Before today's enthusiasm for AI agents, we already had software that could operate computers. In traditional robotic process automation, platforms such as Automation Anywhere and UiPath could navigate interfaces, enter information, move files, and follow business workflows. I worked with this kind of automation, so the arrival of 'computer use' in AI feels both familiar and genuinely different.
The familiar part is the action layer. Software can click, type, open applications, and call services. Those capabilities did not begin with large language models. The newer part is that an AI system can interpret an open-ended request, choose tools, handle unstructured inputs, and decide what step to try next without every branch being explicitly scripted.
This flexibility matters. A traditional RPA workflow might extract fields from a known form and enter them into a database. An AI agent might be asked to research a company, identify relevant documents, resolve naming differences, and draft an evidence-backed conclusion. It can sometimes recover when a page changes or when a source is unfamiliar. That expands the set of tasks we can automate.
But it also changes the failure modes. A scripted process can fail because a rule is wrong or an interface changes. An agent can fail for those reasons and also because it misunderstands the goal, trusts a weak source, chooses an unsuitable tool, or invents a connection between pieces of evidence. Greater flexibility introduces more places where judgment must be evaluated.
MCP servers make tools and data accessible through a common interface, but connectivity is not a guarantee of correctness. A tool call may succeed while the resulting interpretation is wrong. Similarly, a model can retrieve current information without permanently learning it: retrieval supplies context for the current task, not necessarily an update to the model's underlying weights.
I think teams should distinguish three layers: execution, interpretation, and accountability. Execution asks whether the system performed the action correctly. Interpretation asks whether it understood the evidence and objective. Accountability asks who is authorized to approve consequential results and who can reconstruct what happened.
A practical design uses limited permissions, auditable tool calls, source provenance, tests where possible, clear escalation rules, and human approval for high-risk actions. Not every click needs a person. But the system should make it easy to see what it did, why, and with what evidence.
My position is not that AI agents are just old RPA with a new label. They are a meaningful evolution. The mistake would be to celebrate greater autonomy while ignoring the greater need for observability and reliable controls. We should automate work deliberately, not merely automate uncertainty.