How do I build an evaluation suite to test an AI agent's safety and guardrails?

To build an evaluation suite for an AI agent's safety and guardrails, establish a continuous, automated evaluation process integrated into CI/CD pipelines, founded on a golden dataset and multi-dimensional metrics. This suite should include automated red-teaming tools and production evaluation methods like shadow-mode and canary deployments.

The foundation of the evaluation suite is a golden dataset, a curated set of inputs covering security and safety surfaces with expected behaviors. This dataset should include known prompt injection variants, jailbreak attempts, edge cases from past bugs, representative legitimate inputs, and inputs that probe specific policy boundaries. Every change to prompts, models, tools, or policies should run against this dataset, with regressions blocking merges. This aligns with the NIST AI RMF function of Evaluation and Observability (MAESTRO L5).

A robust eval harness is necessary to measure various metrics such as task success rates, refusal rates, tool selection quality, cost per task, latency, and consistency. This multi-dimensional evaluation prevents shipping changes that might improve one metric but negatively impact others. This also relates to the NIST AI RMF function of Evaluation and Observability (MAESTRO L5).

Automated red-teaming tools (e.g., Garak, PyRIT) should be run on every release candidate to generate adversarial inputs at scale, with their output feeding back into the golden dataset. This helps address OWASP LLM Top 10 risks like LLM01: Prompt Injection and LLM02: Insecure Output Handling.

Production evaluation closes the loop by using shadow-mode evaluation (running new versions against production traffic without affecting users) and canary deployments (exposing a small fraction of traffic and watching for anomalies). Online metrics from these deployments should feed back into the evaluation suite, blurring the line between testing and production. This continuous evaluation is a critical aspect of the NIST AI RMF function of Evaluation and Observability (MAESTRO L5).

The evaluation suite should also integrate with existing IT infrastructure, including SIEM, secrets management, and compliance reporting. This ensures that the agent platform does not create security gaps by operating in isolation. This integration is part of the Environment aspect of the ORCHIDEAS framework.