Sequential Execution Beats Parallel Chaos

Running many AI tasks simultaneously seems efficient. Fire off five agents, get five outputs, move faster. In practice it created problems.

Agents overwrote each other's outputs. They duplicated work. They generated conflicting changes to the same file. Race conditions that are subtle in human workflows become obvious when machines are fast and deterministic. Two coders editing the same module at once—you can guess what happened.

Switching to sequential feature execution dramatically improved stability. Each task completes before the next begins. No overwrites, no collisions, no merged conflicts that shouldn't exist. The system became slower in wall-clock time but far more predictable. Fewer retries, fewer rollbacks.

Sometimes slower systems are actually faster in the long run.