) and privacy routing using NVIDIA's NemoClaw stack.

At the end of this workshop, you will take home:

• Deep understanding of agent architecture and design patterns
• Six working agents demonstrating different capabilities
• Knowledge of NVIDIA NIM, NeMo models, and evaluation tools
• Comprehensive evaluation framework for production agents
• A turn-key, portable development environment
• Best practices for continuous agent improvement

The entire workshop can take anywhere from 12 to 18 hours to complete, depending on depth of exploration.

Workshop Modules

Module 1: Build an Agent (1-2 hours)

Learn the fundamentals of AI agents by building a Report Generation Agent from scratch.

What you'll build: An intelligent system that researches any topic, creates outlines, writes detailed sections, and compiles professional reports automatically.

Key concepts:

• The four core components of any AI agent (Model, Tools, Memory, Routing)
• ReAct architecture for tool-calling agents
• Building agents from scratch and with LangChain
• Using NVIDIA Nemotron models

Module 2: Agentic RAG (2-3 hours)

Evolve from basic RAG to intelligent agentic RAG systems.

What you'll build: An IT Help Desk agent that dynamically decides when and how to search knowledge bases to answer user queries.

Key concepts:

• Traditional RAG limitations and how agents solve them
• NVIDIA NeMo Retriever (embeddings and reranking)
• Vector databases with FAISS
• ReAct agents with retrieval tools

Module 3: Agent Evaluation (2-3 hours)

Master the art of measuring and improving agent performance.

What you'll learn: How to systematically evaluate agents using industry-standard metrics, LLM-as-a-judge techniques, and NVIDIA models.

Key concepts:

• RAGAS metrics for RAG evaluation (faithfulness, relevancy, context precision/recall)
• LLM-as-a-judge with NVIDIA models
• Building automated evaluation pipelines
• Continuous improvement strategies
• Production monitoring best practices

Module 4: Agent Customization (3-4 hours)

Specialize agents for specific domains using synthetic data and reinforcement learning.

What you'll build: A bash agent customized into a LangGraph CLI expert using NVIDIA NeMo Data Designer for synthetic data generation and GRPO (Group Relative Policy Optimization) for training.

Key concepts:

• When to use training vs. prompt engineering vs. tools
• Synthetic data generation with NeMo Data Designer
• Verifiable reward functions with NeMo Gym
• GRPO training for exploration-based learning
• Human-in-the-loop safety for command execution agents

Module 5: Deep Agents (1-2 hours)

Build autonomous agents that handle complex, multi-step tasks with planning and delegation.

What you'll build: A production-grade deep agent with explicit planning, hierarchical sub-agent delegation, persistent memory, and sandboxed execution using Docker.

Key concepts:

• The four pillars of deep agents (planning, delegation, memory, skills)
• Shallow vs. deep agent architectures
• Sandboxing and security for autonomous agents
• Using NVIDIA NIM models with the deepagents library
• Production isolation patterns (Docker, resource limits)

Module 6: Agent Safety (2-2.5 hours)

Secure autonomous agents with kernel-level enforcement, data routing, and continuous safety evaluation.

What you'll build: An OpenClaw personal assistant agent that executes inside and outside of an Openshell sandbox, complete with network and filesystem policies that demonstrate how the NVIDIA NemoClaw reference stack improves agent security.

Key concepts:

• Why application-level controls (M4) and container isolation (M5) are insufficient for always-on agents
• Setting up and running an OpenClaw autonomous agent
• Kernel-level enforcement with OpenShell (Landlock LSM, seccomp BPF, OPA proxy)
• Improved security for routing inference via a privacy router
• Red-team testing with adversarial probes
• Safety evaluation using LLM-as-judge (extending M3's evaluation framework)
• The NemoClaw reference architecture (OpenClaw + OpenShell + Nemotron + Privacy Router)

Learning Objectives

By the end of this workshop, you'll know how to:

• Build agents that use tools, maintain context, and make intelligent decisions
• Implement RAG systems that dynamically retrieve and use information
• Evaluate agent quality using quantitative metrics and qualitative assessment
• Use NVIDIA technology including NIM, Nemotron models, and NeMo Retriever
• Customize agents through synthetic data generation and reinforcement learning
• Build deep agents with planning, delegation, and sandboxed execution
• Secure agents with kernel-level enforcement, data classification, and red-team evaluation
• Deploy and monitor agents in production environments
• Continuously improve agent performance through systematic evaluation