WHY MOCKINGBYRD

Where it came from

We are as excited about artificial intelligence as we are aware of its risks, and we see no contradiction between the two. We adopted new AI tools early and rely on them heavily in our professional and personal lives. As those tools delivered more, we wanted more transparency, control and governance over what they could access and do. Mockingbyrd grew out of that need.

From answering to acting

AI tools no longer just make suggestions. They run commands, modify files, call external services through connectors and schedule their own tasks. That independence is what makes them valuable, and it is also what makes them a risk. Few people can say with confidence what their agents can reach today, or what has changed since last week.

Why now

Agents have moved into everyday work faster than the controls around them. In McKinsey's most recent research, 80% of organizations report risky behaviour from AI agents, including improper data exposure and unauthorized system access. McKinsey's conclusion is that guardrails must be built into the system as controls agents cannot bypass, not left to individual discretion.

Why it matters

On its own, a permission list is only a statement of intent. A wildcard grant, a general-purpose interpreter or an unreviewed configuration change can all get around it. Real control needs three things:

• Visibility: an accurate record of what each agent can do
• Enforcement: boundaries that cannot be bypassed
• Assurance: prompt notice when either one changes

Agents also consume resources out of view. Mockingbyrd rebuilds their cost from the logs already on your machine and shows where the spending is inefficient.

Our approach

Mockingbyrd isn't a checklist, and it never makes changes you haven't seen and approved. It measures first, presents every fix as a script for your review, then enforces controls below the configuration layer and continuously monitors for drift.