Start with the outcome
Customers rarely wake up wanting another tool. They want the thing on the other side of it. A useful account brief. A finished itinerary. A decision they can make with confidence. Software has traditionally helped people reach those outcomes. The customer still had to learn the interface, bring the context and complete the work.
Agents change that relationship. They can gather information, apply instructions, make a first judgement and move a workflow forward. When those capabilities are combined with the right human review, the company can take responsibility for more of the outcome. It can sell the work itself rather than access to a tool.
This is not only a change in packaging. It changes what the company must understand. A software company can stop at the feature. An AI service has to understand the full workflow, including the exceptions, the handoffs and the moments where judgement matters most.
The lesson from Twimbit X
I began seeing this while working on Twimbit X. Customer facing teams did not need more information. They already had reports, dashboards and internal material. Their real problem was turning all that information into something relevant to the account sitting in front of them.
The useful output was not a search result. It was intelligence shaped around a customer, a conversation and a commercial decision. That meant beginning with the work the user was trying to complete. The technology mattered, but only after the workflow and the context were clear.
It also showed me why domain knowledge becomes more important as AI becomes more capable. A general model can produce a plausible answer. A useful service needs to know what good looks like, what information can be trusted and when the answer requires another person to review it.
The service owns more of the responsibility
Selling the work creates a higher standard. The customer judges the result, not the elegance of the product. If the recommendation is wrong, the itinerary is impractical or the output arrives too late, the service has failed even if the technology performed as designed.
This is why I do not see human involvement as a temporary weakness. It is part of the architecture. People can review the output, handle unusual situations and remain accountable for the final experience. Over time the service can learn which steps are repeatable and which decisions still benefit from taste, empathy or commercial judgement.
Build close to reality
Curious East is my way of testing this idea in practice. Travel in China can involve fragmented information, unfamiliar platforms, language and many small execution details. The customer does not want a travel technology stack. They want to navigate the complexity and have a good trip.
The opportunity is to build around that outcome. Agents can support itinerary building and execution. People can review the plan, resolve exceptions and stay responsible for what reaches the customer. The boundary between agent and human should follow the work, not an abstract belief about automation.
I think the most interesting AI companies will be built this way. They will begin with a valuable outcome, understand the workflow in detail and use AI to take responsibility for more of the work. The product may still contain software. The difference is that software is no longer the final thing being sold.