The humans are required to state the problem in terms rigorous enough to be acted on by a machine, and be willing to wait for the answer while the machine applies the heuristics. This is in keeping with the Central Dream of AI, namely that humans can be relegated to a check-off function after the machines have done the thinking. As attractive as this dream might be, it is far from the realm of the possible. When you can have an agent that understands why 8 hours between trains in Paris is better than 4 hours between trains in Frankfurt but 8 hours in Peoria is worse than 4 hours in Fargo, then you can let it do all the work for you. Until then, the final step in the process is going to take place in your neural network, not your agent's.
Agents make the market for information less efficient rather than more.
In the agent's world, its particular owner is at the center and there are a huge number of heterogeneous data sources scattered all around, and one agent makes thousands of queries outward to perform one task. This ignores the fact that the data is neither static nor insensitive to the agent's request. Net-crawling agents are completely unable to deal with markets for information like these because these kind of problems require the structured data to be at the center and for a huge number of heterogeneous queries to be made inward toward the centralized data. This enables information about supply and demand to be captured in one place, something no autonomous agent can do. So much of the history of the Internet, and particularly of the Web, has been about decentralization that the idea of distributing processes has become almost reflexive.