One overlooked strength of single-agent systems is their predictability and easier debugging during real-world deployment. When something goes wrong, tracing issues in a single-agent system—where logic, state, and decision flow are centralized—is far simpler than in multi-agent setups where agents communicate, make autonomous decisions, and depend on shared context. That clarity becomes a huge asset when you're under pressure in production, trying to diagnose odd behavior or quickly ship a fix. It also reduces the chances of emergent behavior from poorly coordinated agents, which can be hard to reproduce or simulate.
A single-agent architecture allows better preservation of temporal coherence in path planning or task scheduling. The agent remembers past actions and future goals holistically, avoiding time inconsistencies that often arise from multiple agents operating asynchronously or out of sync during deployment. I think this is often overlooked when evaluating the performance of multi-agent systems, but it becomes a critical factor in real-world scenarios where timing and coordination are crucial. You see, single-agent systems have the advantage of being easier to debug and maintain. As there is only one central decision-maker, tracing back errors or issues becomes more straightforward compared to identifying problems within a network of agents with complex interactions. This can save valuable time and resources during deployment, especially when quick fixes are needed.