The first post about Model Armor, explored the fundamentals of Google Cloud’s managed security service for Generative AI applications, which provides a model-agnostic defense layer to sanitize both prompts and model responses.
It covered the two primary patterns for integrating Model Armor into your stack:
And it walked through the practical configuration of these integrations using gcloud CLI and Terraform, establishing a secure baseline for your GenAI pipelines. In this post I shift my focus to examine how direct invocation works in practice. I will review the methods of interpreting sanitize API responses and incorporating the API calls in two agent frameworks: LangChain ‒ probably the most widespread framework today for implementing agentic workflows and the Agent Development Kit (ADK) which I personally prefer for its simplicity.