Like any other team of employees, your AI agents require continuous oversight and management. And as you scale your agentic workforce, risk rises.

The teams that implement AI agents most successfully don’t wait for their agents to drift. They build observability and validation into the operating model, both before deployment and in production.

In this white paper, Ian Jacobs, analyst at Opus Research, maps how teams are scaling AI agents across a business, and why governance is necessary to prevent inconsistency, broken customer context, and drift that leads to compliance risk.

In this white paper, you’ll learn: 

• The four ways agent sprawl shows up first: inconsistent answers, broken context, policy drift, and weak accountability.
• The five layers of the AI agent control plane, and what each one governs.
• Why scripted, pass/fail QA can’t validate agents that reason, call tools, and change behavior after every update.
• The four types of agentic AI testing brands need: pre-deployment, continuous monitoring, audit and observability, and cross-agent load testing.
• The 3 pillars of AI trust: testing, monitoring, and governance