Vasco Calais Pedro

One reason why we’re going to get uneven diffusion rates of agents is because different workflows in the enterprise are more or less aligned to continuous, uninterrupted computer work. A big reason why agentic coding growth has gone completely vertical is because it’s a type of work where economic value is directly correlated with the output of solely digital information, and where the task size can in theory be unbounded in a single session. This means that as AI agents can do larger amounts of work at a time, and as model capability improves on this task type, they can directly be deployed at larger workloads near instantaneously. Lots of work doesn’t natively have this property in how today’s workflows are designed. A sales rep needs a feedback loop with the customer before they can do additional work for a specific account, a lawyer needs to talk to the client, and a doctor needs to interact with a patient. So large agentic work will likely not look the same as it has in coding, at least by default. Instead, in many of these domains, the agents will actually need to tie into a workflow that is reengineered for AI. Matan Grinberg at Factory had a good way of thinking about this on the Training Data podcast: if everyone called in sick tomorrow, token usage would plummet because no one would be there to prompt them. This shows how early we are in agents running in the background for most processes; whereas this should be the majority of volume of agentic work in the future. In legal, it will be about processing every contract that comes in, in sales it will be agents that roam through customer records and figure out signals of when to better do outreach, in life sciences research it will be about swarms of agents reading through every test or all research. To do that these business processes have to be wired up to support agents, as opposed to the vast amounts of manual work that we do today. This is the big opportunity right now, but it’s going to diffuse differently from something like agentic coding. Lots of change management, lots of data cleanup, lots of workflow reengineering, and more.