Let us carry the arms race scenario a bit further. In a world in which sellers reset their prices at ever-increasing rates, a human price setter would undoubtedly be too inefficient, and would quickly be replaced by an automated pricing algorithm, perhaps a more sophisticated variant of one of the seller strategies proposed in Section 4. Quite possibly, this strategy would make use of information about the buyer population, which could be purchased from other agents. Even more likely, however, the strategy would require knowledge of competitors' prices. How would this information be obtained? From a shopbot, of course!
With each seller seeking to re-price its products faster than its
competitors, shopbots would quickly become overloaded with requests.
Imagine a scenario in which a large player like amazon.com were
to use the following simple, undercutting price-setting algorithm:
every 10 minutes, submit 2 million or so queries to a shopbot (one for
each title carried by amazon.com), then charge 1 cent less than
the minimum price for each title! 
Since the job of shopbots is to query individual sellers for prices,
it would in turn pass this load back to amazon.com's
competitors: barnesandnoble.com, kingbooks.com, etc. The
rate of pricing requests made by sellers could easily dwarf the rate
at which they would be made by human buyers, thereby eliminating the
potential of shopbots to ameliorate market frictions.
An obvious solution to an excess demand for shopbot services would be for shopbots to charge for the pricing information they provide. Today, shopbots tend to make a living by selling advertising space on their Web pages. This appears to be an adequate business model so long as requests are made by humans. Agents, however, are unwelcome customers because they are are not generally influenced by advertisements; as a result, agents are either barely tolerated or excluded intentionally. By charging for the information service that they provide, shopbots would be economically-motivated agents, creating the proper incentives to deter excess demand, and welcoming business from other agents.
Once shopbots begin to charge for pricing information, it would seem natural for sellers -- the actual owners of the desired information -- to themselves charge the shopbots for the use of their information. In turn, the pricing of this pricing information may itself involve the use of dynamic price-setting strategies by a related breed of automated seller agents. This scenario illustrates how the need for agents to dynamically price their services could quickly percolate through an entire economy of software agents. The alternative is a ``meltdown'' due to overload which could occur as agents become more prevalent on the Internet. Rules of etiquette followed voluntarily today by web crawlers and related programs [3] could be trampled in the rush for competitive advantage.