Would you let an AI agent act on your behalf if you weren't sure what it actually knows? That's the question every IT and Security leader is quietly sitting with right now, and it's the question this session answers.
We'll open with something most vendors won't show you on stage: the same request, run live, once through a Rovo agent and once through a general-purpose AI assistant, both pulling from the exact same asset data. One of them wins, clearly and immediately, and the reason why is the whole point of this talk. It comes down to what's underneath the agent, not what's inside the prompt. Specifically, whether that data is complete, consistent, and current, the standard Lansweeper was built to deliver and most environments quietly fail to meet.
This isn't a feature demo. It's a working case for a bigger idea: human-AI collaboration doesn't fail because the model isn't smart enough. It fails at the moment a person has to decide whether to trust what the agent just told them, and that decision is only ever as good as the context behind the answer.
If your organization is betting on Rovo, or evaluating whether to, you'll leave this session with a sharper question to ask about every AI initiative on your roadmap: not "what can this agent do," but "is the data behind it complete, consistent, and current enough to trust?" That question is the difference between an agent your team supervises and one your team works alongside.

Key Takeaways

• An AI agent is only as good as the context behind it: data that's complete, consistent, and current enough to actually mean something.
• A live, side-by-side comparison shows what that context standard buys you in speed and reliability, not just in theory.
• Attendees leave with a sharper evaluation question for their own AI initiatives: does this agent have the context to be trusted with the answer?

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