When AI can use your computer, how do you show who did the work?

OpenAI introduced its Computer Using Agent in January 2025, describing a model that interacts with buttons, menus and text fields through a graphical interface. In March 2026, Anthropic announced computer use in Claude Cowork and Claude Code: when an appropriate connector is unavailable, Claude can operate a screen, mouse and keyboard with permission.

These are documented capabilities, not a prediction that every agent can complete every editing task. For creative teams, their significance is straightforward: the presence of familiar software is no longer enough to explain how a project was produced.

Consider an illustrative film project with a producer, an editor and a sound designer. Everyone contributes to the same delivery. A list of names describes the team. A saved timeline shows the state of the edit. Neither, on its own, explains which creative actions came from each person and which were delegated to software.

The useful question is specific: what happened during the work associated with this version? Answering it calls for a record built during production, with contributors, permitted activity and any gaps kept in context. Reconstructing that history after a dispute is a much harder starting point.

Microsoft documents an approach to agents on Windows that includes distinct agent accounts, limited permissions and contained workspaces. Separating agent activity from a person’s activity is an explicit part of that security design. It is a useful direction for accountability.

Our conclusion for creative production is that account access, software security and creative authorship need separate evidence. An authorised agent may be allowed to act on a computer while still falling outside a project’s human creation rules. A secure device is valuable, but it does not settle that creative claim.

DigiBridge’s verification engine combines permitted device, system and creative tool observations with a dedicated AI verification agent. The agent examines patterns across the recorded activity to help identify prohibited AI assistance and software generated control. Its role is to support verification, not to create the work for you.

A warning needs reasons and supporting evidence. You can inspect the finding and add context for review. A recording gap or an uncertain model result is not, by itself, a confirmed incident. That distinction protects the standard and the people whose work it is meant to support.

If your release will make a human creation claim, agree the programme and tool policy with your contributors before recording begins. Connect each authorised session to the right project and keep the evidence associated with the version you intend to publish.

Ordinary playback, sequencing, keyframes and rendering must be assessed under the tool’s disclosed policy. An agent, script or macro acting in place of a contributor is a different matter. Clear distinctions give your team a practical way to work and give a reviewer something more useful than a blanket assurance.