Agents Are Rewriting AI Infrastructure

On October 1, 2026, Geek Park published an article arguing that AI agents are rewriting the infrastructure they run on. In its framing, an agent is no longer a passive interface waiting to be called. It finds its own data, breaks tasks apart, tries several approaches in parallel, and keeps running — closer to a digital employee than an API endpoint.

The article contrasts how people and agents use a platform. An engineer writes data-processing code, hits submit, and often walks away for a coffee; ten or even several tens of minutes pass before they return to check the results, edit the code, and submit again. That wait-for-feedback gap has long been the default rhythm of data platforms. Agents leave no such gap. As soon as one result comes back, they validate it, immediately try a second and third approach concurrently, get fast feedback, and fire off the next round of calls. A person hands over a relatively well-defined task: compute it, return the result, done. An agent keeps changing tactics to reach the goal it was given. According to the article, the strain this creates — both governance pressure and hidden compute pressure — multiplies tenfold or a hundredfold.

The piece says this is becoming a new kind of workload that cloud computing has to face in 2026, and that what agents generate looks nothing like the loads cloud platforms have carried over the past decade or so. It also notes that Li Feifei, CTO of Alibaba Cloud Intelligence Group, broke agent deployment down into elements including Model and Context at the Yunqi Conference. The article's broader point is that AI is moving deeper into the phase of actually putting models to work.