Agent Testing

Agent testing refers to the systematic evaluation and validation of AI agents to ensure they perform intended tasks correctly, safely, and reliably across diverse scenarios and edge cases. This is critical for enterprise AI governance because agents increasingly operate autonomously with real-world consequences, making it essential to verify their behavior before deployment and monitor their performance in production. Robust agent testing helps organizations mitigate risks related to hallucinations, prompt injection attacks, unintended actions, and regulatory compliance violations while maintaining stakeholder trust in AI-driven automation.

1 item

StandardsGlobal2026-07-05

Agentic AI Governance Demands Dedicated Controls, Mayer Brown Guidance Finds: Least Privilege and Human Checkpoints Are the Core Requirements

Mayer Brown published practitioner guidance titled 'Governance of Agentic Artificial Intelligence Systems' on February 5, 2026, outlining how enterprises should adapt existing AI governance programs to address the distinct risks posed by autonomous agent systems. The guidance recommends pre-deployment testing across task execution, policy compliance, and tool usage robustness, alongside post-deployment behavioral monitoring. It emphasizes least-privilege technical controls and structured human oversight checkpoints as the foundational safeguards for agentic AI.

agentic AI least privilege human oversight agent testing agent drift technical controls Mayer Brown AI governance