AI agents only accelerate when the platform layer can keep up
The bottleneck lies in the handover to operations
An agent is not merely a new interface for a team. As soon as it uses or connects data across existing applications, it creates a platform question: who understands the data route, who assesses exceptions, and who intervenes? The European Commission states in its Collibra case study that AI agents often sit on top of existing applications or integrate data between two applications, increasing the risk of data leakage to unauthorized applications. This does not mean that every agent is unsafe; it is a concrete reason to assess the connections and access rights of each workflow separately. The conclusion is straightforward: scale an agent only after the team can identify who manages the data flow, the access decision and operational follow-up.
Oversight and evaluation are not afterthoughts
For high-risk AI systems, the European Commission mentions, among other things, human oversight and monitoring by deployers, post-market monitoring by providers, and the reporting of serious incidents and malfunctions. That passage applies specifically to high-risk systems, not to all agent applications. Still, it provides a useful design question for any workflow with a noticeable process impact: which action is monitored, when does the workflow stop, and who takes over? Stanford also states that independent evaluation is essential to prevent AI companies from assessing only their own work, while comprehensive protections for third-party evaluation are lacking. This does not support a general judgment about the quality of a specific agent, but it does support the choice not to leave evaluation solely to its builder.
Make one workflow testable before expanding
Use a decision register for one clearly defined agent workflow: describe the task, the two or more systems involved, the permitted data, the owner of the outcome, the checkpoint before an action, and the escalation path for deviations. Then add one evaluation case in which the agent chooses an incorrect or unauthorized route; record who assesses that outcome and what decision follows. This makes the production decision concrete without pretending that a register eliminates risk. Practical tool: “For each agent workflow, document the task, data route, decision owner, human review, evaluation case and escalation path before the workflow is rolled out to the next group of users.” The source passages address regulation, an organizational example and conditions for independent evaluation; they do not measure the speed, cost or reliability of your own implementation. A local pilot with your own log data and assessed exceptions therefore remains necessary.
