Title: Quiz 15 — Agent Deployment and Guardrails Introduction
Course: AIINFRA 300: Agentic AI & the Model Context Protocol (MCP) > Section: Week 15 — Agent Deployment and Guardrails Introduction

Q: Which of the following best describes the recommended order for rolling out guardrail rails as an agent's capabilities grow?
- Tool-call gating first, then everything else
- Retrieval rails first, since RAG is the riskiest component
- Input validation and output filtering first, then retrieval rails and tool-call gating as capability grows
- All four rail types must be implemented simultaneously or none provide value
A: Input validation and output filtering first, then retrieval rails and tool-call gating as capability grows

Q: A guardrail is best understood as a single instruction placed in the system prompt telling the model to behave safely.
- True
- False
A: False

Q: What purpose-built language does NVIDIA NeMo Guardrails use to define dialogue flows?
- YAML Rules Language
- Colang
- GraphQL
- Rego
A: Colang

Q: Name one 2025 addition to NeMo Guardrails that improves performance or integration (e.g., with LangChain or an OpenAI-compatible server).
Feedback: Sample/expected answer: Any of: the optimized parallel IORails engine, ****`check_async`**** in ****`LLMRails`****, the OpenAI-compatible guardrails server, or ****`GuardrailsMiddleware`**** for direct LangChain integration.

Q: What mechanism does the OpenAI Guardrails library use to immediately halt an agent's turn when a check fails?
- A retry loop
- A tripwire
- A circuit breaker only at the network layer
- A manual human-in-the-loop pause on every turn
A: A tripwire

Q: Llama Guard classifies conversations directly as an open-weight safety classifier and can be self-hosted or run in the cloud.
- True
- False
A: True

Q: Amazon Bedrock AgentCore Runtime (GA October 2025) is notable because it lets you:
- Only deploy agents written with AWS's proprietary framework
- Scaffold and test an agent locally with the AgentCore CLI, then deploy the same LangGraph, Google ADK, or OpenAI Agents SDK code to AWS with VPC/PrivateLink support
- Automatically rewrite any agent framework's code into a single unsupported format
- Guarantee 100% per-step accuracy for any deployed agent
A: Scaffold and test an agent locally with the AgentCore CLI, then deploy the same LangGraph, Google ADK, or OpenAI Agents SDK code to AWS with VPC/PrivateLink support

Q: If an agent is 95% accurate at each step, roughly what is its probability of completing a 10-step plan successfully end to end, and why doesn't high per-step accuracy guarantee this?
Feedback: Sample/expected answer: Approximately 0.95\^10 ≈ 60% (just under 60%). Per-step error compounds multiplicatively across the trajectory, so even a high per-step accuracy yields a much lower end-to-end success rate — reliability must be measured and guardrailed across the full trajectory, not just at each individual step.