The article argues that organizations should not automatically respond to growing workloads by adding more AI agents. Instead, the first question should be whether the tasks involved can genuinely be performed in parallel. Simply increasing the number of agents can create additional coordination, communication, and verification overhead, meaning that a system with many agents may sometimes be slower and more complicated than a smaller, well-designed workflow.
A key distinction is between independent work and dependent work. If several tasks can be completed without waiting for one another—for example, researching different sources or processing separate documents—multiple agents can work simultaneously and potentially reduce overall execution time. But when one task depends on the output of another, adding agents does not automatically create speed. The agents still have to wait, exchange information, resolve inconsistencies, and coordinate their actions.
The article therefore presents parallelism as an architectural question, rather than simply an AI-capability question. Developers should map a workflow's dependencies before deciding how many agents to deploy. Excessive agentization can introduce duplicated work, conflicting outputs, additional API calls, higher costs, and more difficult debugging. In some cases, a single agent with a clear sequence of tools may be more reliable than a collection of agents communicating with one another.
Ultimately, the message is that more agents do not necessarily mean more intelligence or better performance. The most effective agentic systems will use parallel execution where the work is genuinely independent and preserve sequential workflows where dependencies require it. Before adding another AI agent, teams should therefore ask a simpler engineering question: what work can actually happen at the same time, and what work fundamentally has to wait?
