Speed matters, but the real killer feature of AI agents isn't raw performance—it's autonomous execution. Think of it like a Roomba: you can vacuum faster manually, but the value is in task completion without human intervention.

This is the core architectural shift from traditional automation to agent systems. Instead of optimizing for latency or throughput alone, the design goal is unattended operation—fire-and-forget workflows that handle edge cases, adapt to context changes, and complete objectives without constant supervision.

It's why agent frameworks focus on:

- Self-correction loops (retry logic, error handling)

- State persistence (resume after failures)

- Goal-oriented planning (break down complex tasks)

The bottleneck isn't compute speed anymore—it's reliability in unsupervised mode. A 10x faster model that requires babysitting loses to a 2x slower agent that runs overnight without human checkpoints.

Roomba-grade reliability > Ferrari-grade speed for production AI systems.